{"id":821,"date":"2026-06-10T05:28:41","date_gmt":"2026-06-10T05:28:41","guid":{"rendered":"https:\/\/www.editage.com\/blog\/?p=821"},"modified":"2026-06-10T05:28:42","modified_gmt":"2026-06-10T05:28:42","slug":"between-subjects-design","status":"publish","type":"post","link":"https:\/\/www.editage.com\/blog\/between-subjects-design\/","title":{"rendered":"Using a Between-Subjects Design in Research: Steps, Examples, Pros &#038; Cons"},"content":{"rendered":"\n<p>Contents<\/p>\n\n\n\n<ul><li><a href=\"#_Toc231981517\">Introduction<\/a><\/li><li><a href=\"#_Toc231981518\">Glossary of Key Terms<\/a><\/li><li><a href=\"#_Toc231981519\">What Is a Between-Subjects Design?<\/a><\/li><li><a href=\"#_Toc231981520\">How a Between-Subjects Design Works<\/a><\/li><li><a href=\"#_Toc231981521\">Examples of Between-Subjects Designs<\/a><\/li><li><a href=\"#_Toc231981522\">Between-Subjects vs. Within-Subjects Design<\/a><\/li><li><a href=\"#_Toc231981523\">Advantages of a Between-Subjects Design<\/a><\/li><li><a href=\"#_Toc231981524\">Disadvantages of a Between-Subjects Design<\/a><\/li><li><a href=\"#_Toc231981525\">Combining Between- and Within-Subjects Designs<\/a><\/li><li><a href=\"#_Toc231981526\">How to Choose: Validity, Causality, and Power<\/a><\/li><li><a href=\"#_Toc231981527\">Frequently Asked Questions<\/a><\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc231981517\">Introduction<\/a><\/h2>\n\n\n\n<p>In any experiment, researchers test the effect