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Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.

Cover of Handbook of eHealth Evaluation: An Evidence-based Approach

Handbook of eHealth Evaluation: An Evidence-based Approach [Internet].

Chapter 10 methods for comparative studies.

Francis Lau and Anne Holbrook .

10.1. Introduction

In eHealth evaluation, comparative studies aim to find out whether group differences in eHealth system adoption make a difference in important outcomes. These groups may differ in their composition, the type of system in use, and the setting where they work over a given time duration. The comparisons are to determine whether significant differences exist for some predefined measures between these groups, while controlling for as many of the conditions as possible such as the composition, system, setting and duration.

According to the typology by Friedman and Wyatt (2006) , comparative studies take on an objective view where events such as the use and effect of an eHealth system can be defined, measured and compared through a set of variables to prove or disprove a hypothesis. For comparative studies, the design options are experimental versus observational and prospective versus retro­­spective. The quality of eHealth comparative studies depends on such aspects of methodological design as the choice of variables, sample size, sources of bias, confounders, and adherence to quality and reporting guidelines.

In this chapter we focus on experimental studies as one type of comparative study and their methodological considerations that have been reported in the eHealth literature. Also included are three case examples to show how these studies are done.

10.2. Types of Comparative Studies

Experimental studies are one type of comparative study where a sample of participants is identified and assigned to different conditions for a given time duration, then compared for differences. An example is a hospital with two care units where one is assigned a cpoe system to process medication orders electronically while the other continues its usual practice without a cpoe . The participants in the unit assigned to the cpoe are called the intervention group and those assigned to usual practice are the control group. The comparison can be performance or outcome focused, such as the ratio of correct orders processed or the occurrence of adverse drug events in the two groups during the given time period. Experimental studies can take on a randomized or non-randomized design. These are described below.

10.2.1. Randomized Experiments

In a randomized design, the participants are randomly assigned to two or more groups using a known randomization technique such as a random number table. The design is prospective in nature since the groups are assigned concurrently, after which the intervention is applied then measured and compared. Three types of experimental designs seen in eHealth evaluation are described below ( Friedman & Wyatt, 2006 ; Zwarenstein & Treweek, 2009 ).

Randomized controlled trials ( rct s) – In rct s participants are randomly assigned to an intervention or a control group. The randomization can occur at the patient, provider or organization level, which is known as the unit of allocation. For instance, at the patient level one can randomly assign half of the patients to receive emr reminders while the other half do not. At the provider level, one can assign half of the providers to receive the reminders while the other half continues with their usual practice. At the organization level, such as a multisite hospital, one can randomly assign emr reminders to some of the sites but not others. Cluster randomized controlled trials ( crct s) – In crct s, clusters of participants are randomized rather than by individual participant since they are found in naturally occurring groups such as living in the same communities. For instance, clinics in one city may be randomized as a cluster to receive emr reminders while clinics in another city continue their usual practice. Pragmatic trials – Unlike rct s that seek to find out if an intervention such as a cpoe system works under ideal conditions, pragmatic trials are designed to find out if the intervention works under usual conditions. The goal is to make the design and findings relevant to and practical for decision-makers to apply in usual settings. As such, pragmatic trials have few criteria for selecting study participants, flexibility in implementing the intervention, usual practice as the comparator, the same compliance and follow-up intensity as usual practice, and outcomes that are relevant to decision-makers.

10.2.2. Non-randomized Experiments

Non-randomized design is used when it is neither feasible nor ethical to randomize participants into groups for comparison. It is sometimes referred to as a quasi-experimental design. The design can involve the use of prospective or retrospective data from the same or different participants as the control group. Three types of non-randomized designs are described below ( Harris et al., 2006 ).

Intervention group only with pretest and post-test design – This design involves only one group where a pretest or baseline measure is taken as the control period, the intervention is implemented, and a post-test measure is taken as the intervention period for comparison. For example, one can compare the rates of medication errors before and after the implementation of a cpoe system in a hospital. To increase study quality, one can add a second pretest period to decrease the probability that the pretest and post-test difference is due to chance, such as an unusually low medication error rate in the first pretest period. Other ways to increase study quality include adding an unrelated outcome such as patient case-mix that should not be affected, removing the intervention to see if the difference remains, and removing then re-implementing the intervention to see if the differences vary accordingly. Intervention and control groups with post-test design – This design involves two groups where the intervention is implemented in one group and compared with a second group without the intervention, based on a post-test measure from both groups. For example, one can implement a cpoe system in one care unit as the intervention group with a second unit as the control group and compare the post-test medication error rates in both units over six months. To increase study quality, one can add one or more pretest periods to both groups, or implement the intervention to the control group at a later time to measure for similar but delayed effects. Interrupted time series ( its ) design – In its design, multiple measures are taken from one group in equal time intervals, interrupted by the implementation of the intervention. The multiple pretest and post-test measures decrease the probability that the differences detected are due to chance or unrelated effects. An example is to take six consecutive monthly medication error rates as the pretest measures, implement the cpoe system, then take another six consecutive monthly medication error rates as the post-test measures for comparison in error rate differences over 12 months. To increase study quality, one may add a concurrent control group for comparison to be more convinced that the intervention produced the change.

10.3. Methodological Considerations

The quality of comparative studies is dependent on their internal and external validity. Internal validity refers to the extent to which conclusions can be drawn correctly from the study setting, participants, intervention, measures, analysis and interpretations. External validity refers to the extent to which the conclusions can be generalized to other settings. The major factors that influence validity are described below.

10.3.1. Choice of Variables

Variables are specific measurable features that can influence validity. In comparative studies, the choice of dependent and independent variables and whether they are categorical and/or continuous in values can affect the type of questions, study design and analysis to be considered. These are described below ( Friedman & Wyatt, 2006 ).

Dependent variables – This refers to outcomes of interest; they are also known as outcome variables. An example is the rate of medication errors as an outcome in determining whether cpoe can improve patient safety. Independent variables – This refers to variables that can explain the measured values of the dependent variables. For instance, the characteristics of the setting, participants and intervention can influence the effects of cpoe . Categorical variables – This refers to variables with measured values in discrete categories or levels. Examples are the type of providers (e.g., nurses, physicians and pharmacists), the presence or absence of a disease, and pain scale (e.g., 0 to 10 in increments of 1). Categorical variables are analyzed using non-parametric methods such as chi-square and odds ratio. Continuous variables – This refers to variables that can take on infinite values within an interval limited only by the desired precision. Examples are blood pressure, heart rate and body temperature. Continuous variables are analyzed using parametric methods such as t -test, analysis of variance or multiple regression.

10.3.2. Sample Size

Sample size is the number of participants to include in a study. It can refer to patients, providers or organizations depending on how the unit of allocation is defined. There are four parts to calculating sample size. They are described below ( Noordzij et al., 2010 ).

Significance level – This refers to the probability that a positive finding is due to chance alone. It is usually set at 0.05, which means having a less than 5% chance of drawing a false positive conclusion. Power – This refers to the ability to detect the true effect based on a sample from the population. It is usually set at 0.8, which means having at least an 80% chance of drawing a correct conclusion. Effect size – This refers to the minimal clinically relevant difference that can be detected between comparison groups. For continuous variables, the effect is a numerical value such as a 10-kilogram weight difference between two groups. For categorical variables, it is a percentage such as a 10% difference in medication error rates. Variability – This refers to the population variance of the outcome of interest, which is often unknown and is estimated by way of standard deviation ( sd ) from pilot or previous studies for continuous outcome.

Table 10.1. Sample Size Equations for Comparing Two Groups with Continuous and Categorical Outcome Variables.

Sample Size Equations for Comparing Two Groups with Continuous and Categorical Outcome Variables.

An example of sample size calculation for an rct to examine the effect of cds on improving systolic blood pressure of hypertensive patients is provided in the Appendix. Refer to the Biomath website from Columbia University (n.d.) for a simple Web-based sample size / power calculator.

10.3.3. Sources of Bias

There are five common sources of biases in comparative studies. They are selection, performance, detection, attrition and reporting biases ( Higgins & Green, 2011 ). These biases, and the ways to minimize them, are described below ( Vervloet et al., 2012 ).

Selection or allocation bias – This refers to differences between the composition of comparison groups in terms of the response to the intervention. An example is having sicker or older patients in the control group than those in the intervention group when evaluating the effect of emr reminders. To reduce selection bias, one can apply randomization and concealment when assigning participants to groups and ensure their compositions are comparable at baseline. Performance bias – This refers to differences between groups in the care they received, aside from the intervention being evaluated. An example is the different ways by which reminders are triggered and used within and across groups such as electronic, paper and phone reminders for patients and providers. To reduce performance bias, one may standardize the intervention and blind participants from knowing whether an intervention was received and which intervention was received. Detection or measurement bias – This refers to differences between groups in how outcomes are determined. An example is where outcome assessors pay more attention to outcomes of patients known to be in the intervention group. To reduce detection bias, one may blind assessors from participants when measuring outcomes and ensure the same timing for assessment across groups. Attrition bias – This refers to differences between groups in ways that participants are withdrawn from the study. An example is the low rate of participant response in the intervention group despite having received reminders for follow-up care. To reduce attrition bias, one needs to acknowledge the dropout rate and analyze data according to an intent-to-treat principle (i.e., include data from those who dropped out in the analysis). Reporting bias – This refers to differences between reported and unreported findings. Examples include biases in publication, time lag, citation, language and outcome reporting depending on the nature and direction of the results. To reduce reporting bias, one may make the study protocol available with all pre-specified outcomes and report all expected outcomes in published results.

10.3.4. Confounders

Confounders are factors other than the intervention of interest that can distort the effect because they are associated with both the intervention and the outcome. For instance, in a study to demonstrate whether the adoption of a medication order entry system led to lower medication costs, there can be a number of potential confounders that can affect the outcome. These may include severity of illness of the patients, provider knowledge and experience with the system, and hospital policy on prescribing medications ( Harris et al., 2006 ). Another example is the evaluation of the effect of an antibiotic reminder system on the rate of post-operative deep venous thromboses ( dvt s). The confounders can be general improvements in clinical practice during the study such as prescribing patterns and post-operative care that are not related to the reminders ( Friedman & Wyatt, 2006 ).

To control for confounding effects, one may consider the use of matching, stratification and modelling. Matching involves the selection of similar groups with respect to their composition and behaviours. Stratification involves the division of participants into subgroups by selected variables, such as comorbidity index to control for severity of illness. Modelling involves the use of statistical techniques such as multiple regression to adjust for the effects of specific variables such as age, sex and/or severity of illness ( Higgins & Green, 2011 ).

10.3.5. Guidelines on Quality and Reporting

There are guidelines on the quality and reporting of comparative studies. The grade (Grading of Recommendations Assessment, Development and Evaluation) guidelines provide explicit criteria for rating the quality of studies in randomized trials and observational studies ( Guyatt et al., 2011 ). The extended consort (Consolidated Standards of Reporting Trials) Statements for non-pharmacologic trials ( Boutron, Moher, Altman, Schulz, & Ravaud, 2008 ), pragmatic trials ( Zwarestein et al., 2008 ), and eHealth interventions ( Baker et al., 2010 ) provide reporting guidelines for randomized trials.

The grade guidelines offer a system of rating quality of evidence in systematic reviews and guidelines. In this approach, to support estimates of intervention effects rct s start as high-quality evidence and observational studies as low-quality evidence. For each outcome in a study, five factors may rate down the quality of evidence. The final quality of evidence for each outcome would fall into one of high, moderate, low, and very low quality. These factors are listed below (for more details on the rating system, refer to Guyatt et al., 2011 ).

Design limitations – For rct s they cover the lack of allocation concealment, lack of blinding, large loss to follow-up, trial stopped early or selective outcome reporting. Inconsistency of results – Variations in outcomes due to unexplained heterogeneity. An example is the unexpected variation of effects across subgroups of patients by severity of illness in the use of preventive care reminders. Indirectness of evidence – Reliance on indirect comparisons due to restrictions in study populations, intervention, comparator or outcomes. An example is the 30-day readmission rate as a surrogate outcome for quality of computer-supported emergency care in hospitals. Imprecision of results – Studies with small sample size and few events typically would have wide confidence intervals and are considered of low quality. Publication bias – The selective reporting of results at the individual study level is already covered under design limitations, but is included here for completeness as it is relevant when rating quality of evidence across studies in systematic reviews.

The original consort Statement has 22 checklist items for reporting rct s. For non-pharmacologic trials extensions have been made to 11 items. For pragmatic trials extensions have been made to eight items. These items are listed below. For further details, readers can refer to Boutron and colleagues (2008) and the consort website ( consort , n.d.).

Title and abstract – one item on the means of randomization used. Introduction – one item on background, rationale, and problem addressed by the intervention. Methods – 10 items on participants, interventions, objectives, outcomes, sample size, randomization (sequence generation, allocation concealment, implementation), blinding (masking), and statistical methods. Results – seven items on participant flow, recruitment, baseline data, numbers analyzed, outcomes and estimation, ancillary analyses, adverse events. Discussion – three items on interpretation, generalizability, overall evidence.

The consort Statement for eHealth interventions describes the relevance of the consort recommendations to the design and reporting of eHealth studies with an emphasis on Internet-based interventions for direct use by patients, such as online health information resources, decision aides and phr s. Of particular importance is the need to clearly define the intervention components, their role in the overall care process, target population, implementation process, primary and secondary outcomes, denominators for outcome analyses, and real world potential (for details refer to Baker et al., 2010 ).

10.4. Case Examples

10.4.1. pragmatic rct in vascular risk decision support.

Holbrook and colleagues (2011) conducted a pragmatic rct to examine the effects of a cds intervention on vascular care and outcomes for older adults. The study is summarized below.

Setting – Community-based primary care practices with emr s in one Canadian province. Participants – English-speaking patients 55 years of age or older with diagnosed vascular disease, no cognitive impairment and not living in a nursing home, who had a provider visit in the past 12 months. Intervention – A Web-based individualized vascular tracking and advice cds system for eight top vascular risk factors and two diabetic risk factors, for use by both providers and patients and their families. Providers and staff could update the patient’s profile at any time and the cds algorithm ran nightly to update recommendations and colour highlighting used in the tracker interface. Intervention patients had Web access to the tracker, a print version mailed to them prior to the visit, and telephone support on advice. Design – Pragmatic, one-year, two-arm, multicentre rct , with randomization upon patient consent by phone, using an allocation-concealed online program. Randomization was by patient with stratification by provider using a block size of six. Trained reviewers examined emr data and conducted patient telephone interviews to collect risk factors, vascular history, and vascular events. Providers completed questionnaires on the intervention at study end. Patients had final 12-month lab checks on urine albumin, low-density lipoprotein cholesterol, and A1c levels. Outcomes – Primary outcome was based on change in process composite score ( pcs ) computed as the sum of frequency-weighted process score for each of the eight main risk factors with a maximum score of 27. The process was considered met if a risk factor had been checked. pcs was measured at baseline and study end with the difference as the individual primary outcome scores. The main secondary outcome was a clinical composite score ( ccs ) based on the same eight risk factors compared in two ways: a comparison of the mean number of clinical variables on target and the percentage of patients with improvement between the two groups. Other secondary outcomes were actual vascular event rates, individual pcs and ccs components, ratings of usability, continuity of care, patient ability to manage vascular risk, and quality of life using the EuroQol five dimensions questionnaire ( eq-5D) . Analysis – 1,100 patients were needed to achieve 90% power in detecting a one-point pcs difference between groups with a standard deviation of five points, two-tailed t -test for mean difference at 5% significance level, and a withdrawal rate of 10%. The pcs , ccs and eq-5D scores were analyzed using a generalized estimating equation accounting for clustering within providers. Descriptive statistics and χ2 tests or exact tests were done with other outcomes. Findings – 1,102 patients and 49 providers enrolled in the study. The intervention group with 545 patients had significant pcs improvement with a difference of 4.70 ( p < .001) on a 27-point scale. The intervention group also had significantly higher odds of rating improvements in their continuity of care (4.178, p < .001) and ability to improve their vascular health (3.07, p < .001). There was no significant change in vascular events, clinical variables and quality of life. Overall the cds intervention led to reduced vascular risks but not to improved clinical outcomes in a one-year follow-up.

10.4.2. Non-randomized Experiment in Antibiotic Prescribing in Primary Care

Mainous, Lambourne, and Nietert (2013) conducted a prospective non-randomized trial to examine the impact of a cds system on antibiotic prescribing for acute respiratory infections ( ari s) in primary care. The study is summarized below.

Setting – A primary care research network in the United States whose members use a common emr and pool data quarterly for quality improvement and research studies. Participants – An intervention group with nine practices across nine states, and a control group with 61 practices. Intervention – Point-of-care cds tool as customizable progress note templates based on existing emr features. cds recommendations reflect Centre for Disease Control and Prevention ( cdc ) guidelines based on a patient’s predominant presenting symptoms and age. cds was used to assist in ari diagnosis, prompt antibiotic use, record diagnosis and treatment decisions, and access printable patient and provider education resources from the cdc . Design – The intervention group received a multi-method intervention to facilitate provider cds adoption that included quarterly audit and feedback, best practice dissemination meetings, academic detailing site visits, performance review and cds training. The control group did not receive information on the intervention, the cds or education. Baseline data collection was for three months with follow-up of 15 months after cds implementation. Outcomes – The outcomes were frequency of inappropriate prescribing during an ari episode, broad-spectrum antibiotic use and diagnostic shift. Inappropriate prescribing was computed by dividing the number of ari episodes with diagnoses in the inappropriate category that had an antibiotic prescription by the total number of ari episodes with diagnosis for which antibiotics are inappropriate. Broad-spectrum antibiotic use was computed by all ari episodes with a broad-spectrum antibiotic prescription by the total number of ari episodes with an antibiotic prescription. Antibiotic drift was computed in two ways: dividing the number of ari episodes with diagnoses where antibiotics are appropriate by the total number of ari episodes with an antibiotic prescription; and dividing the number of ari episodes where antibiotics were inappropriate by the total number of ari episodes. Process measure included frequency of cds template use and whether the outcome measures differed by cds usage. Analysis – Outcomes were measured quarterly for each practice, weighted by the number of ari episodes during the quarter to assign greater weight to practices with greater numbers of relevant episodes and to periods with greater numbers of relevant episodes. Weighted means and 95% ci s were computed separately for adult and pediatric (less than 18 years of age) patients for each time period for both groups. Baseline means in outcome measures were compared between the two groups using weighted independent-sample t -tests. Linear mixed models were used to compare changes over the 18-month period. The models included time, intervention status, and were adjusted for practice characteristics such as specialty, size, region and baseline ari s. Random practice effects were included to account for clustering of repeated measures on practices over time. P -values of less than 0.05 were considered significant. Findings – For adult patients, inappropriate prescribing in ari episodes declined more among the intervention group (-0.6%) than the control group (4.2%)( p = 0.03), and prescribing of broad-spectrum antibiotics declined by 16.6% in the intervention group versus an increase of 1.1% in the control group ( p < 0.0001). For pediatric patients, there was a similar decline of 19.7% in the intervention group versus an increase of 0.9% in the control group ( p < 0.0001). In summary, the cds had a modest effect in reducing inappropriate prescribing for adults, but had a substantial effect in reducing the prescribing of broad-spectrum antibiotics in adult and pediatric patients.

10.4.3. Interrupted Time Series on EHR Impact in Nursing Care

Dowding, Turley, and Garrido (2012) conducted a prospective its study to examine the impact of ehr implementation on nursing care processes and outcomes. The study is summarized below.

Setting – Kaiser Permanente ( kp ) as a large not-for-profit integrated healthcare organization in the United States. Participants – 29 kp hospitals in the northern and southern regions of California. Intervention – An integrated ehr system implemented at all hospitals with cpoe , nursing documentation and risk assessment tools. The nursing component for risk assessment documentation of pressure ulcers and falls was consistent across hospitals and developed by clinical nurses and informaticists by consensus. Design – its design with monthly data on pressure ulcers and quarterly data on fall rates and risk collected over seven years between 2003 and 2009. All data were collected at the unit level for each hospital. Outcomes – Process measures were the proportion of patients with a fall risk assessment done and the proportion with a hospital-acquired pressure ulcer ( hapu ) risk assessment done within 24 hours of admission. Outcome measures were fall and hapu rates as part of the unit-level nursing care process and nursing sensitive outcome data collected routinely for all California hospitals. Fall rate was defined as the number of unplanned descents to the floor per 1,000 patient days, and hapu rate was the percentage of patients with stages i-IV or unstageable ulcer on the day of data collection. Analysis – Fall and hapu risk data were synchronized using the month in which the ehr was implemented at each hospital as time zero and aggregated across hospitals for each time period. Multivariate regression analysis was used to examine the effect of time, region and ehr . Findings – The ehr was associated with significant increase in document rates for hapu risk (2.21; 95% CI 0.67 to 3.75) and non-significant increase for fall risk (0.36; -3.58 to 4.30). The ehr was associated with 13% decrease in hapu rates (-0.76; -1.37 to -0.16) but no change in fall rates (-0.091; -0.29 to 011). Hospital region was a significant predictor of variation for hapu (0.72; 0.30 to 1.14) and fall rates (0.57; 0.41 to 0.72). During the study period, hapu rates decreased significantly (-0.16; -0.20 to -0.13) but not fall rates (0.0052; -0.01 to 0.02). In summary, ehr implementation was associated with a reduction in the number of hapu s but not patient falls, and changes over time and hospital region also affected outcomes.

10.5. Summary

In this chapter we introduced randomized and non-randomized experimental designs as two types of comparative studies used in eHealth evaluation. Randomization is the highest quality design as it reduces bias, but it is not always feasible. The methodological issues addressed include choice of variables, sample size, sources of biases, confounders, and adherence to reporting guidelines. Three case examples were included to show how eHealth comparative studies are done.

  • Baker T. B., Gustafson D. H., Shaw B., Hawkins R., Pingree S., Roberts L., Strecher V. Relevance of consort reporting criteria for research on eHealth interventions. Patient Education and Counselling. 2010; 81 (suppl. 7):77–86. [ PMC free article : PMC2993846 ] [ PubMed : 20843621 ]
  • Columbia University. (n.d.). Statistics: sample size / power calculation. Biomath (Division of Biomathematics/Biostatistics), Department of Pediatrics. New York: Columbia University Medical Centre. Retrieved from http://www ​.biomath.info/power/index.htm .
  • Boutron I., Moher D., Altman D. G., Schulz K. F., Ravaud P. consort Group. Extending the consort statement to randomized trials of nonpharmacologic treatment: Explanation and elaboration. Annals of Internal Medicine. 2008; 148 (4):295–309. [ PubMed : 18283207 ]
  • Cochrane Collaboration. Cochrane handbook. London: Author; (n.d.) Retrieved from http://handbook ​.cochrane.org/
  • consort Group. (n.d.). The consort statement . Retrieved from http://www ​.consort-statement.org/
  • Dowding D. W., Turley M., Garrido T. The impact of an electronic health record on nurse sensitive patient outcomes: an interrupted time series analysis. Journal of the American Medical Informatics Association. 2012; 19 (4):615–620. [ PMC free article : PMC3384108 ] [ PubMed : 22174327 ]
  • Friedman C. P., Wyatt J.C. Evaluation methods in biomedical informatics. 2nd ed. New York: Springer Science + Business Media, Inc; 2006.
  • Guyatt G., Oxman A. D., Akl E. A., Kunz R., Vist G., Brozek J. et al. Schunemann H. J. grade guidelines: 1. Introduction – grade evidence profiles and summary of findings tables. Journal of Clinical Epidemiology. 2011; 64 (4):383–394. [ PubMed : 21195583 ]
  • Harris A. D., McGregor J. C., Perencevich E. N., Furuno J. P., Zhu J., Peterson D. E., Finkelstein J. The use and interpretation of quasi-experimental studies in medical informatics. Journal of the American Medical Informatics Association. 2006; 13 (1):16–23. [ PMC free article : PMC1380192 ] [ PubMed : 16221933 ]
  • The Cochrane Collaboration. Cochrane handbook for systematic reviews of interventions. Higgins J. P. T., Green S., editors. London: 2011. (Version 5.1.0, updated March 2011) Retrieved from http://handbook ​.cochrane.org/
  • Holbrook A., Pullenayegum E., Thabane L., Troyan S., Foster G., Keshavjee K. et al. Curnew G. Shared electronic vascular risk decision support in primary care. Computerization of medical practices for the enhancement of therapeutic effectiveness (compete III) randomized trial. Archives of Internal Medicine. 2011; 171 (19):1736–1744. [ PubMed : 22025430 ]
  • Mainous III A. G., Lambourne C. A., Nietert P.J. Impact of a clinical decision support system on antibiotic prescribing for acute respiratory infections in primary care: quasi-experimental trial. Journal of the American Medical Informatics Association. 2013; 20 (2):317–324. [ PMC free article : PMC3638170 ] [ PubMed : 22759620 ]
  • Noordzij M., Tripepi G., Dekker F. W., Zoccali C., Tanck M. W., Jager K.J. Sample size calculations: basic principles and common pitfalls. Nephrology Dialysis Transplantation. 2010; 25 (5):1388–1393. Retrieved from http://ndt ​.oxfordjournals ​.org/content/early/2010/01/12/ndt ​.gfp732.short . [ PubMed : 20067907 ]
  • Vervloet M., Linn A. J., van Weert J. C. M., de Bakker D. H., Bouvy M. L., van Dijk L. The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: A systematic review of the literature. Journal of the American Medical Informatics Association. 2012; 19 (5):696–704. [ PMC free article : PMC3422829 ] [ PubMed : 22534082 ]
  • Zwarenstein M., Treweek S., Gagnier J. J., Altman D. G., Tunis S., Haynes B., Oxman A. D., Moher D. for the consort and Pragmatic Trials in Healthcare (Practihc) groups. Improving the reporting of pragmatic trials: an extension of the consort statement. British Medical Journal. 2008; 337 :a2390. [ PMC free article : PMC3266844 ] [ PubMed : 19001484 ] [ CrossRef ]
  • Zwarenstein M., Treweek S. What kind of randomized trials do we need? Canadian Medical Association Journal. 2009; 180 (10):998–1000. [ PMC free article : PMC2679816 ] [ PubMed : 19372438 ]

Appendix. Example of Sample Size Calculation

This is an example of sample size calculation for an rct that examines the effect of a cds system on reducing systolic blood pressure in hypertensive patients. The case is adapted from the example described in the publication by Noordzij et al. (2010) .

(a) Systolic blood pressure as a continuous outcome measured in mmHg

Based on similar studies in the literature with similar patients, the systolic blood pressure values from the comparison groups are expected to be normally distributed with a standard deviation of 20 mmHg. The evaluator wishes to detect a clinically relevant difference of 15 mmHg in systolic blood pressure as an outcome between the intervention group with cds and the control group without cds . Assuming a significance level or alpha of 0.05 for 2-tailed t -test and power of 0.80, the corresponding multipliers 1 are 1.96 and 0.842, respectively. Using the sample size equation for continuous outcome below we can calculate the sample size needed for the above study.

n = 2[(a+b)2σ2]/(μ1-μ2)2 where

n = sample size for each group

μ1 = population mean of systolic blood pressures in intervention group

μ2 = population mean of systolic blood pressures in control group

μ1- μ2 = desired difference in mean systolic blood pressures between groups

σ = population variance

a = multiplier for significance level (or alpha)

b = multiplier for power (or 1-beta)

Providing the values in the equation would give the sample size (n) of 28 samples per group as the result

n = 2[(1.96+0.842)2(202)]/152 or 28 samples per group

(b) Systolic blood pressure as a categorical outcome measured as below or above 140 mmHg (i.e., hypertension yes/no)

In this example a systolic blood pressure from a sample that is above 140 mmHg is considered an event of the patient with hypertension. Based on published literature the proportion of patients in the general population with hypertension is 30%. The evaluator wishes to detect a clinically relevant difference of 10% in systolic blood pressure as an outcome between the intervention group with cds and the control group without cds . This means the expected proportion of patients with hypertension is 20% (p1 = 0.2) in the intervention group and 30% (p2 = 0.3) in the control group. Assuming a significance level or alpha of 0.05 for 2-tailed t -test and power of 0.80 the corresponding multipliers are 1.96 and 0.842, respectively. Using the sample size equation for categorical outcome below, we can calculate the sample size needed for the above study.

n = [(a+b)2(p1q1+p2q2)]/χ2

p1 = proportion of patients with hypertension in intervention group

q1 = proportion of patients without hypertension in intervention group (or 1-p1)

p2 = proportion of patients with hypertension in control group

q2 = proportion of patients without hypertension in control group (or 1-p2)

χ = desired difference in proportion of hypertensive patients between two groups

Providing the values in the equation would give the sample size (n) of 291 samples per group as the result

n = [(1.96+0.842)2((0.2)(0.8)+(0.3)(0.7))]/(0.1)2 or 291 samples per group

From Table 3 on p. 1392 of Noordzij et al. (2010).

This publication is licensed under a Creative Commons License, Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0): see https://creativecommons.org/licenses/by-nc/4.0/

  • Cite this Page Lau F, Holbrook A. Chapter 10 Methods for Comparative Studies. In: Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.
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Original research article, a comparative analysis of student performance in an online vs. face-to-face environmental science course from 2009 to 2016.

a comparative research paper

  • Department of Biology, Fort Valley State University, Fort Valley, GA, United States

A growing number of students are now opting for online classes. They find the traditional classroom modality restrictive, inflexible, and impractical. In this age of technological advancement, schools can now provide effective classroom teaching via the Web. This shift in pedagogical medium is forcing academic institutions to rethink how they want to deliver their course content. The overarching purpose of this research was to determine which teaching method proved more effective over the 8-year period. The scores of 548 students, 401 traditional students and 147 online students, in an environmental science class were used to determine which instructional modality generated better student performance. In addition to the overarching objective, we also examined score variabilities between genders and classifications to determine if teaching modality had a greater impact on specific groups. No significant difference in student performance between online and face-to-face (F2F) learners overall, with respect to gender, or with respect to class rank were found. These data demonstrate the ability to similarly translate environmental science concepts for non-STEM majors in both traditional and online platforms irrespective of gender or class rank. A potential exists for increasing the number of non-STEM majors engaged in citizen science using the flexibility of online learning to teach environmental science core concepts.

Introduction

The advent of online education has made it possible for students with busy lives and limited flexibility to obtain a quality education. As opposed to traditional classroom teaching, Web-based instruction has made it possible to offer classes worldwide through a single Internet connection. Although it boasts several advantages over traditional education, online instruction still has its drawbacks, including limited communal synergies. Still, online education seems to be the path many students are taking to secure a degree.

This study compared the effectiveness of online vs. traditional instruction in an environmental studies class. Using a single indicator, we attempted to see if student performance was effected by instructional medium. This study sought to compare online and F2F teaching on three levels—pure modality, gender, and class rank. Through these comparisons, we investigated whether one teaching modality was significantly more effective than the other. Although there were limitations to the study, this examination was conducted to provide us with additional measures to determine if students performed better in one environment over another ( Mozes-Carmel and Gold, 2009 ).

The methods, procedures, and operationalization tools used in this assessment can be expanded upon in future quantitative, qualitative, and mixed method designs to further analyze this topic. Moreover, the results of this study serve as a backbone for future meta-analytical studies.

Origins of Online Education

Computer-assisted instruction is changing the pedagogical landscape as an increasing number of students are seeking online education. Colleges and universities are now touting the efficiencies of Web-based education and are rapidly implementing online classes to meet student needs worldwide. One study reported “increases in the number of online courses given by universities have been quite dramatic over the last couple of years” ( Lundberg et al., 2008 ). Think tanks are also disseminating statistics on Web-based instruction. “In 2010, the Sloan Consortium found a 17% increase in online students from the years before, beating the 12% increase from the previous year” ( Keramidas, 2012 ).

Contrary to popular belief, online education is not a new phenomenon. The first correspondence and distance learning educational programs were initiated in the mid-1800s by the University of London. This model of educational learning was dependent on the postal service and therefore wasn't seen in American until the later Nineteenth century. It was in 1873 when what is considered the first official correspondence educational program was established in Boston, Massachusetts known as the “Society to Encourage Home Studies.” Since then, non-traditional study has grown into what it is today considered a more viable online instructional modality. Technological advancement indubitably helped improve the speed and accessibility of distance learning courses; now students worldwide could attend classes from the comfort of their own homes.

Qualities of Online and Traditional Face to Face (F2F) Classroom Education

Online and traditional education share many qualities. Students are still required to attend class, learn the material, submit assignments, and complete group projects. While teachers, still have to design curriculums, maximize instructional quality, answer class questions, motivate students to learn, and grade assignments. Despite these basic similarities, there are many differences between the two modalities. Traditionally, classroom instruction is known to be teacher-centered and requires passive learning by the student, while online instruction is often student-centered and requires active learning.

In teacher-centered, or passive learning, the instructor usually controls classroom dynamics. The teacher lectures and comments, while students listen, take notes, and ask questions. In student-centered, or active learning, the students usually determine classroom dynamics as they independently analyze the information, construct questions, and ask the instructor for clarification. In this scenario, the teacher, not the student, is listening, formulating, and responding ( Salcedo, 2010 ).

In education, change comes with questions. Despite all current reports championing online education, researchers are still questioning its efficacy. Research is still being conducted on the effectiveness of computer-assisted teaching. Cost-benefit analysis, student experience, and student performance are now being carefully considered when determining whether online education is a viable substitute for classroom teaching. This decision process will most probably carry into the future as technology improves and as students demand better learning experiences.

Thus far, “literature on the efficacy of online courses is expansive and divided” ( Driscoll et al., 2012 ). Some studies favor traditional classroom instruction, stating “online learners will quit more easily” and “online learning can lack feedback for both students and instructors” ( Atchley et al., 2013 ). Because of these shortcomings, student retention, satisfaction, and performance can be compromised. Like traditional teaching, distance learning also has its apologists who aver online education produces students who perform as well or better than their traditional classroom counterparts ( Westhuis et al., 2006 ).

The advantages and disadvantages of both instructional modalities need to be fully fleshed out and examined to truly determine which medium generates better student performance. Both modalities have been proven to be relatively effective, but, as mentioned earlier, the question to be asked is if one is truly better than the other.

Student Need for Online Education

With technological advancement, learners now want quality programs they can access from anywhere and at any time. Because of these demands, online education has become a viable, alluring option to business professionals, stay-at home-parents, and other similar populations. In addition to flexibility and access, multiple other face value benefits, including program choice and time efficiency, have increased the attractiveness of distance learning ( Wladis et al., 2015 ).

First, prospective students want to be able to receive a quality education without having to sacrifice work time, family time, and travel expense. Instead of having to be at a specific location at a specific time, online educational students have the freedom to communicate with instructors, address classmates, study materials, and complete assignments from any Internet-accessible point ( Richardson and Swan, 2003 ). This type of flexibility grants students much-needed mobility and, in turn, helps make the educational process more enticing. According to Lundberg et al. (2008) “the student may prefer to take an online course or a complete online-based degree program as online courses offer more flexible study hours; for example, a student who has a job could attend the virtual class watching instructional film and streaming videos of lectures after working hours.”

Moreover, more study time can lead to better class performance—more chapters read, better quality papers, and more group project time. Studies on the relationship between study time and performance are limited; however, it is often assumed the online student will use any surplus time to improve grades ( Bigelow, 2009 ). It is crucial to mention the link between flexibility and student performance as grades are the lone performance indicator of this research.

Second, online education also offers more program choices. With traditional classroom study, students are forced to take courses only at universities within feasible driving distance or move. Web-based instruction, on the other hand, grants students electronic access to multiple universities and course offerings ( Salcedo, 2010 ). Therefore, students who were once limited to a few colleges within their immediate area can now access several colleges worldwide from a single convenient location.

Third, with online teaching, students who usually don't participate in class may now voice their opinions and concerns. As they are not in a classroom setting, quieter students may feel more comfortable partaking in class dialogue without being recognized or judged. This, in turn, may increase average class scores ( Driscoll et al., 2012 ).

Benefits of Face-to-Face (F2F) Education via Traditional Classroom Instruction

The other modality, classroom teaching, is a well-established instructional medium in which teaching style and structure have been refined over several centuries. Face-to-face instruction has numerous benefits not found in its online counterpart ( Xu and Jaggars, 2016 ).

First and, perhaps most importantly, classroom instruction is extremely dynamic. Traditional classroom teaching provides real-time face-to-face instruction and sparks innovative questions. It also allows for immediate teacher response and more flexible content delivery. Online instruction dampens the learning process because students must limit their questions to blurbs, then grant the teacher and fellow classmates time to respond ( Salcedo, 2010 ). Over time, however, online teaching will probably improve, enhancing classroom dynamics and bringing students face-to face with their peers/instructors. However, for now, face-to-face instruction provides dynamic learning attributes not found in Web-based teaching ( Kemp and Grieve, 2014 ).

Second, traditional classroom learning is a well-established modality. Some students are opposed to change and view online instruction negatively. These students may be technophobes, more comfortable with sitting in a classroom taking notes than sitting at a computer absorbing data. Other students may value face-to-face interaction, pre and post-class discussions, communal learning, and organic student-teacher bonding ( Roval and Jordan, 2004 ). They may see the Internet as an impediment to learning. If not comfortable with the instructional medium, some students may shun classroom activities; their grades might slip and their educational interest might vanish. Students, however, may eventually adapt to online education. With more universities employing computer-based training, students may be forced to take only Web-based courses. Albeit true, this doesn't eliminate the fact some students prefer classroom intimacy.

Third, face-to-face instruction doesn't rely upon networked systems. In online learning, the student is dependent upon access to an unimpeded Internet connection. If technical problems occur, online students may not be able to communicate, submit assignments, or access study material. This problem, in turn, may frustrate the student, hinder performance, and discourage learning.

Fourth, campus education provides students with both accredited staff and research libraries. Students can rely upon administrators to aid in course selection and provide professorial recommendations. Library technicians can help learners edit their papers, locate valuable study material, and improve study habits. Research libraries may provide materials not accessible by computer. In all, the traditional classroom experience gives students important auxiliary tools to maximize classroom performance.

Fifth, traditional classroom degrees trump online educational degrees in terms of hiring preferences. Many academic and professional organizations do not consider online degrees on par with campus-based degrees ( Columbaro and Monaghan, 2009 ). Often, prospective hiring bodies think Web-based education is a watered-down, simpler means of attaining a degree, often citing poor curriculums, unsupervised exams, and lenient homework assignments as detriments to the learning process.

Finally, research shows online students are more likely to quit class if they do not like the instructor, the format, or the feedback. Because they work independently, relying almost wholly upon self-motivation and self-direction, online learners may be more inclined to withdraw from class if they do not get immediate results.

The classroom setting provides more motivation, encouragement, and direction. Even if a student wanted to quit during the first few weeks of class, he/she may be deterred by the instructor and fellow students. F2F instructors may be able to adjust the structure and teaching style of the class to improve student retention ( Kemp and Grieve, 2014 ). With online teaching, instructors are limited to electronic correspondence and may not pick-up on verbal and non-verbal cues.

Both F2F and online teaching have their pros and cons. More studies comparing the two modalities to achieve specific learning outcomes in participating learner populations are required before well-informed decisions can be made. This study examined the two modalities over eight (8) years on three different levels. Based on the aforementioned information, the following research questions resulted.

RQ1: Are there significant differences in academic performance between online and F2F students enrolled in an environmental science course?

RQ2: Are there gender differences between online and F2F student performance in an environmental science course?

RQ3: Are there significant differences between the performance of online and F2F students in an environmental science course with respect to class rank?

The results of this study are intended to edify teachers, administrators, and policymakers on which medium may work best.

Methodology

Participants.

The study sample consisted of 548 FVSU students who completed the Environmental Science class between 2009 and 2016. The final course grades of the participants served as the primary comparative factor in assessing performance differences between online and F2F instruction. Of the 548 total participants, 147 were online students while 401 were traditional students. This disparity was considered a limitation of the study. Of the 548 total students, 246 were male, while 302 were female. The study also used students from all four class ranks. There were 187 freshmen, 184 sophomores, 76 juniors, and 101 seniors. This was a convenience, non-probability sample so the composition of the study set was left to the discretion of the instructor. No special preferences or weights were given to students based upon gender or rank. Each student was considered a single, discrete entity or statistic.

All sections of the course were taught by a full-time biology professor at FVSU. The professor had over 10 years teaching experience in both classroom and F2F modalities. The professor was considered an outstanding tenured instructor with strong communication and management skills.

The F2F class met twice weekly in an on-campus classroom. Each class lasted 1 h and 15 min. The online class covered the same material as the F2F class, but was done wholly on-line using the Desire to Learn (D2L) e-learning system. Online students were expected to spend as much time studying as their F2F counterparts; however, no tracking measure was implemented to gauge e-learning study time. The professor combined textbook learning, lecture and class discussion, collaborative projects, and assessment tasks to engage students in the learning process.

This study did not differentiate between part-time and full-time students. Therefore, many part-time students may have been included in this study. This study also did not differentiate between students registered primarily at FVSU or at another institution. Therefore, many students included in this study may have used FVSU as an auxiliary institution to complete their environmental science class requirement.

Test Instruments

In this study, student performance was operationalized by final course grades. The final course grade was derived from test, homework, class participation, and research project scores. The four aforementioned assessments were valid and relevant; they were useful in gauging student ability and generating objective performance measurements. The final grades were converted from numerical scores to traditional GPA letters.

Data Collection Procedures

The sample 548 student grades were obtained from FVSU's Office of Institutional Research Planning and Effectiveness (OIRPE). The OIRPE released the grades to the instructor with the expectation the instructor would maintain confidentiality and not disclose said information to third parties. After the data was obtained, the instructor analyzed and processed the data though SPSS software to calculate specific values. These converted values were subsequently used to draw conclusions and validate the hypothesis.

Summary of the Results: The chi-square analysis showed no significant difference in student performance between online and face-to-face (F2F) learners [χ 2 (4, N = 548) = 6.531, p > 0.05]. The independent sample t -test showed no significant difference in student performance between online and F2F learners with respect to gender [ t (145) = 1.42, p = 0.122]. The 2-way ANOVA showed no significant difference in student performance between online and F2F learners with respect to class rank ( Girard et al., 2016 ).

Research question #1 was to determine if there was a statistically significant difference between the academic performance of online and F2F students.

Research Question 1

The first research question investigated if there was a difference in student performance between F2F and online learners.

To investigate the first research question, we used a traditional chi-square method to analyze the data. The chi-square analysis is particularly useful for this type of comparison because it allows us to determine if the relationship between teaching modality and performance in our sample set can be extended to the larger population. The chi-square method provides us with a numerical result which can be used to determine if there is a statistically significant difference between the two groups.

Table 1 shows us the mean and SD for modality and for gender. It is a general breakdown of numbers to visually elucidate any differences between scores and deviations. The mean GPA for both modalities is similar with F2F learners scoring a 69.35 and online learners scoring a 68.64. Both groups had fairly similar SDs. A stronger difference can be seen between the GPAs earned by men and women. Men had a 3.23 mean GPA while women had a 2.9 mean GPA. The SDs for both groups were almost identical. Even though the 0.33 numerical difference may look fairly insignificant, it must be noted that a 3.23 is approximately a B+ while a 2.9 is approximately a B. Given a categorical range of only A to F, a plus differential can be considered significant.

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Table 1 . Means and standard deviations for 8 semester- “Environmental Science data set.”

The mean grade for men in the environmental online classes ( M = 3.23, N = 246, SD = 1.19) was higher than the mean grade for women in the classes ( M = 2.9, N = 302, SD = 1.20) (see Table 1 ).

First, a chi-square analysis was performed using SPSS to determine if there was a statistically significant difference in grade distribution between online and F2F students. Students enrolled in the F2F class had the highest percentage of A's (63.60%) as compared to online students (36.40%). Table 2 displays grade distribution by course delivery modality. The difference in student performance was statistically significant, χ 2 (4, N = 548) = 6.531, p > 0.05. Table 3 shows the gender difference on student performance between online and F2F students.

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Table 2 . Contingency table for student's academic performance ( N = 548).

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Table 3 . Gender * performance crosstabulation.

Table 2 shows us the performance measures of online and F2F students by grade category. As can be seen, F2F students generated the highest performance numbers for each grade category. However, this disparity was mostly due to a higher number of F2F students in the study. There were 401 F2F students as opposed to just 147 online students. When viewing grades with respect to modality, there are smaller percentage differences between respective learners ( Tanyel and Griffin, 2014 ). For example, F2F learners earned 28 As (63.60% of total A's earned) while online learners earned 16 As (36.40% of total A's earned). However, when viewing the A grade with respect to total learners in each modality, it can be seen that 28 of the 401 F2F students (6.9%) earned As as compared to 16 of 147 (10.9%) online learners. In this case, online learners scored relatively higher in this grade category. The latter measure (grade total as a percent of modality total) is a better reflection of respective performance levels.

Given a critical value of 7.7 and a d.f. of 4, we were able to generate a chi-squared measure of 6.531. The correlating p -value of 0.163 was greater than our p -value significance level of 0.05. We, therefore, had to accept the null hypothesis and reject the alternative hypothesis. There is no statistically significant difference between the two groups in terms of performance scores.

Research Question 2

The second research question was posed to evaluate if there was a difference between online and F2F varied with gender. Does online and F2F student performance vary with respect to gender? Table 3 shows the gender difference on student performance between online and face to face students. We used chi-square test to determine if there were differences in online and F2F student performance with respect to gender. The chi-square test with alpha equal to 0.05 as criterion for significance. The chi-square result shows that there is no statistically significant difference between men and women in terms of performance.

Research Question 3

The third research question tried to determine if there was a difference between online and F2F varied with respect to class rank. Does online and F2F student performance vary with respect to class rank?

Table 4 shows the mean scores and standard deviations of freshman, sophomore, and junior and senior students for both online and F2F student performance. To test the third hypothesis, we used a two-way ANOVA. The ANOVA is a useful appraisal tool for this particular hypothesis as it tests the differences between multiple means. Instead of testing specific differences, the ANOVA generates a much broader picture of average differences. As can be seen in Table 4 , the ANOVA test for this particular hypothesis states there is no significant difference between online and F2F learners with respect to class rank. Therefore, we must accept the null hypothesis and reject the alternative hypothesis.

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Table 4 . Descriptive analysis of student performance by class rankings gender.

The results of the ANOVA show there is no significant difference in performance between online and F2F students with respect to class rank. Results of ANOVA is presented in Table 5 .

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Table 5 . Analysis of variance (ANOVA) for online and F2F of class rankings.

As can be seen in Table 4 , the ANOVA test for this particular hypothesis states there is no significant difference between online and F2F learners with respect to class rank. Therefore, we must accept the null hypothesis and reject the alternative hypothesis.

Discussion and Social Implications

The results of the study show there is no significant difference in performance between online and traditional classroom students with respect to modality, gender, or class rank in a science concepts course for non-STEM majors. Although there were sample size issues and study limitations, this assessment shows both online learners and classroom learners perform at the same level. This conclusion indicates teaching modality may not matter as much as other factors. Given the relatively sparse data on pedagogical modality comparison given specific student population characteristics, this study could be considered innovative. In the current literature, we have not found a study of this nature comparing online and F2F non-STEM majors with respect to three separate factors—medium, gender, and class rank—and the ability to learn science concepts and achieve learning outcomes. Previous studies have compared traditional classroom learning vs. F2F learning for other factors (including specific courses, costs, qualitative analysis, etcetera, but rarely regarding outcomes relevant to population characteristics of learning for a specific science concepts course over many years) ( Liu, 2005 ).

In a study evaluating the transformation of a graduate level course for teachers, academic quality of the online course and learning outcomes were evaluated. The study evaluated the ability of course instructors to design the course for online delivery and develop various interactive multimedia models at a cost-savings to the respective university. The online learning platform proved effective in translating information where tested students successfully achieved learning outcomes comparable to students taking the F2F course ( Herman and Banister, 2007 ).

Another study evaluated the similarities and differences in F2F and online learning in a non-STEM course, “Foundations of American Education” and overall course satisfaction by students enrolled in either of the two modalities. F2F and online course satisfaction was qualitatively and quantitative analyzed. However, in analyzing online and F2F course feedback using quantitative feedback, online course satisfaction was less than F2F satisfaction. When qualitative data was used, course satisfaction was similar between modalities ( Werhner, 2010 ). The course satisfaction data and feedback was used to suggest a number of posits for effective online learning in the specific course. The researcher concluded that there was no difference in the learning success of students enrolled in the online vs. F2F course, stating that “in terms of learning, students who apply themselves diligently should be successful in either format” ( Dell et al., 2010 ). The author's conclusion presumes that the “issues surrounding class size are under control and that the instructor has a course load that makes the intensity of the online course workload feasible” where the authors conclude that the workload for online courses is more than for F2F courses ( Stern, 2004 ).

In “A Meta-Analysis of Three Types of Interaction Treatments in Distance Education,” Bernard et al. (2009) conducted a meta-analysis evaluating three types of instructional and/or media conditions designed into distance education (DE) courses known as interaction treatments (ITs)—student–student (SS), student–teacher (ST), or student–content (SC) interactions—to other DE instructional/interaction treatments. The researchers found that a strong association existed between the integration of these ITs into distance education courses and achievement compared with blended or F2F modalities of learning. The authors speculated that this was due to increased cognitive engagement based in these three interaction treatments ( Larson and Sung, 2009 ).

Other studies evaluating students' preferences (but not efficacy) for online vs. F2F learning found that students prefer online learning when it was offered, depending on course topic, and online course technology platform ( Ary and Brune, 2011 ). F2F learning was preferred when courses were offered late morning or early afternoon 2–3 days/week. A significant preference for online learning resulted across all undergraduate course topics (American history and government, humanities, natural sciences, social, and behavioral sciences, diversity, and international dimension) except English composition and oral communication. A preference for analytical and quantitative thought courses was also expressed by students, though not with statistically significant results ( Mann and Henneberry, 2014 ). In this research study, we looked at three hypothesis comparing online and F2F learning. In each case, the null hypothesis was accepted. Therefore, at no level of examination did we find a significant difference between online and F2F learners. This finding is important because it tells us traditional-style teaching with its heavy emphasis on interpersonal classroom dynamics may 1 day be replaced by online instruction. According to Daymont and Blau (2008) online learners, regardless of gender or class rank, learn as much from electronic interaction as they do from personal interaction. Kemp and Grieve (2014) also found that both online and F2F learning for psychology students led to similar academic performance. Given the cost efficiencies and flexibility of online education, Web-based instructional systems may rapidly rise.

A number of studies support the economic benefits of online vs. F2F learning, despite differences in social constructs and educational support provided by governments. In a study by Li and Chen (2012) higher education institutions benefit the most from two of four outputs—research outputs and distance education—with teaching via distance education at both the undergraduate and graduate levels more profitable than F2F teaching at higher education institutions in China. Zhang and Worthington (2017) reported an increasing cost benefit for the use of distance education over F2F instruction as seen at 37 Australian public universities over 9 years from 2003 to 2012. Maloney et al. (2015) and Kemp and Grieve (2014) also found significant savings in higher education when using online learning platforms vs. F2F learning. In the West, the cost efficiency of online learning has been demonstrated by several research studies ( Craig, 2015 ). Studies by Agasisti and Johnes (2015) and Bartley and Golek (2004) both found the cost benefits of online learning significantly greater than that of F2F learning at U.S. institutions.

Knowing there is no significant difference in student performance between the two mediums, institutions of higher education may make the gradual shift away from traditional instruction; they may implement Web-based teaching to capture a larger worldwide audience. If administered correctly, this shift to Web-based teaching could lead to a larger buyer population, more cost efficiencies, and more university revenue.

The social implications of this study should be touted; however, several concerns regarding generalizability need to be taken into account. First, this study focused solely on students from an environmental studies class for non-STEM majors. The ability to effectively prepare students for scientific professions without hands-on experimentation has been contended. As a course that functions to communicate scientific concepts, but does not require a laboratory based component, these results may not translate into similar performance of students in an online STEM course for STEM majors or an online course that has an online laboratory based co-requisite when compared to students taking traditional STEM courses for STEM majors. There are few studies that suggest the landscape may be changing with the ability to effectively train students in STEM core concepts via online learning. Biel and Brame (2016) reported successfully translating the academic success of F2F undergraduate biology courses to online biology courses. However, researchers reported that of the large-scale courses analyzed, two F2F sections outperformed students in online sections, and three found no significant difference. A study by Beale et al. (2014) comparing F2F learning with hybrid learning in an embryology course found no difference in overall student performance. Additionally, the bottom quartile of students showed no differential effect of the delivery method on examination scores. Further, a study from Lorenzo-Alvarez et al. (2019) found that radiology education in an online learning platform resulted in similar academic outcomes as F2F learning. Larger scale research is needed to determine the effectiveness of STEM online learning and outcomes assessments, including workforce development results.

In our research study, it is possible the study participants may have been more knowledgeable about environmental science than about other subjects. Therefore, it should be noted this study focused solely on students taking this one particular class. Given the results, this course presents a unique potential for increasing the number of non-STEM majors engaged in citizen science using the flexibility of online learning to teach environmental science core concepts.

Second, the operationalization measure of “grade” or “score” to determine performance level may be lacking in scope and depth. The grades received in a class may not necessarily show actual ability, especially if the weights were adjusted to heavily favor group tasks and writing projects. Other performance indicators may be better suited to properly access student performance. A single exam containing both multiple choice and essay questions may be a better operationalization indicator of student performance. This type of indicator will provide both a quantitative and qualitative measure of subject matter comprehension.

Third, the nature of the student sample must be further dissected. It is possible the online students in this study may have had more time than their counterparts to learn the material and generate better grades ( Summers et al., 2005 ). The inverse holds true, as well. Because this was a convenience non-probability sampling, the chances of actually getting a fair cross section of the student population were limited. In future studies, greater emphasis must be placed on selecting proper study participants, those who truly reflect proportions, types, and skill levels.

This study was relevant because it addressed an important educational topic; it compared two student groups on multiple levels using a single operationalized performance measure. More studies, however, of this nature need to be conducted before truly positing that online and F2F teaching generate the same results. Future studies need to eliminate spurious causal relationships and increase generalizability. This will maximize the chances of generating a definitive, untainted results. This scientific inquiry and comparison into online and traditional teaching will undoubtedly garner more attention in the coming years.

Our study compared learning via F2F vs. online learning modalities in teaching an environmental science course additionally evaluating factors of gender and class rank. These data demonstrate the ability to similarly translate environmental science concepts for non-STEM majors in both traditional and online platforms irrespective of gender or class rank. The social implications of this finding are important for advancing access to and learning of scientific concepts by the general population, as many institutions of higher education allow an online course to be taken without enrolling in a degree program. Thus, the potential exists for increasing the number of non-STEM majors engaged in citizen science using the flexibility of online learning to teach environmental science core concepts.

Limitations of the Study

The limitations of the study centered around the nature of the sample group, student skills/abilities, and student familiarity with online instruction. First, because this was a convenience, non-probability sample, the independent variables were not adjusted for real-world accuracy. Second, student intelligence and skill level were not taken into consideration when separating out comparison groups. There exists the possibility that the F2F learners in this study may have been more capable than the online students and vice versa. This limitation also applies to gender and class rank differences ( Friday et al., 2006 ). Finally, there may have been ease of familiarity issues between the two sets of learners. Experienced traditional classroom students now taking Web-based courses may be daunted by the technical aspect of the modality. They may not have had the necessary preparation or experience to efficiently e-learn, thus leading to lowered scores ( Helms, 2014 ). In addition to comparing online and F2F instructional efficacy, future research should also analyze blended teaching methods for the effectiveness of courses for non-STEM majors to impart basic STEM concepts and see if the blended style is more effective than any one pure style.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by Fort Valley State University Human Subjects Institutional Review Board. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

JP provided substantial contributions to the conception of the work, acquisition and analysis of data for the work, and is the corresponding author on this paper who agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. FJ provided substantial contributions to the design of the work, interpretation of the data for the work, and revised it critically for intellectual content.

This research was supported in part by funding from the National Science Foundation, Awards #1649717, 1842510, Ñ900572, and 1939739 to FJ.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank the reviewers for their detailed comments and feedback that assisted in the revising of our original manuscript.

Agasisti, T., and Johnes, G. (2015). Efficiency, costs, rankings and heterogeneity: the case of US higher education. Stud. High. Educ. 40, 60–82. doi: 10.1080/03075079.2013.818644

CrossRef Full Text | Google Scholar

Ary, E. J., and Brune, C. W. (2011). A comparison of student learning outcomes in traditional and online personal finance courses. MERLOT J. Online Learn. Teach. 7, 465–474.

Google Scholar

Atchley, W., Wingenbach, G., and Akers, C. (2013). Comparison of course completion and student performance through online and traditional courses. Int. Rev. Res. Open Dist. Learn. 14, 104–116. doi: 10.19173/irrodl.v14i4.1461

Bartley, S. J., and Golek, J. H. (2004). Evaluating the cost effectiveness of online and face-to-face instruction. Educ. Technol. Soc. 7, 167–175.

Beale, E. G., Tarwater, P. M., and Lee, V. H. (2014). A retrospective look at replacing face-to-face embryology instruction with online lectures in a human anatomy course. Am. Assoc. Anat. 7, 234–241. doi: 10.1002/ase.1396

PubMed Abstract | CrossRef Full Text | Google Scholar

Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkesh, M. A., et al. (2009). A meta-analysis of three types of interaction treatments in distance education. Rev. Educ. Res. 79, 1243–1289. doi: 10.3102/0034654309333844

Biel, R., and Brame, C. J. (2016). Traditional versus online biology courses: connecting course design and student learning in an online setting. J. Microbiol. Biol. Educ. 17, 417–422. doi: 10.1128/jmbe.v17i3.1157

Bigelow, C. A. (2009). Comparing student performance in an online versus a face to face introductory turfgrass science course-a case study. NACTA J. 53, 1–7.

Columbaro, N. L., and Monaghan, C. H. (2009). Employer perceptions of online degrees: a literature review. Online J. Dist. Learn. Administr. 12.

Craig, R. (2015). A Brief History (and Future) of Online Degrees. Forbes/Education . Available online at: https://www.forbes.com/sites/ryancraig/2015/06/23/a-brief-history-and-future-of-online-degrees/#e41a4448d9a8

Daymont, T., and Blau, G. (2008). Student performance in online and traditional sections of an undergraduate management course. J. Behav. Appl. Manag. 9, 275–294.

Dell, C. A., Low, C., and Wilker, J. F. (2010). Comparing student achievement in online and face-to-face class formats. J. Online Learn. Teach. Long Beach 6, 30–42.

Driscoll, A., Jicha, K., Hunt, A. N., Tichavsky, L., and Thompson, G. (2012). Can online courses deliver in-class results? A comparison of student performance and satisfaction in an online versus a face-to-face introductory sociology course. Am. Sociol. Assoc . 40, 312–313. doi: 10.1177/0092055X12446624

Friday, E., Shawnta, S., Green, A. L., and Hill, A. Y. (2006). A multi-semester comparison of student performance between multiple traditional and online sections of two management courses. J. Behav. Appl. Manag. 8, 66–81.

Girard, J. P., Yerby, J., and Floyd, K. (2016). Knowledge retention in capstone experiences: an analysis of online and face-to-face courses. Knowl. Manag. ELearn. 8, 528–539. doi: 10.34105/j.kmel.2016.08.033

Helms, J. L. (2014). Comparing student performance in online and face-to-face delivery modalities. J. Asynchr. Learn. Netw. 18, 1–14. doi: 10.24059/olj.v18i1.348

Herman, T., and Banister, S. (2007). Face-to-face versus online coursework: a comparison of costs and learning outcomes. Contemp. Issues Technol. Teach. Educ. 7, 318–326.

Kemp, N., and Grieve, R. (2014). Face-to-Face or face-to-screen? Undergraduates' opinions and test performance in classroom vs. online learning. Front. Psychol. 5:1278. doi: 10.3389/fpsyg.2014.01278

Keramidas, C. G. (2012). Are undergraduate students ready for online learning? A comparison of online and face-to-face sections of a course. Rural Special Educ. Q . 31, 25–39. doi: 10.1177/875687051203100405

Larson, D.K., and Sung, C. (2009). Comparing student performance: online versus blended versus face-to-face. J. Asynchr. Learn. Netw. 13, 31–42. doi: 10.24059/olj.v13i1.1675

Li, F., and Chen, X. (2012). Economies of scope in distance education: the case of Chinese Research Universities. Int. Rev. Res. Open Distrib. Learn. 13, 117–131.

Liu, Y. (2005). Effects of online instruction vs. traditional instruction on student's learning. Int. J. Instruct. Technol. Dist. Learn. 2, 57–64.

Lorenzo-Alvarez, R., Rudolphi-Solero, T., Ruiz-Gomez, M. J., and Sendra-Portero, F. (2019). Medical student education for abdominal radiographs in a 3D virtual classroom versus traditional classroom: a randomized controlled trial. Am. J. Roentgenol. 213, 644–650. doi: 10.2214/AJR.19.21131

Lundberg, J., Castillo-Merino, D., and Dahmani, M. (2008). Do online students perform better than face-to-face students? Reflections and a short review of some Empirical Findings. Rev. Univ. Soc. Conocim . 5, 35–44. doi: 10.7238/rusc.v5i1.326

Maloney, S., Nicklen, P., Rivers, G., Foo, J., Ooi, Y. Y., Reeves, S., et al. (2015). Cost-effectiveness analysis of blended versus face-to-face delivery of evidence-based medicine to medical students. J. Med. Internet Res. 17:e182. doi: 10.2196/jmir.4346

Mann, J. T., and Henneberry, S. R. (2014). Online versus face-to-face: students' preferences for college course attributes. J. Agric. Appl. Econ . 46, 1–19. doi: 10.1017/S1074070800000602

Mozes-Carmel, A., and Gold, S. S. (2009). A comparison of online vs proctored final exams in online classes. Imanagers J. Educ. Technol. 6, 76–81. doi: 10.26634/jet.6.1.212

Richardson, J. C., and Swan, K. (2003). Examining social presence in online courses in relation to student's perceived learning and satisfaction. J. Asynchr. Learn. 7, 68–88.

Roval, A. P., and Jordan, H. M. (2004). Blended learning and sense of community: a comparative analysis with traditional and fully online graduate courses. Int. Rev. Res. Open Dist. Learn. 5. doi: 10.19173/irrodl.v5i2.192

Salcedo, C. S. (2010). Comparative analysis of learning outcomes in face-to-face foreign language classes vs. language lab and online. J. Coll. Teach. Learn. 7, 43–54. doi: 10.19030/tlc.v7i2.88

Stern, B. S. (2004). A comparison of online and face-to-face instruction in an undergraduate foundations of american education course. Contemp. Issues Technol. Teach. Educ. J. 4, 196–213.

Summers, J. J., Waigandt, A., and Whittaker, T. A. (2005). A comparison of student achievement and satisfaction in an online versus a traditional face-to-face statistics class. Innov. High. Educ. 29, 233–250. doi: 10.1007/s10755-005-1938-x

Tanyel, F., and Griffin, J. (2014). A Ten-Year Comparison of Outcomes and Persistence Rates in Online versus Face-to-Face Courses . Retrieved from: https://www.westga.edu/~bquest/2014/onlinecourses2014.pdf

Werhner, M. J. (2010). A comparison of the performance of online versus traditional on-campus earth science students on identical exams. J. Geosci. Educ. 58, 310–312. doi: 10.5408/1.3559697

Westhuis, D., Ouellette, P. M., and Pfahler, C. L. (2006). A comparative analysis of on-line and classroom-based instructional formats for teaching social work research. Adv. Soc. Work 7, 74–88. doi: 10.18060/184

Wladis, C., Conway, K. M., and Hachey, A. C. (2015). The online STEM classroom-who succeeds? An exploration of the impact of ethnicity, gender, and non-traditional student characteristics in the community college context. Commun. Coll. Rev. 43, 142–164. doi: 10.1177/0091552115571729

Xu, D., and Jaggars, S. S. (2016). Performance gaps between online and face-to-face courses: differences across types of students and academic subject areas. J. Higher Educ. 85, 633–659. doi: 10.1353/jhe.2014.0028

Zhang, L.-C., and Worthington, A. C. (2017). Scale and scope economies of distance education in Australian universities. Stud. High. Educ. 42, 1785–1799. doi: 10.1080/03075079.2015.1126817

Keywords: face-to-face (F2F), traditional classroom teaching, web-based instructions, information and communication technology (ICT), online learning, desire to learn (D2L), passive learning, active learning

Citation: Paul J and Jefferson F (2019) A Comparative Analysis of Student Performance in an Online vs. Face-to-Face Environmental Science Course From 2009 to 2016. Front. Comput. Sci. 1:7. doi: 10.3389/fcomp.2019.00007

Received: 15 May 2019; Accepted: 15 October 2019; Published: 12 November 2019.

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Copyright © 2019 Paul and Jefferson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jasmine Paul, paulj@fvsu.edu

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Comparative analysis is a multidisciplinary method, which spans a wide cross-section of disciplines (Azarian, International Journal of Humanities and Social Science, 1(4), 113–125 (2014)). It is the process of comparing multiple units of study for the purpose of scientific discovery and for informing policy decisions (Rogers, Comparative effectiveness research, 2014). Even though there has been a renewed interest in comparative analysis as a research method over the last decade in fields such as education, it has been used in studies for decades.

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Azarian, R. (2011). Potentials and limitations of comparative method in social science. International Journal of Humanities and Social Science, 1 (4), 113–125. http://www.ijhssnet.com/journals/Vol._1_No._4%3b_April_2011/15.pdf

Bray, M., Adamson, B., & Mason, M. (2014). Comparative education research: Approaches and methods . Springer.

Crossley, M. (2002). Comparative and international education: Contemporary challenges, reconceptualization and new directions for the field. Current Issues in Comparative Education, 4 (2), 81–86. https://www.tc.columbia.edu/cice/pdf/25691_4_2_Crossley.pdf

Esser, F., & Vliegenthart, R. (2017). Comparative research methods. The International Encyclopedia of Communication Research Methods, 1 , 1–22. https://doi.org/10.1002/9781118901731.iecrm0035

Article   Google Scholar  

Henry, I. (Ed.). (2007). Transnational and comparative research in sport globalisation, governance and sport policy. Routledge. https://doi-org.cyber.usask.ca/ https://doi.org/10.4324/9780203944738

Mills, M., Bunt, G., & Bruijn, J. (2006). Comparative research: Persistent problems and promising solutions. International Sociology, 21 (5), 619–631. https://doi.org/10.1177/0268580906067833

Nóvoa, A., & Yariv-Mashal, T. (2003). Comparative research in education: A mode of governance or a historical journey? Comparative Education, 39 (4), 423–438. https://repositorio.ul.pt/bitstream/10451/680/1/21185_0305-0068_423-438.pdf

Peters, G. (2013). Strategies for comparative research in political science . Macmillan.

Book   Google Scholar  

Pickvance, C. (2005). The four varieties of comparative analysis: The case of environmental regulation. Journal of Housing and the Built Environment, 16 , 7–28.

Rogers, M. (2014). Comparative effectiveness research .

Rokkan, S. (1968). The structuring of mass politics in the smaller European democracies: A developmental typology. Comparative Studies in Society and History, 10 (2), 173–210. https://www.jstor.org/stable/177728

Tilly, C. (1984). Big structures, large processes, huge comparisons . SAGE.

Wang, G., & Huang, Y. (2016). Contextuality, commensurability, and comparability in comparative research: Learning from Chinese relationship research. Cross-Cultural Research, 50 (2), 154–177. https://doi.org/10.1177/1069397116630241

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Comparing and Contrasting in an Essay | Tips & Examples

Published on August 6, 2020 by Jack Caulfield . Revised on July 23, 2023.

Comparing and contrasting is an important skill in academic writing . It involves taking two or more subjects and analyzing the differences and similarities between them.

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When should i compare and contrast, making effective comparisons, comparing and contrasting as a brainstorming tool, structuring your comparisons, other interesting articles, frequently asked questions about comparing and contrasting.

Many assignments will invite you to make comparisons quite explicitly, as in these prompts.

  • Compare the treatment of the theme of beauty in the poetry of William Wordsworth and John Keats.
  • Compare and contrast in-class and distance learning. What are the advantages and disadvantages of each approach?

Some other prompts may not directly ask you to compare and contrast, but present you with a topic where comparing and contrasting could be a good approach.

One way to approach this essay might be to contrast the situation before the Great Depression with the situation during it, to highlight how large a difference it made.

Comparing and contrasting is also used in all kinds of academic contexts where it’s not explicitly prompted. For example, a literature review involves comparing and contrasting different studies on your topic, and an argumentative essay may involve weighing up the pros and cons of different arguments.

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As the name suggests, comparing and contrasting is about identifying both similarities and differences. You might focus on contrasting quite different subjects or comparing subjects with a lot in common—but there must be some grounds for comparison in the first place.

For example, you might contrast French society before and after the French Revolution; you’d likely find many differences, but there would be a valid basis for comparison. However, if you contrasted pre-revolutionary France with Han-dynasty China, your reader might wonder why you chose to compare these two societies.

This is why it’s important to clarify the point of your comparisons by writing a focused thesis statement . Every element of an essay should serve your central argument in some way. Consider what you’re trying to accomplish with any comparisons you make, and be sure to make this clear to the reader.

Comparing and contrasting can be a useful tool to help organize your thoughts before you begin writing any type of academic text. You might use it to compare different theories and approaches you’ve encountered in your preliminary research, for example.

Let’s say your research involves the competing psychological approaches of behaviorism and cognitive psychology. You might make a table to summarize the key differences between them.

Or say you’re writing about the major global conflicts of the twentieth century. You might visualize the key similarities and differences in a Venn diagram.

A Venn diagram showing the similarities and differences between World War I, World War II, and the Cold War.

These visualizations wouldn’t make it into your actual writing, so they don’t have to be very formal in terms of phrasing or presentation. The point of comparing and contrasting at this stage is to help you organize and shape your ideas to aid you in structuring your arguments.

When comparing and contrasting in an essay, there are two main ways to structure your comparisons: the alternating method and the block method.

The alternating method

In the alternating method, you structure your text according to what aspect you’re comparing. You cover both your subjects side by side in terms of a specific point of comparison. Your text is structured like this:

Mouse over the example paragraph below to see how this approach works.

One challenge teachers face is identifying and assisting students who are struggling without disrupting the rest of the class. In a traditional classroom environment, the teacher can easily identify when a student is struggling based on their demeanor in class or simply by regularly checking on students during exercises. They can then offer assistance quietly during the exercise or discuss it further after class. Meanwhile, in a Zoom-based class, the lack of physical presence makes it more difficult to pay attention to individual students’ responses and notice frustrations, and there is less flexibility to speak with students privately to offer assistance. In this case, therefore, the traditional classroom environment holds the advantage, although it appears likely that aiding students in a virtual classroom environment will become easier as the technology, and teachers’ familiarity with it, improves.

The block method

In the block method, you cover each of the overall subjects you’re comparing in a block. You say everything you have to say about your first subject, then discuss your second subject, making comparisons and contrasts back to the things you’ve already said about the first. Your text is structured like this:

  • Point of comparison A
  • Point of comparison B

The most commonly cited advantage of distance learning is the flexibility and accessibility it offers. Rather than being required to travel to a specific location every week (and to live near enough to feasibly do so), students can participate from anywhere with an internet connection. This allows not only for a wider geographical spread of students but for the possibility of studying while travelling. However, distance learning presents its own accessibility challenges; not all students have a stable internet connection and a computer or other device with which to participate in online classes, and less technologically literate students and teachers may struggle with the technical aspects of class participation. Furthermore, discomfort and distractions can hinder an individual student’s ability to engage with the class from home, creating divergent learning experiences for different students. Distance learning, then, seems to improve accessibility in some ways while representing a step backwards in others.

Note that these two methods can be combined; these two example paragraphs could both be part of the same essay, but it’s wise to use an essay outline to plan out which approach you’re taking in each paragraph.

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Some essay prompts include the keywords “compare” and/or “contrast.” In these cases, an essay structured around comparing and contrasting is the appropriate response.

Comparing and contrasting is also a useful approach in all kinds of academic writing : You might compare different studies in a literature review , weigh up different arguments in an argumentative essay , or consider different theoretical approaches in a theoretical framework .

Your subjects might be very different or quite similar, but it’s important that there be meaningful grounds for comparison . You can probably describe many differences between a cat and a bicycle, but there isn’t really any connection between them to justify the comparison.

You’ll have to write a thesis statement explaining the central point you want to make in your essay , so be sure to know in advance what connects your subjects and makes them worth comparing.

Comparisons in essays are generally structured in one of two ways:

  • The alternating method, where you compare your subjects side by side according to one specific aspect at a time.
  • The block method, where you cover each subject separately in its entirety.

It’s also possible to combine both methods, for example by writing a full paragraph on each of your topics and then a final paragraph contrasting the two according to a specific metric.

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How to Do Comparative Analysis in Research ( Examples )

Comparative analysis is a method that is widely used in social science . It is a method of comparing two or more items with an idea of uncovering and discovering new ideas about them. It often compares and contrasts social structures and processes around the world to grasp general patterns. Comparative analysis tries to understand the study and explain every element of data that comparing. 

Comparative Analysis in Social SCIENCE RESEARCH

We often compare and contrast in our daily life. So it is usual to compare and contrast the culture and human society. We often heard that ‘our culture is quite good than theirs’ or ‘their lifestyle is better than us’. In social science, the social scientist compares primitive, barbarian, civilized, and modern societies. They use this to understand and discover the evolutionary changes that happen to society and its people.  It is not only used to understand the evolutionary processes but also to identify the differences, changes, and connections between societies.

Most social scientists are involved in comparative analysis. Macfarlane has thought that “On account of history, the examinations are typically on schedule, in that of other sociologies, transcendently in space. The historian always takes their society and compares it with the past society, and analyzes how far they differ from each other.

The comparative method of social research is a product of 19 th -century sociology and social anthropology. Sociologists like Emile Durkheim, Herbert Spencer Max Weber used comparative analysis in their works. For example, Max Weber compares the protestant of Europe with Catholics and also compared it with other religions like Islam, Hinduism, and Confucianism.

To do a systematic comparison we need to follow different elements of the method.

1. Methods of comparison The comparison method

In social science, we can do comparisons in different ways. It is merely different based on the topic, the field of study. Like Emile Durkheim compare societies as organic solidarity and mechanical solidarity. The famous sociologist Emile Durkheim provides us with three different approaches to the comparative method. Which are;

  • The first approach is to identify and select one particular society in a fixed period. And by doing that, we can identify and determine the relationship, connections and differences exist in that particular society alone. We can find their religious practices, traditions, law, norms etc.
  •  The second approach is to consider and draw various societies which have common or similar characteristics that may vary in some ways. It may be we can select societies at a specific period, or we can select societies in the different periods which have common characteristics but vary in some ways. For example, we can take European and American societies (which are universally similar characteristics) in the 20 th century. And we can compare and contrast their society in terms of law, custom, tradition, etc. 
  • The third approach he envisaged is to take different societies of different times that may share some similar characteristics or maybe show revolutionary changes. For example, we can compare modern and primitive societies which show us revolutionary social changes.

2 . The unit of comparison

We cannot compare every aspect of society. As we know there are so many things that we cannot compare. The very success of the compare method is the unit or the element that we select to compare. We are only able to compare things that have some attributes in common. For example, we can compare the existing family system in America with the existing family system in Europe. But we are not able to compare the food habits in china with the divorce rate in America. It is not possible. So, the next thing you to remember is to consider the unit of comparison. You have to select it with utmost care.

3. The motive of comparison

As another method of study, a comparative analysis is one among them for the social scientist. The researcher or the person who does the comparative method must know for what grounds they taking the comparative method. They have to consider the strength, limitations, weaknesses, etc. He must have to know how to do the analysis.

Steps of the comparative method

1. Setting up of a unit of comparison

As mentioned earlier, the first step is to consider and determine the unit of comparison for your study. You must consider all the dimensions of your unit. This is where you put the two things you need to compare and to properly analyze and compare it. It is not an easy step, we have to systematically and scientifically do this with proper methods and techniques. You have to build your objectives, variables and make some assumptions or ask yourself about what you need to study or make a hypothesis for your analysis.

The best casings of reference are built from explicit sources instead of your musings or perceptions. To do that you can select some attributes in the society like marriage, law, customs, norms, etc. by doing this you can easily compare and contrast the two societies that you selected for your study. You can set some questions like, is the marriage practices of Catholics are different from Protestants? Did men and women get an equal voice in their mate choice? You can set as many questions that you wanted. Because that will explore the truth about that particular topic. A comparative analysis must have these attributes to study. A social scientist who wishes to compare must develop those research questions that pop up in your mind. A study without those is not going to be a fruitful one.

2. Grounds of comparison

The grounds of comparison should be understandable for the reader. You must acknowledge why you selected these units for your comparison. For example, it is quite natural that a person who asks why you choose this what about another one? What is the reason behind choosing this particular society? If a social scientist chooses primitive Asian society and primitive Australian society for comparison, he must acknowledge the grounds of comparison to the readers. The comparison of your work must be self-explanatory without any complications.

If you choose two particular societies for your comparative analysis you must convey to the reader what are you intended to choose this and the reason for choosing that society in your analysis.

3 . Report or thesis

The main element of the comparative analysis is the thesis or the report. The report is the most important one that it must contain all your frame of reference. It must include all your research questions, objectives of your topic, the characteristics of your two units of comparison, variables in your study, and last but not least the finding and conclusion must be written down. The findings must be self-explanatory because the reader must understand to what extent did they connect and what are their differences. For example, in Emile Durkheim’s Theory of Division of Labour, he classified organic solidarity and Mechanical solidarity . In which he means primitive society as Mechanical solidarity and modern society as Organic Solidarity. Like that you have to mention what are your findings in the thesis.

4. Relationship and linking one to another

Your paper must link each point in the argument. Without that the reader does not understand the logical and rational advance in your analysis. In a comparative analysis, you need to compare the ‘x’ and ‘y’ in your paper. (x and y mean the two-unit or things in your comparison). To do that you can use likewise, similarly, on the contrary, etc. For example, if we do a comparison between primitive society and modern society we can say that; ‘in the primitive society the division of labour is based on gender and age on the contrary (or the other hand), in modern society, the division of labour is based on skill and knowledge of a person.

Demerits of comparison

Comparative analysis is not always successful. It has some limitations. The broad utilization of comparative analysis can undoubtedly cause the feeling that this technique is a solidly settled, smooth, and unproblematic method of investigation, which because of its undeniable intelligent status can produce dependable information once some specialized preconditions are met acceptably.

Perhaps the most fundamental issue here respects the independence of the unit picked for comparison. As different types of substances are gotten to be analyzed, there is frequently a fundamental and implicit supposition about their independence and a quiet propensity to disregard the mutual influences and common impacts among the units.

One more basic issue with broad ramifications concerns the decision of the units being analyzed. The primary concern is that a long way from being a guiltless as well as basic assignment, the decision of comparison units is a basic and precarious issue. The issue with this sort of comparison is that in such investigations the depictions of the cases picked for examination with the principle one will in general turn out to be unreasonably streamlined, shallow, and stylised with contorted contentions and ends as entailment.

However, a comparative analysis is as yet a strategy with exceptional benefits, essentially due to its capacity to cause us to perceive the restriction of our psyche and check against the weaknesses and hurtful results of localism and provincialism. We may anyway have something to gain from history specialists’ faltering in utilizing comparison and from their regard for the uniqueness of settings and accounts of people groups. All of the above, by doing the comparison we discover the truths the underlying and undiscovered connection, differences that exist in society.

Also Read: How to write a Sociology Analysis? Explained with Examples

a comparative research paper

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Comparative Research

Comparative Research Examples 1

Although not everyone would agree, comparing is not always bad. Comparing things can also give you a handful of benefits. For instance, there are times in our life where we feel lost. You may not be getting the job that you want or have the sexy body that you have been aiming for a long time now. Then, you happen to cross path with an old friend of yours, who happened to get the job that you always wanted. This scenario may put your self-esteem down, knowing that this friend got what you want, while you didn’t. Or you can choose to look at your friend as an example that your desire is actually attainable. Come up with a plan to achieve your  personal development goal . Perhaps, ask for tips from this person or from the people who inspire you. According to the article posted in  brit.co , licensed master social worker and therapist Kimberly Hershenson said that comparing yourself to someone successful can be an excellent self-motivation to work on your goals.

Aside from self-improvement, as a researcher, you should know that comparison is an essential method in scientific studies, such as experimental research and descriptive research . Through this method, you can uncover the relationship between two or more variables of your project in the form of comparative analysis .

What is Comparative Research?

Aiming to compare two or more variables of an experiment project, experts usually apply comparative research examples in social sciences to compare countries and cultures across a particular area or the entire world. Despite its proven effectiveness, you should keep it in mind that some states have different disciplines in sharing data. Thus, it would help if you consider the affecting factors in gathering specific information.

Quantitative and Qualitative Research Methods in Comparative Studies

In comparing variables, the statistical and mathematical data collection, and analysis that quantitative research methodology naturally uses to uncover the correlational connection of the variables, can be essential. Additionally, since quantitative research requires a specific research question, this method can help you can quickly come up with one particular comparative research question.

The goal of comparative research is drawing a solution out of the similarities and differences between the focused variables. Through non-experimental or qualitative research , you can include this type of research method in your comparative research design.

13+ Comparative Research Examples

Know more about comparative research by going over the following examples. You can download these zipped documents in PDF and MS Word formats.

1. Comparative Research Report Template

comparative research report template

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2. Business Comparative Research Template

business comparative research template

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3. Comparative Market Research Template

comparative market research template

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4. Comparative Research Strategies Example

comparative research strategies example

5. Comparative Research in Anthropology Example

comparative research in anthropology example

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6. Sample Comparative Research Example

sample comparative research example

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7. Comparative Area Research Example

comparative area research example

8. Comparative Research on Women’s Emplyment Example

comparative research on womens emplyment

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9. Basic Comparative Research Example

basic comparative research example

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10. Comparative Research in Medical Treatments Example

comparative research in medical treatments

11. Comparative Research in Education Example

comparative research in education

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12. Formal Comparative Research Example

formal comparative research example

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13. Comparative Research Designs Example

comparing comparative research designs

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14. Casual Comparative Research in DOC

caasual comparative research in doc

Best Practices in Writing an Essay for Comparative Research in Visual Arts

If you are going to write an essay for a comparative research examples paper, this section is for you. You must know that there are inevitable mistakes that students do in essay writing . To avoid those mistakes, follow the following pointers.

1. Compare the Artworks Not the Artists

One of the mistakes that students do when writing a comparative essay is comparing the artists instead of artworks. Unless your instructor asked you to write a biographical essay, focus your writing on the works of the artists that you choose.

2. Consult to Your Instructor

There is broad coverage of information that you can find on the internet for your project. Some students, however, prefer choosing the images randomly. In doing so, you may not create a successful comparative study. Therefore, we recommend you to discuss your selections with your teacher.

3. Avoid Redundancy

It is common for the students to repeat the ideas that they have listed in the comparison part. Keep it in mind that the spaces for this activity have limitations. Thus, it is crucial to reserve each space for more thoroughly debated ideas.

4. Be Minimal

Unless instructed, it would be practical if you only include a few items(artworks). In this way, you can focus on developing well-argued information for your study.

5. Master the Assessment Method and the Goals of the Project

We get it. You are doing this project because your instructor told you so. However, you can make your study more valuable by understanding the goals of doing the project. Know how you can apply this new learning. You should also know the criteria that your teachers use to assess your output. It will give you a chance to maximize the grade that you can get from this project.

Comparing things is one way to know what to improve in various aspects. Whether you are aiming to attain a personal goal or attempting to find a solution to a certain task, you can accomplish it by knowing how to conduct a comparative study. Use this content as a tool to expand your knowledge about this research methodology .

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Human Relations Area Files

Cultural information for education and research, teaching a comparative approach with ehraf research papers.

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The Comparative Approach in Anthropology

In a blog titled Where Have All the Comparisons Gone? , originally published on the website for the Society For Cultural Anthropology, Robert Borofsky from the Center For A Public Anthropology at Hawaii Pacific University writes:

Comparison is basic to anthropology. It frames an understanding of ourselves and others…Comparison, I would suggest, draws the attention of others beyond the discipline. It encourages public appreciation of cultural anthropology’s intellectual power—how it broadens our collective understanding of the world around us, above and beyond the insights of individual cases (Borofsky 2019).

HRAF recognizes the intellectual importance of anthropology and its potential to make substantive contributions to fostering cross-cultural understanding in the age of globalization. Our mission is to promote understanding of cultural diversity and commonality in the past and present. Many of the key points made in “Where Have All the Comparisons Gone?” are echoed by our open access resource,  Explaining Human Culture :

The vast anthropological record of human societies and cultures allows us to ask cross-cultural questions about human universals and differences. What cultural and societal features are universal? What features vary? And how can we explain these patterns? These are the fundamental questions asked by cross-cultural researchers (Ember 2016).

Peoples and Cultures of the World

Matthew Longcore , HRAF’s member services manager who also teaches anthropology and archaeology at the University of Connecticut, takes a comparative approach in his introductory courses and regularly incorporates the eHRAF databases into his teaching. In the Spring 2022 semester, Professor Longcore taught ANTH 1000W Peoples and Cultures of the World , an introductory writing-intensive course in cultural anthropology. The course is considered a first year, or freshman level, course with college-level English as a prerequisite. Cross-cultural comparisons help students understand the differences and similarities among extant societies, as well as providing a perspective for evaluating one’s own society and place within it. Here is the course description:

An introduction to the anthropological understanding of human society through ethnographic case studies of selected peoples and cultures, exploring the richness and variety of human life. Encourages students to learn about different cultures and to apply their knowledge to make sense of their own society (UConn Catalog 2022).

To meet the writing requirement for the course, students are expected to submit a research paper using eHRAF World Cultures . Here is the research paper assignment in Teaching eHRAF. The paper should address a specific cultural behavior or topic of their choice (e.g. romantic kissing) and a related research question (e.g. is romantic kissing a cultural universal?). Students use data from eHRAF World Cultures to answer the question and must select any three cultures to research their topic. The assignment provides students with sample topics, tips for selecting a research question, the step-by-step process for the development of the research paper, and guidelines for writing the paper. The completed assignment should comprise a minimum of 9 pages, double-spaced, including a title page, and references cited.

The assigned textbook for ANTH 1000W is  Cultural Anthropology: An Applied Perspective  by Ferraro and Andreatta. This textbook is ideal for preparing students to explore eHRAF as it has several references to the Human Relations Area Files, George Peter Murdock, and cultural universals. To help students get started with selecting a topic, Professor Longcore presented the concept of cultural universals and reviewed a list from Murdock with the class. Longcore explained to his students that cultural universals are those general human traits found in all societies of the world. By examining the cultures of the world in a comparative way, anthropologists can begin to discover similarities and differences between cultures and can identify common denominators.

Based on feedback received early in the course, students appreciated having the opportunity to select a topic for their research based on their own interests, or to develop a new theme on a facet of culture that they discovered independently while searching in eHRAF. Several research papers focused on topics related to marriage, including subtopics such as arranged marriages, polygyny and polyandry, exogamy and endogamy, patrilineal and matrilineal descent, and premarital sex. Students also developed research papers on some new topics including the concepts of machismo and marianismo in Latin American societies, the use of jewelry across cultures, recreational and non-therapeutic drugs, tattooing, and intimacy across cultures.

At the beginning of the semester, Professor Longcore presented a webinar on how to effectively use eHRAF World Cultures for the research paper assignment. During the webinar, students were able to ask questions about how to navigate the databases in order to find relevant information to complete their assignments. Of particular interest were recommendations for saving information and citing sources.

HRAF highly recommends that faculty who are teaching with eHRAF schedule a  webinar for their classes. For those instructors assigning an eHRAF research paper, it is also particularly helpful to share posts from the HRAF homepage with your students as they are accessible examples of research featuring popular anthropological topics which appeal to students. Additionally, all of the ethnographic  sources included in the posts have been properly cited with links to the databases.

At the conclusion of the course, Professor Longcore created a presentation which features the work of each of the students including their research topics and direct quotes from their papers. Students were glad to have the opportunity to share their research topics with their classmates and to discuss the process of developing their papers. The students thoroughly enjoyed the journey of cultural discovery that they had undergone in order to produce their papers, and were amazed that they were able to find such diversity of ethnographic information in one place.

UConn-Yale

Teaching with eHRAF

Reflecting on his experience teaching with the eHRAF databases, Professor Longcore shared the following:

I found that eHRAF World Cultures is ideally suited to teaching introductory courses in cultural anthropology. My main objective is to get students to think comparatively and cross-culturally. Students are encouraged to take an aerial view of the world – past and present – and to see the interconnectedness of cultures in terms of similarities and differences. With the click of a button, students are able to make startling discoveries through a repository of trusted ethnographic resources.

Students from both courses were excited to learn about anthropology and found the subject to be engaging well beyond their expectations. While many of the students enrolled in the courses simply for the purpose of satisfying distribution requirements, they emerged with a deep passion for anthropology, seeing its relevance to their own lives and career aspirations. Some students expressed interest in taking more advanced courses in anthropology and in joining UConn Stamford Anthropology Society , which is advised by Professor Longcore.

We are pleased that eHRAF World Cultures has played such a significant role in attracting a new generation of anthropologists with the help of dedicated instructors like Professor Longcore.

Contribute & Collaborate

Teaching eHRAF is made possible with the help and generosity of educators who use eHRAF in their classrooms. Instructors are invited to contact HRAF with innovative and practical teaching ideas. We encourage educators to collaborate with us and to share any eHRAF-themed teaching materials that they have produced. If you would like to contribute to Teaching eHRAF, please contact Dr. Francine Barone at  [email protected] .

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The Comparative Essay

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What is a comparative essay?

A comparative essay asks that you compare at least two (possibly more) items. These items will differ depending on the assignment. You might be asked to compare

  • positions on an issue (e.g., responses to midwifery in Canada and the United States)
  • theories (e.g., capitalism and communism)
  • figures (e.g., GDP in the United States and Britain)
  • texts (e.g., Shakespeare’s Hamlet and Macbeth )
  • events (e.g., the Great Depression and the global financial crisis of 2008–9)

Although the assignment may say “compare,” the assumption is that you will consider both the similarities and differences; in other words, you will compare and contrast.

Make sure you know the basis for comparison

The assignment sheet may say exactly what you need to compare, or it may ask you to come up with a basis for comparison yourself.

  • Provided by the essay question: The essay question may ask that you consider the figure of the gentleman in Charles Dickens’s Great Expectations and Anne Brontë’s The Tenant of Wildfell Hall . The basis for comparison will be the figure of the gentleman.
  • Developed by you: The question may simply ask that you compare the two novels. If so, you will need to develop a basis for comparison, that is, a theme, concern, or device common to both works from which you can draw similarities and differences.

Develop a list of similarities and differences

Once you know your basis for comparison, think critically about the similarities and differences between the items you are comparing, and compile a list of them.

For example, you might decide that in Great Expectations , being a true gentleman is not a matter of manners or position but morality, whereas in The Tenant of Wildfell Hall , being a true gentleman is not about luxury and self-indulgence but hard work and productivity.

The list you have generated is not yet your outline for the essay, but it should provide you with enough similarities and differences to construct an initial plan.

Develop a thesis based on the relative weight of similarities and differences

Once you have listed similarities and differences, decide whether the similarities on the whole outweigh the differences or vice versa. Create a thesis statement that reflects their relative weights. A more complex thesis will usually include both similarities and differences. Here are examples of the two main cases:

While Callaghan’s “All the Years of Her Life” and Mistry’s “Of White Hairs and Cricket” both follow the conventions of the coming-of-age narrative, Callaghan’s story adheres more closely to these conventions by allowing its central protagonist to mature. In Mistry’s story, by contrast, no real growth occurs.
Although Darwin and Lamarck came to different conclusions about whether acquired traits can be inherited, they shared the key distinction of recognizing that species evolve over time.

Come up with a structure for your essay

Note that the French and Russian revolutions (A and B) may be dissimilar rather than similar in the way they affected innovation in any of the three areas of technology, military strategy, and administration. To use the alternating method, you just need to have something noteworthy to say about both A and B in each area. Finally, you may certainly include more than three pairs of alternating points: allow the subject matter to determine the number of points you choose to develop in the body of your essay.

When do I use the block method? The block method is particularly useful in the following cases:

  • You are unable to find points about A and B that are closely related to each other.
  • Your ideas about B build upon or extend your ideas about A.
  • You are comparing three or more subjects as opposed to the traditional two.

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  • Open access
  • Published: 11 November 2020

A comparative genomics multitool for scientific discovery and conservation

Zoonomia consortium.

Nature volume  587 ,  pages 240–245 ( 2020 ) Cite this article

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  • Comparative genomics
  • Ecological genetics
  • Evolutionary genetics
  • Genome informatics
  • Phylogenetics

The Zoonomia Project is investigating the genomics of shared and specialized traits in eutherian mammals. Here we provide genome assemblies for 131 species, of which all but 9 are previously uncharacterized, and describe a whole-genome alignment of 240 species of considerable phylogenetic diversity, comprising representatives from more than 80% of mammalian families. We find that regions of reduced genetic diversity are more abundant in species at a high risk of extinction, discern signals of evolutionary selection at high resolution and provide insights from individual reference genomes. By prioritizing phylogenetic diversity and making data available quickly and without restriction, the Zoonomia Project aims to support biological discovery, medical research and the conservation of biodiversity.

The genomics revolution is enabling advances not only in medical research 1 , but also in basic biology 2 and in the conservation of biodiversity, where genomic tools have helped to apprehend poachers 3 and to protect endangered populations 4 . However, we have only a limited ability to predict which genomic variants lead to changes in organism-level phenotypes, such as increased disease risk—a task that, in humans, is complicated by the sheer size of the genome (about three billion nucleotides) 5 .

Comparative genomics can address this challenge by identifying nucleotide positions that have remained unchanged across millions of years of evolution 6 (suggesting that changes at these positions will negatively affect fitness), focusing the search for disease-causing variants. In 2011, the 29 Mammals Project 7 identified 12-base-pair (bp) regions of evolutionary constraint that in total comprise 4.2% of the genome, by measuring sequence conservation in humans plus 28 other mammals. These regions proved to be more enriched for the heritability of complex diseases than any other functional mark, including coding status 8 . By expanding the number of species and making an alignment that is independent of any single reference genome, the Zoonomia Project was designed to detect evolutionary constraint in the eutherian lineage at increased resolution, and to provide genomic resources for over 130 previously uncharacterized species.

Designing a comparative-genomics multitool

When selecting species, we sought to maximize evolutionary branch length, to include at least one species from each eutherian family, and to prioritize species of medical, biological or biodiversity conservation interest. Our assemblies increase the percentage of eutherian families with a representative genome from 49% to 82%, and include 9 species that are the sole extant member of their family and 7 species that are critically endangered 9 (Fig. 1 ): the Mexican howler monkey ( Alouatta palliata mexicana ), hirola ( Beatragus hunteri ), Russian saiga ( Saiga tatarica tatarica ), social tuco-tuco ( Ctenomys sociabilis ), indri ( Indri indri ), northern white rhinoceros ( Ceratotherium simum cottoni ) and black rhinoceros ( Diceros bicornis ).

figure 1

Phylogenetic tree of the mammalian families in the Zoonomia Project alignment, including both our new assemblies and all other high-quality mammalian genomes publicly available in GenBank when we started the alignment (March 2018) (Supplementary Table 2 ). Tree topology is based on data from TimeTree ( www.timetree.org) 47 . Existing taxonomic classifications recognize a total of 127 extant families of eutherian mammal 48 , including 43 families that were not previously represented in GenBank (red boxes) and 41 families with additional representative genome assemblies (pink boxes). Of the remaining families, 21 had GenBank genome assemblies but no Zoonomia Project assembly (grey boxes) and 22 had no representative genome assembly (white boxes). Parenthetical numbers indicate the number of species with genome assemblies in a given family. Image credits: fossa, Bertal/Wikimedia (CC BY-SA); Arctic fox, Michael Haferkamp/Wikimedia (CC BY-SA); hirola, JRProbert/Wikimedia (CC BY-SA); bumblebee bat, Sébastien J. Puechmaille (CC BY-SA); snowshoe hare, Denali National Park and Preserve/Wikimedia (public domain); aye-aye, Tom Junek/Wikimedia (CC BY-SA); Geoffroy’s spider monkey, Patrick Gijsbers/Wikimedia (CC BY-SA); southern three-banded armadillo, Hedwig Storch/Wikimedia (CC BY-SA); giant anteater, Graham Hughes/Wikimedia (CC BY-SA); brown-throated sloth, Dick Culbert from Gibsons, B.C., Canada/Wikimedia (CC BY).

We collaborated with 28 institutions to collect samples, nearly half (47%) of which were provided by The Frozen Zoo of San Diego Zoo Global (Supplementary Table 1 ). Since 1975, The Frozen Zoo has stored renewable cell cultures for about 10,000 vertebrate animals that represent over 1,100 taxa, including more than 200 species that are classified as vulnerable, endangered, critically endangered or extinct by the International Union for Conservation of Nature (IUCN) 10 . For 36 target species we were unable to acquire a DNA sample of sufficient quality, even though our requirements were modest (Methods), which highlights a major impediment to expanding the phylogenetic diversity of genomics.

We used two complementary approaches to generate genome assemblies (Extended Data Table 1 ). First, for 131 genomes we generated assemblies by performing a single lane of sequencing (2× 250-bp reads) on PCR-free libraries and assembling with DISCOVAR de novo 11 (referred to here as ‘DISCOVAR assemblies’). This method does not require intact cells and uses less than two micrograms of medium-quality DNA (most fragments are over 5 kilobases (kb) in size), which allowed us to include species that are difficult to access (Extended Data Figs. 1 , 2 ) while achieving ‘contiguous sequences constructed from overlapping short reads’ (contig) lengths comparable to those of existing assemblies (median contig N50 of 46.8 kb, compared to 47.9 kb for Refseq genome assemblies).

For nine DISCOVAR assemblies and one pre-existing assembly (the lesser hedgehog tenrec ( Echinops telfairi )), we increased contiguity 200-fold (the median scaffold length increased from 90.5 kb to 18.5 megabases (Mb)) through proximity ligation, which uses chromatin interaction data to capture the physical relationships among genomic regions 12 . Unlike short-contiguity genomes, these assemblies capture structural changes such as chromosomal rearrangements 13 . The upgraded assemblies increase the number of eutherian orders that are represented by a long-range assembly (contig N50 > 20 kb and scaffold N50 > 10 Mb) from 12 to 18 (out of 19). We are working on upgrading the assembly of the large treeshrew ( Tupaia tana ) for the remaining order (Scandentia).

Comparative power of 240 species

The Zoonomia alignment includes 120 newly generated assemblies and 121 existing assemblies, representing a total of 240 species (the dataset includes assemblies for two different dogs) and spanning about 110 million years of mammalian evolution (Supplementary Table 2 ). With a total evolutionary branch length of 16.6 substitutions per site, we expect only 191 positions in the human genome (0.000006%) to be identical across the aligned species owing to chance (false positives) rather than evolutionary constraint (Extended Data Table 2 ). We applied this same calculation to data from The Exome Aggregation Consortium (ExAC)—who analysed exomes for 60,706 humans 14 —and estimated that 88% of positions would be expected to have no variation. This illustrates the potential for relatively small cross-species datasets to inform human genetic studies—even for diseases driven by high-penetrance coding mutations, for which ExAC data are optimally powered 15 .

Biological insights from additional assemblies

The scope and species diversity in the Zoonomia Project supports evolutionary studies in many lineages. Previously published papers (discussed in the subsections below), and the demonstrated utility of existing comparative genomics resources 16 , 17 , illustrate the benefits of making newly generated genome assemblies and alignments accessible to all researchers without restrictions on use.

Comparing our assembly for the endangered Mexican howler monkey ( Alouatta palliata mexicana , a subspecies of the mantled howler monkey) with the Guatemalan black howler monkey ( Alouatta pigra )—which has a neighbouring range—suggests that different forms of selection shape the reproductive isolation of the two species 18 . Initial divergence in allopatry was followed by positive selection on postzygotic isolating mechanisms, which offers empirical support for a speciation process that was first outlined by Dobzhansky in 1935 19 .

Protection from cancer

Using our assembly for the capybara ( Hydrochoerus hydrochaeris ) (a giant rodent), a previous publication 20 has identified positive selection on anti-cancer pathways, echoing previous reports 21 that other large mammal species—the African and Asian elephants ( Loxodonta africana and Elephas maximus indicus , respectively) —carry extra copies (retrogenes) of the tumour-suppressor gene TP53 . This offers a possible resolution to Peto’s paradox—the observation that cancer in large mammals is rarer than expected—and could reveal anti-cancer mechanisms.

Convergent evolution of venom

A previous publication 22 has used our assembly for the Hispaniolan solenodon ( Solenodon paradoxus ) (Extended Data Fig. 2 ) to investigate venom production—a trait that is found in only a few eutherian lineages, including shrews and solenodons. They identified paralogous copies of a kallikrein 1 serine protease gene ( KLK1 ) that together encode solenodon venom, and showed that the KLK1 gene was independently co-opted for venom production in both solenodons and shrews, in an example of molecular convergence.

Informing biodiversity conservation strategies

A previous analysis 23 of our giant otter ( Pteronura brasiliensis ) assembly found low diversity and an elevated burden of putatively deleterious genetic variants, consistent with the recent population decline of this species through overhunting and habitat loss. The giant otter had fewer putatively deleterious variants than either the southern or northern sea otter ( Enhydra lutris nereis and E. lutris kenyoni , respectively), which suggests that it has highest potential for recovery among these species if populations are protected.

Rapid assessment of species infection risk

Using the Zoonomia alignment and public genomic data from hundreds of other vertebrates, a previous publication 24 compared the structure of ACE2—the receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19)—and identified 47 mammals that have a high or very high likelihood of being virus reservoirs, intermediate hosts or good model organisms for the study of COVID-19, and detected positive selection in the ACE2 receptor-binding domain that is specific to bats.

Genetic diversity and extinction risk

We next asked whether a reference genome from a single individual can help to identify populations with low genetic diversity to prioritize in efforts to conserve biodiversity. Diversity metrics reflect demographic history 25 , 26 , and heterozygosity is lower in threatened species 27 . This analysis was feasible because we used a single sequencing and assembly protocol for all DISCOVAR assemblies, which minimized variation in accuracy, completeness and contiguity due to the sequencing technology and the assembly process that would otherwise confound species comparisons.

We estimated genetic diversity for 130 of our DISCOVAR assemblies, each of which represented a different species (Supplementary Table 3 ). Four of these estimates failed during analysis. For the remaining 126 DISCOVAR assemblies, we calculated 2 metrics: (1) the fraction of sites at which the sequenced individual is heterozygous (overall heterozygosity); and (2) the proportion of the genome that resides in an extended region without any variation (segments of homozygosity (SoH)). The SoH measurement is designed for short-contiguity assemblies, in which scaffolds are potentially shorter than runs of homozygosity. Overall, heterozygosity and SoH values are correlated (Pearson correlation r  = −0.56, P  = 1.8 × 10 −9 , n  = 98). Although overall heterozygosity is correlated with contig N50 values (Pearson correlation r het  = −0.39, P het  = 4 × 10 −5 , n het  = 105) (probably owing to the difficulty of assembling more heterozygous genomes 28 ), SoH values are not (Pearson correlation r SoH  = 0.09, P SoH  = 0.38, n SoH  = 98). Overall heterozygosity and SoH values are highly correlated between the lower- and high-contiguity versions of the upgraded assemblies (Pearson correlation r het  = 0.999, P het  = 5 × 10 −7 , n het  = 7; r SoH  = 0.996, P SoH  = 1.4 × 10 −6 , n SoH  = 7).

Genomic diversity varies significantly among species in different IUCN conservation categories, as measured by overall heterozygosity (Fig. 2a ) and SoH values (Fig. 2b ). SoH values increase ( P  = 0.024, R 2  = 0.055, n  = 94) with increasing levels of conservation concern, whereas heterozygosity decreases ( P  = 0.011, R 2  = 0.064, n  = 101). There is no significant difference between wild and captive populations in overall heterozygosity (Fig. 2c ) or SoH values (Fig. 2d ).

figure 2

a , b , Heterozygosity declines ( a ) and SoH value increases ( b ) with the level of concern for species conservation, as assessed by IUCN conservation categories. Horizontal grey lines indicate median. c , d , Comparing individuals sampled from wild and captive populations, we saw no statistically significant difference (independent samples t -test) in overall heterozygosity ( c ) or per cent SoH ( d ), with similar means (horizontal grey lines) between types of birth population. In a – d , there was a total of 105 species, with n for each tested category indicated on the x axis. Statistical tests were two-sided. LC, least concern. e , Overall heterozygosity and SoH values for all genomes analysed (including those with high allelic balance ratio; n  = 124 species), with median SoH (17.1%, horizontal dashed line) and median overall heterozygosity (0.0026, vertical dashed line) for species categorized as least concern. Values for individuals from the seven critically endangered species are shown in red.

Source data

Unusual diversity values can suggest particular population demographics, although data from more than a single individual are needed to confirm these inferences. All seven critically endangered species have SoH values that are higher than the median for species categorized as of least concern (Fig. 2e ). The genomes with the lowest heterozygosity and highest SoH values were the social tuco-tuco (heterozygosity = 0.00063 and SoH = 78.7%), which was sampled from a small laboratory colony with only 12 founders 29 , and the eastern mole ( Scalopus aquaticus ) (heterozygosity = 0.0008 and SoH = 81.3%), which was supplied by a professional mole catcher and was probably from a population that had experienced a bottleneck owing to pest control measures.

The correlation between diversity metrics and IUCN category is not explained by other species-level phenotypes. For species of least concern ( n  = 75), we assessed 21 phenotypes that are catalogued in the PanTHERIA 30 database for correlation with heterozygosity or SoH values. The most significant was between SoH value and litter size, a trait that has previously been shown to predict extinction risk 31 ( P SoH  = 0.02), but none is significant after Bonferroni correction (Extended Data Table 3 ).

Our inference that diversity trends lower in species at a higher risk of extinction comes from a small fraction (2.6%) of threatened mammals 9 . Whether this is a direct correlation with extinction risk or arises from an association between diversity and species-level phenotypes such as litter size, it suggests that valuable information can be gleaned from sequencing only a single individual. Should this pattern prove robust across more species, diversity metrics from a single reference genome could help to identify populations that are at risk—even when few species-level phenotypes are documented—and to prioritize species for follow-up at the population level.

Resources for biodiversity conservation

For each genome assembly, we catalogued all high-confidence variant sites ( http://broad.io/variants ) to support the design of cost-effective and accurate genetic assays that are usable even when the sample quality is low 32 ; such assays are often preferable to designing expensive custom tools, relying on tools from related species or sequencing random regions 33 . The reference genomes themselves support the development of technologies such as using gene drives to control invasive species or pursuing ‘de-extinction’ through cloning and genetic engineering 34 .

Our genomes have two notable limitations. We sequenced only a single individual for each species, which is insufficient for studying population origins, population structure and recent demographic events 35 , 36 , and the shorter contiguity of our assemblies prevented us from analysing runs of homozygosity 26 . This highlights a dilemma that faces all large-scale genomics initiatives: determining when the value of sequencing additional individuals exceeds the value of improving the reference genome itself.

Whole-genome alignment

We aligned the genomes of 240 species (our assemblies and other mammalian genomes that were released when we started the alignment) as part of a 600-way pan-amniote alignment using the Cactus alignment software 37 (Supplementary Table 2 ). Rather than aligning to a single anchor genome, Cactus infers an ancestral genome for each pair of assemblies (Fig. 3a ). Consistent with our predictions, we have increased power to detect sequence constraint at individual bases relative to previous studies 7 , 38 . We detect 3.1% of bases in the human genome to be under purifying selection in the eutherian lineage (false-discovery rate (FDR) < 5%), without using windowing or other means to integrate contextual information across neighbouring bases. This is more than double the number from the largest previous 100-vertebrate alignment 38 (Fig. 3b ), with improvements being most notable in the non-coding sequence (Fig. 3c ) and in the increased resolution of individual features (Fig. 3d ). This represents a substantial proportion—but not all—of the 5 to 8% of the human genome that has previously been suggested to be under purifying selection 7 , 39 .

figure 3

a , Cactus alignments are reference-genome-free, enabling the detection of sequence that is absent from human (red) or other clades (purple), lineage-specific innovations (orange and green) and eutherian-mammal-specific sequence (blue). b , We compared phyloP predictions of conserved positions for a widely used 100-vertebrate alignment ( n  = 100 vertebrate species) (grey) to the Zoonomia alignment ( n  = 240 eutherian species) (red). The cumulative portion of the genome expected to be covered by true- versus false-positive calls is shown, starting from the highest confidence calls (solid line) and proceeding to calls with lower confidence (dashed lines); the horizontal line indicates the point at which the confidence level drops below an expected FDR of 0.05 (two-sided). c , A higher proportion of functionally annotated bases are detected as highly conserved (FDR < 0.05) in the Zoonomia alignment (red) than the 100-vertebrate alignment (grey), most notably in non-coding regions. lncRNA, long non-coding RNA; UTR, untranslated region. d , At a putative androgen-receptor binding site, phyloP scores predict that seven bases are under purifying selection (FDR < 0.05, two-sided) in the Zoonomia alignment (dark red), peaking in positions with the most information content in the androgen receptor JASPAR 49 motif, compared to one (dark grey) in the 100-vertebrate alignment, with scores at FDR > 0.05 shown in light red (top) and light grey (bottom).

Using our alignment of 240 mammalian genomes, we are pursuing four key strategies of analysis. First, we aim to provide the largest eutherian phylogeny based on nuclear genomes by building a comprehensive phylogeny and time tree, including trees partitioned by functional annotations, mode of inheritance and long-term recombination rates. Second, we will produce a detailed map of evolutionary constraint, identifying highly conserved genomic regions, regions under accelerated evolution in particular lineages and changes that probably affect phenotype, leveraging functional data from ENCODE 40 , GTEx 41 and the Human Cell Atlas 42 . Third, we will use genotype–phenotype correlations to investigate patterns of constraint in regions associated with disease in humans, identify patterns of convergent adaptive evolution 2 and apply a forward genomics strategy to link functional elements to traits. Finally, we will explore the evolution of genome structure by mapping syntenic regions between genomes, identifying evolutionary breakpoints and characterizing the repeat landscape.

The Zoonomia Project has captured mammalian diversity at a high resolution, and is among the first of many projects that are underway to sequence, catalogue and characterize whole branches of the eukaryotic biodiversity of the Earth. On the basis of our experience, we propose the following principles for realizing the full value of large-scale comparative genomics.

First, we should prioritize sample collection. We must support field researchers who collect samples and understand species ecology and behaviour, develop strategies for sample collection that do not rely on bulky laboratory equipment or cold chains, develop technology for using non-invasive types of sampling and establish more repositories of renewable cell cultures 10 .

Second, we need accessible and scalable tools for computational analysis. Few research groups have access to the computational resources necessary for work with massive genomic datasets. We must address the shortage of skilled computational scientists, and design software and data-storage systems that make powerful computational pipelines accessible to all researchers.

Finally, we should promote rapid data-sharing. Data embargoes must not be permitted to delay analyses that directly benefit the conservation of endangered species, human health or progress in basic science. Genomic data should be shared as quickly as possible and without restrictions on use.

Numerous large-scale genome-sequencing efforts are now underway, including the Earth BioGenome Project 43 , Genome 10K 44 , the Vertebrate Genomes Project, Bat 1K 45 , Bird 10K 46 and DNA Zoo. As the number of genomes grows, so too will the usefulness of comparative genomics in disease research and the development of therapeutic strategies. Preserving, rather than merely recording, the biodiversity of the Earth must be a priority. Through global scientific collaborations, and by making genomic resources available and accessible to all research communities, we can ensure that the legacy of genomics is not a digital archive of lost species.

The number of samples (species) required to detect evolutionary conservation at a single base was estimated by applying a Poisson model of the distribution of substitution counts in the genome.

Species selection, sample shipping and regulatory approvals

Species were selected to maximize branch length across the eutherian mammal phylogeny, and to capture genomes of species from previously unrepresented eutherian families. Of 172 species initially selected for inclusion, we obtained sufficiently high-quality DNA samples for genome sequencing for 137. DNA samples from collaborating institutions were shipped to the Broad Institute ( n  = 69) or Uppsala University ( n  = 68). For samples received at the Broad Institute that were then sent to Uppsala, shipping approval was secured from the US Fish and Wildlife Service. Institutional Animal Care and Use Committee approval was not required.

Sample quality control, library construction and sequencing

DNA integrity for each sample was visualized via agarose gel (at the Broad Institute) or Agilent tape station (at Uppsala University). Samples passed quality control if the bulk of DNA fragments were greater than 5 kb. DNA concentration was then determined using Invitrogen Qubit dsDNA HS assay kit. For each of the samples that passed quality control, 1–3 μg of DNA was fragmented on the Covaris E220 Instrument using the 400-bp standard programme (10% duty cycle, 140 PIP, 200 cycles per burst, 55 s). Fragmented samples underwent SPRI double-size selection (0.55×, 0.7 ×  f ) followed by PCR-free Illumina library construction following the manufacturer’s instructions (Kapa no. KK8232) using PCR-free adapters from Illumina (no. FC-121-3001). Final library fragment size distribution was determined on Agilent 2100 Bioanalyzer with High Sensitivity DNA Chips. Paired-end libraries were pooled, and then sequenced on a single lane of the Illumina HiSeq2500, set for Version 2 chemistry and 2×250-bp reads. This yielded a total of mean 375 million (s.d. = 125 million) reads per sample.

Assembly and validation

For each species, we applied DISCOVAR de novo 11 (discovardenovo-52488) (ftp://ftp.broadinstitute.org/pub/crd/DiscovarDeNovo/) to assemble the 2×250-bp read group, using the following command: DiscovarDeNovo READS = [READFILE] OUT_DIR = [SPECIES_ID]//[SPECIES_ID].discovar_files NUM_THREADS = 24 MAX_MEM_GB = 200G.

Coverage for each genome was automatically calculated by DISCOVAR, with a mean coverage of 40.1× (s.d.± 14×). We assessed genome assembly, gene set and transcriptome completeness using Benchmarking Universal Single-Copy Orthologs (BUSCO), which provides quantitative measures on the basis of gene content from near-universal single-copy orthologues 50 . BUSCO was run with default parameters, using the mammalian gene model set (mammalia_odb9, n  = 4,104), using the following command: python ./BUSCO.py -i [input fasta] -o [output_file] -l ./mammalia_odb9/ -m genome -c 1 -sp. human.

Median contig N50 for existing RefSeq assemblies was calculated using the assembly statistics for the most recent release of 118 eutherian mammals with RefSeq assembly accession numbers. Assemblies were all classified as either reference genome or representative genome. Assembly statistics were downloaded from the NCBI on 10 April 2019.

Genome upgrades

We selected genomes from each eutherian order without a pre-existing long-contiguity assembly on the basis of (1) whether the underlying assembly met the minimum quality threshold needed for HiRise upgrades; and (2) whether a second sample of sufficient quality could be obtained from that individual. All upgrades were done with Dovetail Chicago libraries and assembled with HiRise 2.1, as previously described 51 .

Estimating heterozygosity

Selection of assemblies for heterozygosity analysis.

Heterozygosity statistics were calculated for all but four of our short read assemblies ( n  = 126) as well as eight Dovetail-upgraded genomes. Four failed because they were either too fragmented to analyse ( n  = 3) or because of undetermined errors ( n  = 1). One assembly was excluded because it was a second individual from a species that was already represented.

Heterozygosity calls

We applied the standard GATK pipeline with genotype quality banding to identify the callable fraction of the genome 52 , 53 . First, we used samtools to subsample paired reads from the unmapped .bam files 54 . After removing adaptor sequences from the selected reads, we used BWA-MEM to map reads to the reference genome scaffolds of >10 kb, removing duplicates using the PicardTools MarkDuplicates utility 55 . We then called heterozygous sites using standard GATK-Haplotypecaller specifications, and with additional gVCF banding at 0, 10, 20, 30, 40, 50 and 99 qualities. We used the fraction of the genome with genotype quality >15 for subsequent analyses. For the lists of high-confidence variant sites, we include only heterozygous positions after filtering at GQ >20, maximum DP <100, minimum DP >6, as described in the README file at http://broad.io/variants .

Inferring overall heterozygosity

To avoid confounding by sex chromosomes or complex regions, we excluded all scaffolds with less than 0.5 or greater than 2× of the average sample read depth, then calculated global heterozygosity as the fraction of heterozygous calls over the whole callable genome.

Calling SoH

We estimated the proportion of the genome within SoH using a metric designed for genomes with scaffold N50 shorter than the expected maximum length of runs of homozygosity (our median scaffold N50 is 62 kb). We first split all scaffolds into windows with a maximum length of 50 kb, with windows ranging from 20 to 50 kb for scaffolds <50 kb. For each window, we calculated the average number of heterozygous sites per bp. We discriminated windows with extremely low heterozygosity by using the Python 3.5.2 pomegranate package to fit a two-component Gaussian mixture model to the joint distribution of window heterozygosity, forcing the first component to be centred around the lower tail of the distribution and allowing the second to freely capture all the remaining heterozygosity variability 56 , 57 . As heterozygosity cannot be negative, and normal distributions near zero can cross into negative values, we used the normal cumulative distribution function to correct the posterior distribution by the negative excess—effectively fitting a truncated normal to the first component. The final SoH value was calculated using the posterior maximum likelihood classification between both components. We saw no significant correlation between contig N50 and SoH (Pearson correlation = 0.055, P  = 0.57, n  = 112).

Assessing the effect of the percentage of callable genome

We assessed whether the percentage of the genome that was callable (Supplementary Table 3 ) was likely to affect our analysis. The callable percentage was correlated with heterozygosity ( r  = −0.80, P  < 2.2 × 10 −16 , n  = 130), and weakly with SoH values ( r  = 0.18, P  = 0.06, n  = 112). There is no significant difference in callable percentage among IUCN categories (analysis of variance P  = 0.98, n  = 122) or between captive and wild populations ( t -test P  = 0.81, n  = 120).

Analysing patterns of diversity

We excluded two genomes with exceptionally high heterozygosity (heterozygosity >0.02; >5 s.d. above the mean). Both were of non-endangered species, and thus removing them made our determination of lower heterozygosity in endangered species more conservative. Of the remaining 124 genomes, we excluded 19 with allelic balance values that were more than one s.d. above the mean (>0.36). Abnormally high allelic balance can indicate sequencing biases with potential for artefacts in estimates of heterozygosity and/or SoH. Our final dataset contains heterozygosity values for 105 genomes and SoH values for 98 genomes (Supplementary Table 3 ). For seven genomes, we were unable to estimate SoH because the two components of the Gaussian mixture model overlapped completely. To ask about a possible directional relationship between level of IUCN concern and overall heterozygosity or SoH, we applied regression using the IUCN category as an ordinal predictor. We also asked about the relationship of diversity metrics to a set of species-level phenotypes for which correlations were previously reported (Extended Data Table 3 ).

The alignment was generated using the progressive mode of Cactus 37 , 58 . The topology used for the guide tree of the alignment was taken from TimeTree 47 ; the branch lengths of the guide tree were generated by a least-squares fit from a distance matrix. The distance matrix was based on the UCSC 100-way phyloP fourfold-degenerate site tree 38 for those species that had corresponding entries in the 100-way tree. For species not present in the 100-way tree, distance matrix entries were more coarsely estimated using the distance estimated from Mash 59 to the closest relative included in the 100-way data.

Cactus does not attempt to fully resolve the gene tree when multiple duplications take place along a single branch, as there is an implicit restriction in Cactus that a duplication event be represented as multiple regions in the child species aligned to a single region in the parent species. This precludes representing discordance between the gene tree and species tree that could occur with incomplete lineage-sorting or horizontal transfer. However, the guide tree has a minimal effect on the alignment, with little difference between alignments with different trees—even when using a tree that is purposely wrong 37 . Phenomena such as incomplete lineage sorting that affect a subset of species are unlikely to substantially affect the detection of purifying selection across the whole eutherian lineage described in Fig. 3 .

The alignment was generated in several steps, on account of its large scale. First, a backbone alignment of several long contiguity assemblies was generated, using the genomes of two non-placental mammals (Tasmanian devil ( Sarcophilus harrisii ) and platypus ( Ornithorhynchus anatinus )), to inform the reconstruction of the placental root. Next, separate clade alignments were generated for each major clade in the alignment: Euarchonta, Glires, Laurasiatheria, Afrotheria and Xenarthra. The roots of these clade alignments were then aligned to the corresponding ancestral genomes from the backbone, stitching these alignments together to create the final alignment. The process of aligning a genome to an existing ancestor is complex and further described in an accompanying Article that introduces the progressive mode of Cactus 37 .

We created a neutral model for the conservation analysis using ancestral repeats detected by RepeatMasker 60 on the eutherian ancestral genome produced in the Cactus alignment (tRNA and low-complexity repeats were removed). To fit the neutral model, we used phyloFit from the PHAST 61 package, using the REV (generalized reversible) model and EM optimization method. The training input was a MAF exported on columns from the set of ancestral repeats mentioned above. Because phyloFit does not support alignment columns that contain duplicates, if a genome had more than one sequence in a single alignment block, these were replaced with a single entry representing the consensus base at each column.

We extracted initial conservation scores using phyloP from the PHAST 61 package on a MAF exported using human as a reference. We converted the phyloP scores (which represent log-scaled P  values of acceleration or conservation) into P  values, then into q  values using the FDR-correction of Benjamini and Hochberg 62 . Any column with a resulting q  value less than 0.05 was deemed significantly conserved or accelerated.

The alignment—as well as conservation annotations—are available at https://cglgenomics.ucsc.edu/data/cactus/ .

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this paper.

Data availability

The project website is http://zoonomiaproject.org/ . Details of each Zoonomia genome assembly—including NCBI GenBank 63 accession numbers—are provided in Supplementary Table 1 . Sequence data and genome assemblies are available at https://www.ncbi.nlm.nih.gov/ . Variant lists for each species are provided at http://broad.io/variants . Further source data for Fig. 2 are provided in the Zoonomia GitHub repository ( https://doi.org/10.5281/zenodo.3887432 ). The Cactus alignment is provided at https://cglgenomics.ucsc.edu/data/cactus/ . A visualization of the alignments and phyloP data is available by loading our assembly hub into the UCSC browser 64 by copying the hub link https://comparative-genomics-hubs.s3-us-west-2.amazonaws.com/200m_hub.txt into the Track Hubs page. There are no restrictions on use. Source data are provided with this paper.

Code availability

The DISCOVAR de novo assembly code is available at https://github.com/broadinstitute/discovar_de_novo/releases/tag/v52488 ( https://doi.org/10.5281/zenodo.3870889 ), the Cactus pipeline is available at https://github.com/ComparativeGenomicsToolkit/cactus ( https://doi.org/10.5281/zenodo.3873410 ) and code for other analyses is available at https://github.com/broadinstitute/Zoonomia/ ( https://doi.org/10.5281/zenodo.3887432 ).

Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577 , 179–189 (2020).

ADS   CAS   PubMed   PubMed Central   Google Scholar  

Hiller, M. et al. A “forward genomics” approach links genotype to phenotype using independent phenotypic losses among related species. Cell Rep . 2 , 817–823 (2012).

CAS   PubMed   PubMed Central   Google Scholar  

Wasser, S. K. et al. Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science 349 , 84–87 (2015).

Wright, B. et al. Development of a SNP-based assay for measuring genetic diversity in the Tasmanian devil insurance population. BMC Genomics 16 , 791 (2015).

PubMed   PubMed Central   Google Scholar  

Lappalainen, T., Scott, A. J., Brandt, M. & Hall, I. M. Genomic analysis in the age of human genome sequencing. Cell 177 , 70–84 (2019).

Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet . 46 , 310–315 (2014).

Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478 , 476–482 (2011).

Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet . 47 , 1228–1235 (2015).

IUCN. The IUCN Red List of Threatened Species. Version 2019-2  http://www.iucnredlist.org (2019).

Ryder, O. A. & Onuma, M. Viable cell culture banking for biodiversity characterization and conservation. Annu. Rev. Anim. Biosci . 6 , 83–98 (2018).

PubMed   Google Scholar  

Weisenfeld, N. I. et al. Comprehensive variation discovery in single human genomes. Nat. Genet . 46 , 1350–1355 (2014).

Putnam, N. H. et al. Chromosome-scale shotgun assembly using an in vitro method for long-range linkage. Genome Res . 26 , 342–350 (2016).

Kim, J. et al. Reconstruction and evolutionary history of eutherian chromosomes. Proc. Natl Acad. Sci. USA 114 , E5379–E5388 (2017).

Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536 , 285–291 (2016).

Balasubramanian, S. et al. Using ALoFT to determine the impact of putative loss-of-function variants in protein-coding genes. Nat. Commun . 8 , 382 (2017).

ADS   PubMed   PubMed Central   Google Scholar  

Meadows, J. R. S. & Lindblad-Toh, K. Dissecting evolution and disease using comparative vertebrate genomics. Nat. Rev. Genet . 18 , 624–636 (2017).

CAS   PubMed   Google Scholar  

Cooper, G. M. & Shendure, J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat. Rev. Genet . 12 , 628–640 (2011).

Baiz, M. D., Tucker, P. K., Mueller, J. L. & Cortés-Ortiz, L. X-linked signature of reproductive isolation in humans is mirrored in a howler monkey hybrid zone.  J. Hered. 111 , 419–428 (2020).

Dobzhansky, T. & Dobzhansky, T. G. Genetics and the Origin of Species (Columbia Univ. Press, 1937).

Herrera-Álvarez, S., Karlsson, E., Ryder, O. A., Lindblad-Toh, K. & Crawford, A. J. How to make a rodent giant: genomic basis and tradeoffs of gigantism in the capybara, the world’s largest rodent. Preprint at https://doi.org/10.1101/424606 (2018).

Abegglen, L. M. et al. Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans. J. Am. Med. Assoc . 314 , 1850–1860 (2015).

CAS   Google Scholar  

Casewell, N. R. et al. Solenodon genome reveals convergent evolution of venom in eulipotyphlan mammals. Proc. Natl Acad. Sci. USA 116 , 25745–25755 (2019).

Beichman, A. C. et al. Aquatic adaptation and depleted diversity: a deep dive into the genomes of the sea otter and giant otter. Mol. Biol. Evol . 36 , 2631–2655 (2019).

Damas, J. et al. Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proc. Natl Acad. Sci. USA 117 , 22311–22322 (2020).

Xue, Y. et al. Mountain gorilla genomes reveal the impact of long-term population decline and inbreeding. Science 348 , 242–245 (2015).

Ceballos, F. C., Joshi, P. K., Clark, D. W., Ramsay, M. & Wilson, J. F. Runs of homozygosity: windows into population history and trait architecture. Nat. Rev. Genet . 19 , 220–234 (2018).

Spielman, D., Brook, B. W. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. Proc. Natl Acad. Sci. USA 101 , 15261–15264 (2004).

Vinson, J. P. et al. Assembly of polymorphic genomes: algorithms and application to Ciona savignyi . Genome Res . 15 , 1127–1135 (2005).

MacManes, M. D. & Lacey, E. A. The social brain: transcriptome assembly and characterization of the hippocampus from a social subterranean rodent, the colonial tuco-tuco ( Ctenomys sociabilis ). PLoS ONE 7 , e45524 (2012).

Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90 , 2648 (2009).

Google Scholar  

Cardillo, M. Biological determinants of extinction risk: why are smaller species less vulnerable? Anim. Conserv . 6 , 63–69 (2003).

Natesh, M. et al. Empowering conservation practice with efficient and economical genotyping from poor quality samples. Methods Ecol. Evol . 10 , 853–859 (2019).

Lowry, D. B. et al. Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol. Ecol. Resour . 17 , 142–152 (2017).

Shapiro, B. Pathways to de-extinction: how close can we get to resurrection of an extinct species? Funct. Ecol . 31 , 996–1002 (2017).

Benazzo, A. et al. Survival and divergence in a small group: the extraordinary genomic history of the endangered Apennine brown bear stragglers. Proc. Natl Acad. Sci. USA 114 , E9589–E9597 (2017).

Saremi, N. F. et al. Puma genomes from North and South America provide insights into the genomic consequences of inbreeding. Nat. Commun . 10 , 4769 (2019).

Armstrong, J. et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature https://doi.org/10.1038/s41586-020-2871-y (2020).

Haeussler, M. et al. The UCSC genome browser database: 2019 update. Nucleic Acids Res . 47 , D853–D858 (2019).

Rands, C. M., Meader, S., Ponting, C. P. & Lunter, G. 8.2% of the human genome is constrained: variation in rates of turnover across functional element classes in the human lineage. PLoS Genet . 10 , e1004525 (2014).

ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489 , 57–74 (2012).

ADS   Google Scholar  

GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550 , 204–213 (2017).

PubMed Central   Google Scholar  

Regev, A. et al. The human cell atlas. eLife 6 , e27041 (2017).

Lewin, H. A. et al. Earth BioGenome project: sequencing life for the future of life. Proc. Natl Acad. Sci. USA 115 , 4325–4333 (2018).

Koepfli, K.-P., Paten, B., the Genome 10K Community of Scientists & O’Brien, S. J. The Genome 10K project: a way forward. Annu. Rev. Anim. Biosci . 3 , 57–111 (2015).

Teeling, E. C. et al. Bat biology, genomes, and the Bat1K project: to generate chromosome-level genomes for all living bat species. Annu. Rev. Anim. Biosci . 6 , 23–46 (2018).

Feng, S. et al. Dense sampling of bird diversity increases power of comparative genomics. Nature https://doi.org/10.1038/s41586-020-2873-9 (2020).

Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol . 34 , 1812–1819 (2017).

Wilson, D. E. & Reeder, D. M. (eds)  Mammal Species of the World. A Taxonomic and Geographic Reference  3rd edn (Johns Hopkins Univ. Press, 2005).

Vlieghe, D. et al. A new generation of JASPAR, the open-access repository for transcription factor binding site profiles. Nucleic Acids Res . 34 , D95–D97 (2006).

Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31 , 3210–3212 (2015).

Farré, M. et al. A near-chromosome-scale genome assembly of the gemsbok ( Oryx gazella ): an iconic antelope of the Kalahari desert. Gigascience 8 , giy162 (2019).

McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res . 20 , 1297–1303 (2010).

DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet . 43 , 491–498 (2011).

Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25 , 2078–2079 (2009).

Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25 , 1754–1760 (2009).

Benaglia, T., Chauveau, D., Hunter, D. & Young, D. mixtools: an R package for analyzing finite mixture models. J. Stat. Softw . 32 , 1–29 (2009).

R Core Team. R: A Language and Environment for Statistical Computing.   https://www.R-project.org/ (2019).

Paten, B. et al. Cactus: algorithms for genome multiple sequence alignment. Genome Res . 21 , 1512–1528 (2011).

Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol . 17 , 132 (2016).

Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker Open-4.0.   http://www.repeatmasker.org/ (2013–2015).

Hubisz, M. J., Pollard, K. S. & Siepel, A. PHAST and RPHAST: phylogenetic analysis with space/time models. Brief. Bioinform . 12 , 41–51 (2011).

Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57 , 289–300 (1995).

MathSciNet   MATH   Google Scholar  

Benson, D. A. et al. GenBank. Nucleic Acids Res . 41 , D36–D42 (2013).

Nguyen, N. et al. Comparative assembly hubs: web-accessible browsers for comparative genomics. Bioinformatics 30 , 3293–3301 (2014).

Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581 , 434–443 (2020).

Pinheiro, E. C., Taddei, V. A., Migliorini, R. H. & Kettelhut, I. C. Effect of fasting on carbohydrate metabolism in frugivorous bats ( Artibeus lituratus and Artibeus jamaicensis ). Comp. Biochem. Physiol. B Biochem. Mol. Biol . 143 , 279–284 (2006).

Gordon, L. M. et al. Amorphous intergranular phases control the properties of rodent tooth enamel. Science 347 , 746–750 (2015).

ADS   CAS   PubMed   Google Scholar  

Hindle, A. G. & Martin, S. L. Intrinsic circannual regulation of brown adipose tissue form and function in tune with hibernation. Am. J. Physiol. Endocrinol. Metab . 306 , E284–E299 (2014).

Stanford, K. I. et al. Brown adipose tissue regulates glucose homeostasis and insulin sensitivity. J. Clin. Invest . 123 , 215–223 (2013).

Chondronikola, M. et al. Brown adipose tissue improves whole-body glucose homeostasis and insulin sensitivity in humans. Diabetes 63 , 4089–4099 (2014).

Saito, M. et al. High incidence of metabolically active brown adipose tissue in healthy adult humans: effects of cold exposure and adiposity. Diabetes 58 , 1526–1531 (2009).

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Acknowledgements

We thank the many individuals who provided samples and advice, including C. Adenyo, C. Avila, E. Baitchman, R. Behringer, A. Boyko, M. Breen, K. Campbell, P. Campbell, C. J. Conroy, K. Cooper, L. M. Dávalos, F. Delsuc, D. Distel, C. A. Emerling, J. Fronczek, N. Gemmel, J. Good, K. He, K. Helgen, A. Hindle, H. Hoekstra, R. Honeycutt, P. Hulva, W. Israelsen, B. Kayang, R. Kennerley, M. Korody, D. N. Lee, E. Louis, M. MacManes, A. Misuraca, A. Mitelberg, P. Morin, A. Mouton, M. Murayama, M. Nachman, A. Navarro, R. Ogden, B. Pasch, S. Puechmaille, T. J. Robinson, S. Rossiter, M. Ruedi, A. Seifert, S. Thomas, S. Turvey, G. Verbeylen and the late R. J. Baker. We also thank the Broad Institute Genomics Platform and SNP & SEQ Technology Platform (part of the National Genomics Infrastructure (NGI) Sweden and Science for Life Laboratory) and Swedish National Infrastructure for Computing (SNIC) at Uppmax. This project was funded by NIH NHGRI R01HG008742 (E.K.K., B.B., D.P.G., R.S., J.T.-M., J.J., H.J.N., B.P. and J. Armstrong), Swedish Research Council Distinguished Professor Award (K.L.-T., V.D.M., E.M. and J.R.S.M.), Swedish Research Council grant 2018-05973 (K.L.-T.), Knut and Alice Wallenberg Foundation (K.L.-T., V.D.M., E.M. and J.R.S.M.), Uppsala University (K.L.-T., V.D.M., E.M., J.R.S.M., J.J., J. Alfoldi and L.G.), Broad Institute Next10 (L.G.), Gladstone Institutes (K.S.P.), NIH NHGRI 5R01HG002939 (A.F.A.S. and R.H.), NIH NHGRI 5U24HG010136 (A.F.A.S. and R.H.), NIH NHGRI 5R01HG010485 (B.P. and M.D.), NIH NHGRI 2U41HG007234 (B.P., M.D. and J. Armstrong), NIH NIA 5PO1AG047200 (V.N.G.), NIH NIA 1UH2AG064706 (V.N.G.), BFU2017-86471-P MINECO/FEDER, UE (T.M.-B.), Secretaria d’Universitats i Recerca and CERCA Programme del Departament d’Economia i Coneixement de la Generalitat de Catalunya GRC 2017 SGR 880 (T.M.-B.), Howard Hughes International Early Career (T.M.-B.), European Research Council Horizon 2020 no. 864203 (T.M.-B.), Obra Social ‘La Caixa’ (T.M.-B.), BBSRC BBS/E/T/000PR9818, BBS/E/T/ 000PR9783 (W.H. and W.N.), BBSRC Core Strategic Programme Grant BB/P016774/1 (W.H., W.N. and F.D.), Sir Henry Dale Fellowship 200517/Z/16/Z jointly funded by the Wellcome Trust and the Royal Society (N.R.C.), FJCI-2016-29558 MICINN (D.J.), Prince Albert II Foundation of Monaco and Canada, Global Genome Initiative, Smithsonian Institution (M.N.), European Research Council Research Grant ERC-2012-StG311000 (E.C.T.), Irish Research Council Laureate Award (E.C.T.), UK Medical Research Council MR/P026028/1 (W.H. and W.N.), National Science Foundation DEB-1457735 (M.S.S.), National Science Foundation DEB-1753760 (W.J.M.), National Science Foundation IOS-2029774 (E.K.K. and D.P.G.), Robert and Rosabel Osborne Endowment (H.A.L. and J.D.), Swedish Research Council, FORMAS 221-2012-1531 (J.R.S.M.), NSF RoL: FELS: EAGER: DEB 1838283 (D.A.R.) and Academy of Finland grant to Center of Excellence in Tumor Genetics Research no. 312042 (T.K. and J.T.).

Author information

These authors contributed equally: Kerstin Lindblad-Toh, Elinor K. Karlsson

Authors and Affiliations

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Diane P. Genereux, Jeremy Johnson, Vadim N. Gladyshev, Linda Goodman, Eric S. Lander, Hyun Ji Noh, Ross Swofford, Jason Turner-Maier, Jessica Alfoldi, Bruce Birren, Kerstin Lindblad-Toh & Elinor K. Karlsson

Institute of Evolutionary Biology (UPF-CSIC), PRBB, Barcelona, Spain

Aitor Serres, David Juan, Lukas F. K. Kuderna & Tomas Marques-Bonet

UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA

Joel Armstrong, Mark Diekhans, Ian T. Fiddes & Benedict Paten

Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden

Voichita D. Marinescu, Eva Murén, Jennifer R. S. Meadows & Kerstin Lindblad-Toh

Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

Gill Bejerano & Linda Goodman

Department of Computer Science, Stanford University, Stanford, CA, USA

Gill Bejerano

Department of Developmental Biology, Stanford University, Stanford, CA, USA

Department of Pediatrics, Stanford University, Stanford, CA, USA

Centre for Snakebite Research and Interventions, Liverpool School of Tropical Medicine, Liverpool, UK

Nicholas R. Casewell

San Diego Zoo Global, Beckman Center for Conservation Research, San Diego, CA, USA

Leona G. Chemnick, Marlys L. Houck, Cynthia C. Steiner & Oliver A. Ryder

The UC Davis Genome Center, University of California, Davis, Davis, CA, USA

Joana Damas

Department of Biological Sciences, University of East Anglia, Norwich, UK

Federica Di Palma & Harris A. Lewin

Earlham Institute, Norwich, UK

Federica Di Palma, Wilfried Haerty & Will Nash

Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA

Manuel Garber & Elinor K. Karlsson

Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Vadim N. Gladyshev

Fauna Bio Incorporated, Emeryville, CA, USA

Linda Goodman

Institute for Systems Biology, Seattle, WA, USA

Robert Hubley & Arian F. A. Smit

Department of Biochemistry, University of Cambridge, Cambridge, UK

Teemu Kivioja & Jussi Taipale

Applied Tumor Genomics Research Program, University of Helsinki, Helsinki, Finland

Smithsonian-Mason School of Conservation, Front Royal, VA, USA

Klaus-Peter Koepfli

Department of Biology, MIT, Cambridge, MA, USA

Eric S. Lander

Department of Systems Biology, Harvard Medical School, Boston, MA, USA

Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA

William J. Murphy

Marine Mammal Program, Smithsonian Institution, Washington, DC, USA

Martin Nweeia

Restorative Dentistry and Biomaterials Sciences, Harvard School of Dental Medicine, Boston, MA, USA

School of Dental Medicine, Case Western Reserve University, Cleveland, OH, USA

Carnegie Mellon University, School of Computer Science, Department of Computational Biology, Pittsburgh, PA, USA

Andreas R. Pfenning

Chan Zuckerberg Biohub, San Francisco, CA, USA

Katherine S. Pollard

Gladstone Institutes, San Francisco, CA, USA

Department of Epidemiology and Biostatistics, Institute for Computational Health Sciences and Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA

David A. Ray

Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA, USA

Beth Shapiro

Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA, USA

Department of Evolution, Ecology and Organismal Biology, University of California, Riverside, Riverside, CA, USA

Mark S. Springer

Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

Jussi Taipale

School of Biology and Environmental Science, University College Dublin, Dublin, Ireland

Emma C. Teeling

Department of Evolution, Behavior, and Ecology, Division of Biology, University of California, San Diego, La Jolla, CA, USA

Oliver A. Ryder

Department of Evolution and Ecology, University of California, Davis, Davis, CA, USA

Harris A. Lewin

Catalan Institution of Research and Advanced Studies (ICREA), Barcelona, Spain

Tomas Marques-Bonet

Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Barcelona, Spain

CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain

Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA

Elinor K. Karlsson

  • Diane P. Genereux
  • , Aitor Serres
  • , Joel Armstrong
  • , Jeremy Johnson
  • , Voichita D. Marinescu
  • , Eva Murén
  • , David Juan
  • , Gill Bejerano
  • , Nicholas R. Casewell
  • , Leona G. Chemnick
  • , Joana Damas
  • , Federica Di Palma
  • , Mark Diekhans
  • , Ian T. Fiddes
  • , Manuel Garber
  • , Vadim N. Gladyshev
  • , Linda Goodman
  • , Wilfried Haerty
  • , Marlys L. Houck
  • , Robert Hubley
  • , Teemu Kivioja
  • , Klaus-Peter Koepfli
  • , Lukas F. K. Kuderna
  • , Eric S. Lander
  • , Jennifer R. S. Meadows
  • , William J. Murphy
  • , Will Nash
  • , Hyun Ji Noh
  • , Martin Nweeia
  • , Andreas R. Pfenning
  • , Katherine S. Pollard
  • , David A. Ray
  • , Beth Shapiro
  • , Arian F. A. Smit
  • , Mark S. Springer
  • , Cynthia C. Steiner
  • , Ross Swofford
  • , Jussi Taipale
  • , Emma C. Teeling
  • , Jason Turner-Maier
  • , Jessica Alfoldi
  • , Bruce Birren
  • , Oliver A. Ryder
  • , Harris A. Lewin
  • , Benedict Paten
  • , Tomas Marques-Bonet
  • , Kerstin Lindblad-Toh
  •  & Elinor K. Karlsson

Contributions

K.L.-T. conceived the project. J.J., V.D.M., E.M., N.R.C., L.G.C., J.D., V.N.G., M.L.H., K.-P.K., J.R.S.M., W.J.M., M.N., D.A.R., R.S., E.C.T., J. Alfoldi, O.A.R., H.A.L., K.L.-T. and E.K.K. contributed to the acquisition of the samples. J.J., V.D.M., E.M., J.D., L.G., K.-P.K., H.J.N., C.C.S., R.S., J.T.-M., J. Alfoldi, O.A.R., H.A.L., K.L.-T. and E.K.K. contributed to the production of the genome assemblies. D.P.G., A.S., J. Armstrong, J.J., D.J., I.T.F., L.F.K.K., H.A.L., T.M.-B., K.L.-T. and E.K.K. contributed to the data analysis. D.P.G., J.J., V.D.M., G.B., F.D.P., M.D., I.T.F., M.G., V.N.G., W.H., R.H., T.K., E.S.L., J.R.S.M., A.R.P., K.S.P., A.F.A.S., M.S.S., J.T., J. Alfoldi, B.B., O.A.R., H.A.L., B.P., T.M.-B., K.L.-T. and E.K.K. contributed to the design and conduct of the project. D.P.G., E.S.L., W.N., B.S., O.A.R., K.L.-T. and E.K.K. wrote the manuscript, with input from all other authors.

Corresponding author

Correspondence to Elinor K. Karlsson .

Ethics declarations

Competing interests.

L.G. is a co-founder of, equity owner in and chief technical officer at Fauna Bio Incorporated.

Additional information

Peer review information Nature thanks Chris Ponting, Steven Salzberg, Guojie Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended data fig. 1 notable traits in non-human mammals..

Sequences from species with notable phenotypes can inform human medicine, basic biology and biodiversity conservation, but sample collection can be challenging. a , The Jamaican fruit bat ( Artibeus jamaicensis ) maintains constant blood glucose across intervals of fruit-eating and fasting 66 , achieving homeostasis to a degree that is unknown in the treatment of human diabetes. b , The North American beaver ( Castor canadensis ) avoids tooth decay by incorporating iron rather than magnesium into tooth enamel, which yields an orange hue 67 . c , The thirteen-lined ground squirrel ( Ictidomys tridecemlineatus ) prepares for hibernation by rapidly increasing the thermogenic activity of brown fat 68 , a process that—in humans—is connected to improved glucose homeostasis and insulin sensitivity 69 , 70 , 71 . d , The tiny bumblebee bat ( Craseonycteris thonglongyai ) is among the smallest of mammals, making it a sparse source of DNA. e , The remote habitat of the very rare Amazon River dolphin ( Inia geoffrensis ) precludes collection of the high-molecular weight DNA. Image sources: Merlin D. Tuttle/Science Source ( a ); Stephen J. Krasemann/Science Source ( b ); Allyson Hindle ( c ); Sébastien J. Puechmaille (CC BY-SA) ( d ); M. Watson/Science Source ( e ).

Extended Data Fig. 2 Sample collection can be challenging, and sequencing methods must be selected to handle the sample quality.

To enable the inclusion of species from across the eutherian tree (including from the 50% of mammalian families not represented in existing genome databases), the Zoonomia Project needed sequencing and assembly methods that produce reliable data from DNA collected in remote locations, sometimes in only modest quantities and often without benefit of cold chains for transport. a , For the marine species such as the narwhal ( Monodon monoceros ), simply accessing an individual in the wild can prove challenging. For example, to sample DNA from the near-threatened narwhal, M.N. and Inuit guide D. Angnatsiak camped on the edge of an ice floe between Pond Inlet and Bylot Island, at the northeastern tip of Baffin Island. After a narwhal was collected by Inuit hunters as part of an annual hunt, hours of flensing were necessary for the collection of tissue samples. From left to right, F. McCann, H. C. Schmidt, F. Eichmiller, M.N., J. Orr (facing backward) and J. Orr (standing). b , For endangered species such as the Hispaniolan solenodon ( S. paradoxus ), sample collection must be designed to minimize stress to the individual, limiting the amount of DNA that can be collected 22 . To collect DNA from the endangered solenodon without imposing stress on individuals in the wild, N.R.C. turned to the world’s only captive solenodons, which are housed off-exhibit at ZOODOM in the Dominican Republic. With help from veterinarians at the zoo, N.R.C. collected a small amount of blood from the rugged tail of the solenodon. Narwhal photograph by G. Freund, and courtesy of M.N. Solenodon photograph courtesy of L. Emery.

Supplementary information

Supplementary tables.

This file contains Supplementary Tables 1-3.

Reporting Summary

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Zoonomia Consortium. A comparative genomics multitool for scientific discovery and conservation. Nature 587 , 240–245 (2020). https://doi.org/10.1038/s41586-020-2876-6

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Comparative Essay

Barbara P

How to Write a Comparative Essay – A Complete Guide

10 min read

Published on: Jan 28, 2020

Last updated on: Nov 21, 2023

Comparative Essay

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Comparative essay is a common assignment for school and college students. Many students are not aware of the complexities of crafting a strong comparative essay. 

If you too are struggling with this, don't worry!

In this blog, you will get a complete writing guide for comparative essay writing. From structuring formats to creative topics, this guide has it all.

So, keep reading!

On This Page On This Page -->

What is a Comparative Essay?

A comparative essay is a type of essay in which an essay writer compares at least two or more items. The author compares two subjects with the same relation in terms of similarities and differences depending on the assignment.

The main purpose of the comparative essay is to:

  • Highlight the similarities and differences in a systematic manner.
  • Provide great clarity of the subject to the readers.
  • Analyze two things and describe their advantages and drawbacks.

A comparative essay is also known as compare and contrast essay or a comparison essay. It analyzes two subjects by either comparing them, contrasting them, or both. The Venn diagram is the best tool for writing a paper about the comparison between two subjects.  

Moreover, a comparative analysis essay discusses the similarities and differences of themes, items, events, views, places, concepts, etc. For example, you can compare two different novels (e.g., The Adventures of Huckleberry Finn and The Red Badge of Courage).

However, a comparative essay is not limited to specific topics. It covers almost every topic or subject with some relation.

Comparative Essay Structure

A good comparative essay is based on how well you structure your essay. It helps the reader to understand your essay better. 

The structure is more important than what you write. This is because it is necessary to organize your essay so that the reader can easily go through the comparisons made in an essay.

The following are the two main methods in which you can organize your comparative essay.

Point-by-Point Method 

The point-by-point or alternating method provides a detailed overview of the items that you are comparing. In this method, organize items in terms of similarities and differences.

This method makes the writing phase easy for the writer to handle two completely different essay subjects. It is highly recommended where some depth and detail are required.

Below given is the structure of the point-by-point method. 

Block Method 

The block method is the easiest as compared to the point-by-point method. In this method, you divide the information in terms of parameters. It means that the first paragraph compares the first subject and all their items, then the second one compares the second, and so on.

However, make sure that you write the subject in the same order. This method is best for lengthy essays and complicated subjects.

Here is the structure of the block method. 

Therefore, keep these methods in mind and choose the one according to the chosen subject.

Mixed Paragraphs Method

In this method, one paragraph explains one aspect of the subject. As a writer, you will handle one point at a time and one by one. This method is quite beneficial as it allows you to give equal weightage to each subject and help the readers identify the point of comparison easily.

How to Start a Comparative Essay?

Here, we have gathered some steps that you should follow to start a well-written comparative essay.  

Choose a Topic

The foremost step in writing a comparative essay is to choose a suitable topic.

Choose a topic or theme that is interesting to write about and appeals to the reader. 

An interesting essay topic motivates the reader to know about the subject. Also, try to avoid complicated topics for your comparative essay. 

Develop a List of Similarities and Differences 

Create a list of similarities and differences between two subjects that you want to include in the essay. Moreover, this list helps you decide the basis of your comparison by constructing your initial plan. 

Evaluate the list and establish your argument and thesis statement .

Establish the Basis for Comparison 

The basis for comparison is the ground for you to compare the subjects. In most cases, it is assigned to you, so check your assignment or prompt.

Furthermore, the main goal of the comparison essay is to inform the reader of something interesting. It means that your subject must be unique to make your argument interesting.  

Do the Research 

In this step, you have to gather information for your subject. If your comparative essay is about social issues, historical events, or science-related topics, you must do in-depth research.    

However, make sure that you gather data from credible sources and cite them properly in the essay.

Create an Outline

An essay outline serves as a roadmap for your essay, organizing key elements into a structured format.

With your topic, list of comparisons, basis for comparison, and research in hand, the next step is to create a comprehensive outline. 

Here is a standard comparative essay outline:

How to Write a Comparative Essay?

Now that you have the basic information organized in an outline, you can get started on the writing process. 

Here are the essential parts of a comparative essay: 

Comparative Essay Introduction 

Start off by grabbing your reader's attention in the introduction . Use something catchy, like a quote, question, or interesting fact about your subjects. 

Then, give a quick background so your reader knows what's going on. 

The most important part is your thesis statement, where you state the main argument , the basis for comparison, and why the comparison is significant.

This is what a typical thesis statement for a comparative essay looks like:

Comparative Essay Body Paragraphs 

The body paragraphs are where you really get into the details of your subjects. Each paragraph should focus on one thing you're comparing.

Start by talking about the first point of comparison. Then, go on to the next points. Make sure to talk about two to three differences to give a good picture.

After that, switch gears and talk about the things they have in common. Just like you discussed three differences, try to cover three similarities. 

This way, your essay stays balanced and fair. This approach helps your reader understand both the ways your subjects are different and the ways they are similar. Keep it simple and clear for a strong essay.

Comparative Essay Conclusion

In your conclusion , bring together the key insights from your analysis to create a strong and impactful closing.

Consider the broader context or implications of the subjects' differences and similarities. What do these insights reveal about the broader themes or ideas you're exploring?

Discuss the broader implications of these findings and restate your thesis. Avoid introducing new information and end with a thought-provoking statement that leaves a lasting impression.

Below is the detailed comparative essay template format for you to understand better.

Comparative Essay Format

Comparative Essay Examples

Have a look at these comparative essay examples pdf to get an idea of the perfect essay.

Comparative Essay on Summer and Winter

Comparative Essay on Books vs. Movies

Comparative Essay Sample

Comparative Essay Thesis Example

Comparative Essay on Football vs Cricket

Comparative Essay on Pet and Wild Animals

Comparative Essay Topics

Comparative essay topics are not very difficult or complex. Check this list of essay topics and pick the one that you want to write about.

  • How do education and employment compare?
  • Living in a big city or staying in a village.
  • The school principal or college dean.
  • New Year vs. Christmas celebration.
  • Dried Fruit vs. Fresh. Which is better?
  • Similarities between philosophy and religion.
  • British colonization and Spanish colonization.
  • Nuclear power for peace or war?
  • Bacteria or viruses.
  • Fast food vs. homemade food.

Tips for Writing A Good Comparative Essay

Writing a compelling comparative essay requires thoughtful consideration and strategic planning. Here are some valuable tips to enhance the quality of your comparative essay:

  • Clearly define what you're comparing, like themes or characters.
  • Plan your essay structure using methods like point-by-point or block paragraphs.
  • Craft an introduction that introduces subjects and states your purpose.
  • Ensure an equal discussion of both similarities and differences.
  • Use linking words for seamless transitions between paragraphs.
  • Gather credible information for depth and authenticity.
  • Use clear and simple language, avoiding unnecessary jargon.
  • Dedicate each paragraph to a specific point of comparison.
  • Summarize key points, restate the thesis, and emphasize significance.
  • Thoroughly check for clarity, coherence, and correct any errors.

Transition Words For Comparative Essays

Transition words are crucial for guiding your reader through the comparative analysis. They help establish connections between ideas and ensure a smooth flow in your essay. 

Here are some transition words and phrases to improve the flow of your comparative essay:

Transition Words for Similarities

  • Correspondingly
  • In the same vein
  • In like manner
  • In a similar fashion
  • In tandem with

Transition Words for Differences

  • On the contrary
  • In contrast
  • Nevertheless
  • In spite of
  • Notwithstanding
  • On the flip side
  • In contradistinction

Check out this blog listing more transition words that you can use to enhance your essay’s coherence!

In conclusion, now that you have the important steps and helpful tips to write a good comparative essay, you can start working on your own essay. 

However, if you find it tough to begin, you can always hire our professional essay writing service . 

Our skilled writers can handle any type of essay or assignment you need. So, don't wait—place your order now and make your academic journey easier!

Frequently Asked Question

How long is a comparative essay.

A comparative essay is 4-5 pages long, but it depends on your chosen idea and topic.

How do you end a comparative essay?

Here are some tips that will help you to end the comparative essay.

  • Restate the thesis statement
  • Wrap up the entire essay
  • Highlight the main points

Barbara P (Literature, Marketing)

Dr. Barbara is a highly experienced writer and author who holds a Ph.D. degree in public health from an Ivy League school. She has worked in the medical field for many years, conducting extensive research on various health topics. Her writing has been featured in several top-tier publications.

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Writing a paper: comparing & contrasting.

A compare and contrast paper discusses the similarities and differences between two or more topics. The paper should contain an introduction with a thesis statement, a body where the comparisons and contrasts are discussed, and a conclusion.

Address Both Similarities and Differences

Because this is a compare and contrast paper, both the similarities and differences should be discussed. This will require analysis on your part, as some topics will appear to be quite similar, and you will have to work to find the differing elements.

Make Sure You Have a Clear Thesis Statement

Just like any other essay, a compare and contrast essay needs a thesis statement. The thesis statement should not only tell your reader what you will do, but it should also address the purpose and importance of comparing and contrasting the material.

Use Clear Transitions

Transitions are important in compare and contrast essays, where you will be moving frequently between different topics or perspectives.

  • Examples of transitions and phrases for comparisons: as well, similar to, consistent with, likewise, too
  • Examples of transitions and phrases for contrasts: on the other hand, however, although, differs, conversely, rather than.

For more information, check out our transitions page.

Structure Your Paper

Consider how you will present the information. You could present all of the similarities first and then present all of the differences. Or you could go point by point and show the similarity and difference of one point, then the similarity and difference for another point, and so on.

Include Analysis

It is tempting to just provide summary for this type of paper, but analysis will show the importance of the comparisons and contrasts. For instance, if you are comparing two articles on the topic of the nursing shortage, help us understand what this will achieve. Did you find consensus between the articles that will support a certain action step for people in the field? Did you find discrepancies between the two that point to the need for further investigation?

Make Analogous Comparisons

When drawing comparisons or making contrasts, be sure you are dealing with similar aspects of each item. To use an old cliché, are you comparing apples to apples?

  • Example of poor comparisons: Kubista studied the effects of a later start time on high school students, but Cook used a mixed methods approach. (This example does not compare similar items. It is not a clear contrast because the sentence does not discuss the same element of the articles. It is like comparing apples to oranges.)
  • Example of analogous comparisons: Cook used a mixed methods approach, whereas Kubista used only quantitative methods. (Here, methods are clearly being compared, allowing the reader to understand the distinction.

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  • Comparative Analysis

What It Is and Why It's Useful

Comparative analysis asks writers to make an argument about the relationship between two or more texts. Beyond that, there's a lot of variation, but three overarching kinds of comparative analysis stand out:

  • Coordinate (A ↔ B): In this kind of analysis, two (or more) texts are being read against each other in terms of a shared element, e.g., a memoir and a novel, both by Jesmyn Ward; two sets of data for the same experiment; a few op-ed responses to the same event; two YA books written in Chicago in the 2000s; a film adaption of a play; etc. 
  • Subordinate (A  → B) or (B → A ): Using a theoretical text (as a "lens") to explain a case study or work of art (e.g., how Anthony Jack's The Privileged Poor can help explain divergent experiences among students at elite four-year private colleges who are coming from similar socio-economic backgrounds) or using a work of art or case study (i.e., as a "test" of) a theory's usefulness or limitations (e.g., using coverage of recent incidents of gun violence or legislation un the U.S. to confirm or question the currency of Carol Anderson's The Second ).
  • Hybrid [A  → (B ↔ C)] or [(B ↔ C) → A] , i.e., using coordinate and subordinate analysis together. For example, using Jack to compare or contrast the experiences of students at elite four-year institutions with students at state universities and/or community colleges; or looking at gun culture in other countries and/or other timeframes to contextualize or generalize Anderson's main points about the role of the Second Amendment in U.S. history.

"In the wild," these three kinds of comparative analysis represent increasingly complex—and scholarly—modes of comparison. Students can of course compare two poems in terms of imagery or two data sets in terms of methods, but in each case the analysis will eventually be richer if the students have had a chance to encounter other people's ideas about how imagery or methods work. At that point, we're getting into a hybrid kind of reading (or even into research essays), especially if we start introducing different approaches to imagery or methods that are themselves being compared along with a couple (or few) poems or data sets.

Why It's Useful

In the context of a particular course, each kind of comparative analysis has its place and can be a useful step up from single-source analysis. Intellectually, comparative analysis helps overcome the "n of 1" problem that can face single-source analysis. That is, a writer drawing broad conclusions about the influence of the Iranian New Wave based on one film is relying entirely—and almost certainly too much—on that film to support those findings. In the context of even just one more film, though, the analysis is suddenly more likely to arrive at one of the best features of any comparative approach: both films will be more richly experienced than they would have been in isolation, and the themes or questions in terms of which they're being explored (here the general question of the influence of the Iranian New Wave) will arrive at conclusions that are less at-risk of oversimplification.

For scholars working in comparative fields or through comparative approaches, these features of comparative analysis animate their work. To borrow from a stock example in Western epistemology, our concept of "green" isn't based on a single encounter with something we intuit or are told is "green." Not at all. Our concept of "green" is derived from a complex set of experiences of what others say is green or what's labeled green or what seems to be something that's neither blue nor yellow but kind of both, etc. Comparative analysis essays offer us the chance to engage with that process—even if only enough to help us see where a more in-depth exploration with a higher and/or more diverse "n" might lead—and in that sense, from the standpoint of the subject matter students are exploring through writing as well the complexity of the genre of writing they're using to explore it—comparative analysis forms a bridge of sorts between single-source analysis and research essays.

Typical learning objectives for single-sources essays: formulate analytical questions and an arguable thesis, establish stakes of an argument, summarize sources accurately, choose evidence effectively, analyze evidence effectively, define key terms, organize argument logically, acknowledge and respond to counterargument, cite sources properly, and present ideas in clear prose.

Common types of comparative analysis essays and related types: two works in the same genre, two works from the same period (but in different places or in different cultures), a work adapted into a different genre or medium, two theories treating the same topic; a theory and a case study or other object, etc.

How to Teach It: Framing + Practice

Framing multi-source writing assignments (comparative analysis, research essays, multi-modal projects) is likely to overlap a great deal with "Why It's Useful" (see above), because the range of reasons why we might use these kinds of writing in academic or non-academic settings is itself the reason why they so often appear later in courses. In many courses, they're the best vehicles for exploring the complex questions that arise once we've been introduced to the course's main themes, core content, leading protagonists, and central debates.

For comparative analysis in particular, it's helpful to frame assignment's process and how it will help students successfully navigate the challenges and pitfalls presented by the genre. Ideally, this will mean students have time to identify what each text seems to be doing, take note of apparent points of connection between different texts, and start to imagine how those points of connection (or the absence thereof)

  • complicates or upends their own expectations or assumptions about the texts
  • complicates or refutes the expectations or assumptions about the texts presented by a scholar
  • confirms and/or nuances expectations and assumptions they themselves hold or scholars have presented
  • presents entirely unforeseen ways of understanding the texts

—and all with implications for the texts themselves or for the axes along which the comparative analysis took place. If students know that this is where their ideas will be heading, they'll be ready to develop those ideas and engage with the challenges that comparative analysis presents in terms of structure (See "Tips" and "Common Pitfalls" below for more on these elements of framing).

Like single-source analyses, comparative essays have several moving parts, and giving students practice here means adapting the sample sequence laid out at the " Formative Writing Assignments " page. Three areas that have already been mentioned above are worth noting:

  • Gathering evidence : Depending on what your assignment is asking students to compare (or in terms of what), students will benefit greatly from structured opportunities to create inventories or data sets of the motifs, examples, trajectories, etc., shared (or not shared) by the texts they'll be comparing. See the sample exercises below for a basic example of what this might look like.
  • Why it Matters: Moving beyond "x is like y but also different" or even "x is more like y than we might think at first" is what moves an essay from being "compare/contrast" to being a comparative analysis . It's also a move that can be hard to make and that will often evolve over the course of an assignment. A great way to get feedback from students about where they're at on this front? Ask them to start considering early on why their argument "matters" to different kinds of imagined audiences (while they're just gathering evidence) and again as they develop their thesis and again as they're drafting their essays. ( Cover letters , for example, are a great place to ask writers to imagine how a reader might be affected by reading an their argument.)
  • Structure: Having two texts on stage at the same time can suddenly feel a lot more complicated for any writer who's used to having just one at a time. Giving students a sense of what the most common patterns (AAA / BBB, ABABAB, etc.) are likely to be can help them imagine, even if provisionally, how their argument might unfold over a series of pages. See "Tips" and "Common Pitfalls" below for more information on this front.

Sample Exercises and Links to Other Resources

  • Common Pitfalls
  • Advice on Timing
  • Try to keep students from thinking of a proposed thesis as a commitment. Instead, help them see it as more of a hypothesis that has emerged out of readings and discussion and analytical questions and that they'll now test through an experiment, namely, writing their essay. When students see writing as part of the process of inquiry—rather than just the result—and when that process is committed to acknowledging and adapting itself to evidence, it makes writing assignments more scientific, more ethical, and more authentic. 
  • Have students create an inventory of touch points between the two texts early in the process.
  • Ask students to make the case—early on and at points throughout the process—for the significance of the claim they're making about the relationship between the texts they're comparing.
  • For coordinate kinds of comparative analysis, a common pitfall is tied to thesis and evidence. Basically, it's a thesis that tells the reader that there are "similarities and differences" between two texts, without telling the reader why it matters that these two texts have or don't have these particular features in common. This kind of thesis is stuck at the level of description or positivism, and it's not uncommon when a writer is grappling with the complexity that can in fact accompany the "taking inventory" stage of comparative analysis. The solution is to make the "taking inventory" stage part of the process of the assignment. When this stage comes before students have formulated a thesis, that formulation is then able to emerge out of a comparative data set, rather than the data set emerging in terms of their thesis (which can lead to confirmation bias, or frequency illusion, or—just for the sake of streamlining the process of gathering evidence—cherry picking). 
  • For subordinate kinds of comparative analysis , a common pitfall is tied to how much weight is given to each source. Having students apply a theory (in a "lens" essay) or weigh the pros and cons of a theory against case studies (in a "test a theory") essay can be a great way to help them explore the assumptions, implications, and real-world usefulness of theoretical approaches. The pitfall of these approaches is that they can quickly lead to the same biases we saw here above. Making sure that students know they should engage with counterevidence and counterargument, and that "lens" / "test a theory" approaches often balance each other out in any real-world application of theory is a good way to get out in front of this pitfall.
  • For any kind of comparative analysis, a common pitfall is structure. Every comparative analysis asks writers to move back and forth between texts, and that can pose a number of challenges, including: what pattern the back and forth should follow and how to use transitions and other signposting to make sure readers can follow the overarching argument as the back and forth is taking place. Here's some advice from an experienced writing instructor to students about how to think about these considerations:

a quick note on STRUCTURE

     Most of us have encountered the question of whether to adopt what we might term the “A→A→A→B→B→B” structure or the “A→B→A→B→A→B” structure.  Do we make all of our points about text A before moving on to text B?  Or do we go back and forth between A and B as the essay proceeds?  As always, the answers to our questions about structure depend on our goals in the essay as a whole.  In a “similarities in spite of differences” essay, for instance, readers will need to encounter the differences between A and B before we offer them the similarities (A d →B d →A s →B s ).  If, rather than subordinating differences to similarities you are subordinating text A to text B (using A as a point of comparison that reveals B’s originality, say), you may be well served by the “A→A→A→B→B→B” structure.  

     Ultimately, you need to ask yourself how many “A→B” moves you have in you.  Is each one identical?  If so, you may wish to make the transition from A to B only once (“A→A→A→B→B→B”), because if each “A→B” move is identical, the “A→B→A→B→A→B” structure will appear to involve nothing more than directionless oscillation and repetition.  If each is increasingly complex, however—if each AB pair yields a new and progressively more complex idea about your subject—you may be well served by the “A→B→A→B→A→B” structure, because in this case it will be visible to readers as a progressively developing argument.

As we discussed in "Advice on Timing" at the page on single-source analysis, that timeline itself roughly follows the "Sample Sequence of Formative Assignments for a 'Typical' Essay" outlined under " Formative Writing Assignments, " and it spans about 5–6 steps or 2–4 weeks. 

Comparative analysis assignments have a lot of the same DNA as single-source essays, but they potentially bring more reading into play and ask students to engage in more complicated acts of analysis and synthesis during the drafting stages. With that in mind, closer to 4 weeks is probably a good baseline for many single-source analysis assignments. For sections that meet once per week, the timeline will either probably need to expand—ideally—a little past the 4-week side of things, or some of the steps will need to be combined or done asynchronously.

What It Can Build Up To

Comparative analyses can build up to other kinds of writing in a number of ways. For example:

  • They can build toward other kinds of comparative analysis, e.g., student can be asked to choose an additional source to complicate their conclusions from a previous analysis, or they can be asked to revisit an analysis using a different axis of comparison, such as race instead of class. (These approaches are akin to moving from a coordinate or subordinate analysis to more of a hybrid approach.)
  • They can scaffold up to research essays, which in many instances are an extension of a "hybrid comparative analysis."
  • Like single-source analysis, in a course where students will take a "deep dive" into a source or topic for their capstone, they can allow students to "try on" a theoretical approach or genre or time period to see if it's indeed something they want to research more fully.
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4.1: Introduction to Comparison and Contrast Essay

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The key to a good compare-and-contrast essay is to choose two or more subjects that connect in a meaningful way. Comparison and contrast is simply telling how two things are alike or different. The compare-and-contrast essay starts with a thesis that clearly states the two subjects that are to be compared, contrasted, or both. The thesis should focus on comparing, contrasting, or both.

Key Elements of the Compare and Contrast:

  • A compare-and-contrast essay analyzes two subjects by either comparing them, contrasting them, or both.
  • The purpose of writing a comparison or contrast essay is not to state the obvious but rather to illuminate subtle differences or unexpected similarities between two subjects.
  • The thesis should clearly state the subjects that are to be compared, contrasted, or both, and it should state what is to be learned from doing so.
  • Organize by the subjects themselves, one then the other.
  • Organize by individual points, in which you discuss each subject in relation to each point.
  • Use phrases of comparison or phrases of contrast to signal to readers how exactly the two subjects are being analyzed.

Objectives: By the end of this unit, you will be able to

  • Identify compare & contrast relationships in model essays
  • Construct clearly formulated thesis statements that show compare & contrast relationships
  • Use pre-writing techniques to brainstorm and organize ideas showing a comparison and/or contrast
  • Construct an outline for a five-paragraph compare & contrast essay
  • Write a five-paragraph compare & contrast essay
  • Use a variety of vocabulary and language structures that express compare & contrast essay relationships

Example Thesis: Organic vegetables may cost more than those that are conventionally grown, but when put to the test, they are definitely worth every extra penny.

Graphic Showing Organization for Comparison Contrast Essay

Sample Paragraph:

Organic grown tomatoes purchased at the farmers’ market are very different from tomatoes that are grown conventionally. To begin with, although tomatoes from both sources will mostly be red, the tomatoes at the farmers’ market are a brighter red than those at a grocery store. That doesn’t mean they are shinier—in fact, grocery store tomatoes are often shinier since they have been waxed. You are likely to see great size variation in tomatoes at the farmers’ market, with tomatoes ranging from only a couple of inches across to eight inches across. By contrast, the tomatoes in a grocery store will be fairly uniform in size. All the visual differences are interesting, but the most important difference is the taste. The farmers’ market tomatoes will be bursting with flavor from ripening on the vine in their own time. However, the grocery store tomatoes are often close to being flavorless. In conclusion, the differences in organic and conventionally grown tomatoes are obvious in color, size and taste.

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How to write a comparative analysis.

Writing a comparative review in a research paper is not as difficult as many people might tend to think. With some tips, it is possible to write an outstanding comparative review. There are steps that must be utilized to attain this result. They are as detailed in this article.

Within the literary, academic, and journalistic world, analysis allows exposing ideas and arguments in front of a context, making it an important material for discussion within the professional work.

Within this genre, we can find a comparative analysis. For some authors, the comparative essay is defined as the text where two opposing positions are proposed or where two theses are verified. Through this comparison, the author intends to make the reader reflect on a specific topic. It consists of giving a written opinion about two positions, which are compared between them to conclude. Do you know how to write a comparative essay? In this article, we will explain step by step how to do it.

So, let's see the guidelines that you must follow to achieve a good comparative analysis .

How to write a good comparative analysis

The structure.

In general, the approach is developed in the first paragraph or at the beginning of the work. Its objective is to propose the author's position regarding a specific subject. Generally, this approach specifies the objective to be achieved. You must be clear about what topic you are going to deal with, what you want to explain, and what the perspectives will be to use in your comparative analysis, and you must also define who you write for.

As it is a comparative text, it begins with a general observation that can serve as a context for both approaches, then begins by establishing the arguments in each of the two cases. Do not forget to compare both objects of study according to each argument or idea to develop.

Let it be the reader himself who finds or defines his position in this essay and choose one of the two alternatives.

In this entry, there are two possibilities of approach: one deductive and the other inductive. The deductive method raises the issue, and you use your analysis of the variables leading, guiding the reader to draw their conclusions or fix a position on the issue. While the inductive method starts with argument, developing each of the variables until reaching the topic's approach or problem. The two ways of approaching the subject are viable. Choose the one that is easiest for you to work with.

At the end of this section, your audience should:

  • First of all, have a clear understanding of what topics you will cover in your essay, what you want to explain, and under what positions or perspectives you will do it. It begins with a general observation that establishes the similarity between the two subjects and then moves the essay's focus to the concrete.
  • The reader should understand which points will be examined and which points will not be examined in the comparison. At the end of the introduction, state your preference, or describe the two subjects' meaning.
  • Your readers should be able to describe the ideas you are going to treat. Make a detailed exposition of its characteristics, history, consequences, and development that you consider appropriate. Your comparative analysis should expose the characteristics of the second position on which you want to speak as much as in the first one.

Development of body

Generally, in the body of the essay, the author presents all the arguments that support his thesis, which gives him a reflective and justifying body of the author's initial statement. Depending on the length of the work, which can range from two to 15 pages, each paragraph or before a title corresponds to an argument's development.

After speaking on the subject, the author must close the essay, must conclude, must show the findings of his work, and/or show the conclusions he reached. You must write a final closing paragraph, as a conclusion, in which you expose a confrontation between the two positions. Try to create a fight between them so that the reader gets involved. The conclusion should give a brief and general summary of the most important similarities and differences. It should end with a personal statement, an opinion, and the “what then?” – what is important about the two things being compared.

Readers should be left feeling that all the different threads of this essay have been put together coherently, that they have learned something – and they must be sure that this is the end – that they do not look around for pages missing. And finally, your assessment must explain what position you stand in solidarity and why you prefer it to the other.

Examples of how to write a comparative analysis

Paragraph 1: Messi's preferred position / Ronaldo's preferred position.

Paragraph 2: Messi's play style / Ronaldo's play style.

Paragraph 3: Messi aerial game / Ronaldo aerial game.

Paragraph 1: Messi teamwork .

Paragraph 2: Ronaldo's teamwork.

Paragraph 3: Messi stopped the ball.

Paragraph 4: Ronaldo's stopped the ball.

Paragraph 5: Messi's achievements.

Paragraph 6: Ronaldo's achievements.

Few Important Rules for Comparative analysis

Even if the exercise sounds simple, there are a few rules that should be followed to help your audience as best as possible make the best decision.

1. Clearly state your position

The first question is, “Why are you doing a comparison analysis”? To highlight your view or ideas over another, or simply to compare two (or more) solutions that do not belong to you? It is imperative that you clearly state your position to your reader, so does your credibility.

Be honest and state, for example:

  • The idea you are trying to espouse
  • The framework you are using
  • The reason why you are doing this comparison, the objective

In addition to the above, you must be consistent with the exposition of your ideas.

2. Stay objective

Even if you include your personal ideology in your comparison, stay as objective as possible. Your readers will not appreciate it when you point out all the disadvantages of one idea while you display the advantages of the other. Your comparison will turn into advertising. You have to raise weak points and strong points on both sides.

These analyses are always subjective, so you have to clarify which position convinces you the most.

3. Think about audience' expectations

The research paper is intended for your readers, which means that you must take their expectations into account when writing your review. Put aside your desire to sell your desired idea, and take your readers' perspective:

  • What information are they interested in?
  • What are their criteria?
  • What do they want to know?
  • What do they want from the product or service?

Again, it is about being objective in all your statements.

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4. Organize information

For your readers to want to read your comparative analysis, it is important to structure your comments. The idea is to make it easy for your readers to navigate your paper and get them to find the information that interests them quickly.

5. End with a conclusion

You've tried to be as objective as possible throughout your comparison, and now is the time to let go like we have mentioned many times in this post. In your conclusion, you can go directly to your readers and give your opinion. With a few tips, you can also encourage them to go towards one or the other idea.

Note: If time is not an issue, the best way to review the essay is to leave it for one day. Go for a walk, eat something, have fun, and forget. Then it's time to go back to the text, find problems, and fix them. This must be done separately, that is, first find all the problems you can without correcting them. Although the idea of ​​doing it at the same time is tempting, it is smarter to do it separately. It is effective and fast.

Tips on Comparative analysis

Be concise or accurate in your analysis and dissertation of the topic.

Sometimes the authors believe that the more elaborate the language and the more extensive the writing, the better the writers or essayists. On the contrary, a good essay refers to the exact analysis of a topic, where the reader can dynamically advance the work and understand the author's position.

Use only the arguments necessary for the explanation of the topic, do not talk too much. You run the risk of redundant or repetitive, which makes the text-heavy both when reading it and understanding it.

Write in Short Sentences

Just as we recommend that you do not redound in your texts, we also encourage you to write with short sentences. They give dynamism to the text. Communication is direct . The reader advances in the text and understands much more.

Include Reflections in Your Text

Supporting your approach with reflections or quotes from authors makes your essay more important. Above all, use those arguments that justify or give strength to your position regarding one thesis or the other.

Text Revision

Since comparative analysis can tend to be a subjective work, you must let it “sit” for a day or a few hours and read it again. This exercise will allow you to make corrections. Modify those aspects that are not clear enough for you. And you can improve it, in a few words. Once you do this exercise, just like this, you can submit it.

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COMMENTS

  1. (PDF) A Short Introduction to Comparative Research

    A comparative approach is a methodology that analyses phenomena by putting them together to establish points of similarity and difference between them (Shahrokh & Miri, 2019).

  2. Comparative Research Methods

    A recent synthesis by Esser and Hanitzsch ( 2012a) concluded that comparative communication research involves comparisons between a minimum of two macro-level cases (systems, cultures, markets, or their sub-elements) in which at least one object of investigation is relevant to the field of communication.

  3. 15

    John B. Williamson , David A. Karp and John R. Dalphin Chapter Get access Cite Summary INTRODUCTION In contrast to the chapters on survey research, experimentation, or content analysis that described a distinct set of skills, in this chapter, a variety of comparative research techniques are discussed.

  4. Comparative research

    Comparative research is a research methodology in the social sciences exemplified in cross-cultural or comparative studies that aims to make comparisons across different countries or cultures.

  5. Chapter 10 Methods for Comparative Studies

    Chapter 10 of this book provides an overview of the methods for comparative studies in pharmacology, including the design, analysis, and interpretation of experiments and clinical trials. It also discusses the advantages and limitations of different types of comparisons, such as placebo, active, and dose comparisons. This chapter is a useful resource for researchers and students who want to ...

  6. Frontiers

    Volume 1 - 2019 | https://doi.org/10.3389/fcomp.2019.00007 A Comparative Analysis of Student Performance in an Online vs. Face-to-Face Environmental Science Course From 2009 to 2016 Jasmine Paul * Felicia Jefferson Department of Biology, Fort Valley State University, Fort Valley, GA, United States

  7. PDF COMPARATIVE RESEARCH

    1 2 3 4 6 7 8 Introduction According to Pickvance (2005), comparative analysis is conducted mainly to explain and gain a better understanding of the causal processes involved in the creation of an event, feature or relationship usually by bringing together variations in the explanatory variable or variables.

  8. Comparative Research Methods

    A recent synthesis by Esser and Hanitzsch (2012a) concluded that comparative communication research involves comparisons between a minimum of two macro-level cases (systems, cultures, markets, or their sub-elements) in which at least one object of investigation is relevant to the field of communica-tion.

  9. PDF How to Write a Comparative Analysis

    How to Write a Comparative Analysis Throughout your academic career, you'll be asked to write papers in which you compare and contrast two things: two texts, two theories, two historical figures, two scientific processes, and so on.

  10. [PDF] Comparative research methods

    Comparative research methods. Frank Esser, R. Vliegenthart. Published 1 August 2017. Economics, Sociology, Political Science. In this entry, we discuss the opportunities and challenges of comparative research. We outline the major obstacles in terms of building a comparative theoretical framework, collecting good‐quality data and analyzing ...

  11. Comparative Analysis

    While comparative analysis has its roots in the increased popularity of globalization and innovation in technology, typically the focus of comparative analysis studies has been on explaining distinctions and similarities among variables, usually across geographical borders (Adiyia & Ashton, 2017 ).

  12. Comparing and Contrasting in an Essay

    Revised on July 23, 2023. Comparing and contrasting is an important skill in academic writing. It involves taking two or more subjects and analyzing the differences and similarities between them.

  13. [PDF] Methodology in comparative studies

    Methodology in comparative studies. Published 2011. Education. TLDR. This chapter deals with methodology as the bridge between metatheory, the general higher-level assumptions that underlie researchers' work, and method, the specific practical procedures they use in collecting, analyzing and interpreting data. Expand.

  14. How to Do Comparative Analysis in Research ( Examples )

    October 31, 2021 by Sociology Group Comparative analysis is a method that is widely used in social science. It is a method of comparing two or more items with an idea of uncovering and discovering new ideas about them. It often compares and contrasts social structures and processes around the world to grasp general patterns.

  15. Comparative Research

    Comparative is a research methodology that aims to compare two or more variables that leads to a conclusion. Expand your understanding of this research by downloading the samples that we included in this article. ... If you are going to write an essay for a comparative research examples paper, this section is for you. You must know that there ...

  16. Teaching a comparative approach with eHRAF research papers

    The assignment provides students with sample topics, tips for selecting a research question, the step-by-step process for the development of the research paper, and guidelines for writing the paper. The completed assignment should comprise a minimum of 9 pages, double-spaced, including a title page, and references cited.

  17. The Comparative Essay

    A comparative essay asks that you compare at least two (possibly more) items. These items will differ depending on the assignment. You might be asked to compare positions on an issue (e.g., responses to midwifery in Canada and the United States) theories (e.g., capitalism and communism) figures (e.g., GDP in the United States and Britain)

  18. A comparative genomics multitool for scientific discovery and

    Previously published papers (discussed in the subsections below), and the demonstrated utility of existing comparative genomics resources 16,17, illustrate the benefits of making newly generated ...

  19. Comparative Essay

    A comparative essay is a type of essay in which an essay writer compares at least two or more items. The author compares two subjects with the same relation in terms of similarities and differences depending on the assignment. The main purpose of the comparative essay is to:

  20. Academic Guides: Writing a Paper: Comparing & Contrasting

    Use Clear Transitions. Transitions are important in compare and contrast essays, where you will be moving frequently between different topics or perspectives. Examples of transitions and phrases for comparisons: as well, similar to, consistent with, likewise, too. Examples of transitions and phrases for contrasts: on the other hand, however ...

  21. Comparative Analysis

    Comparative analysis asks writers to make an argument about the relationship between two or more texts. Beyond that, there's a lot of variation, but three overarching kinds of comparative analysis stand out:

  22. 4.1: Introduction to Comparison and Contrast Essay

    The key to a good compare-and-contrast essay is to choose two or more subjects that connect in a meaningful way. Comparison and contrast is simply telling how two things are alike or different. The compare-and-contrast essay starts with a thesis that clearly states the two subjects that are to be compared, contrasted, or both.

  23. Do gender disparities in socioeconomic status affect Teff productivity

    Ethiopia is one of Africa's fastest-growing economies, and the recent political and economic reforms recognize the importance of empowering women and increasing their labour force participation. The Federal Democratic Republic of Ethiopia (FDRE) constitution of 1995 recognized women's right to equality and provides intervened to enable women to compete and participate in all spheres of life ...

  24. A Step-by-Step Guide to Writing a Comparative Analysis

    How to Write a Comparative Analysis. Writing a comparative review in a research paper is not as difficult as many people might tend to think. With some tips, it is possible to write an outstanding comparative review.There are steps that must be utilized to attain this result.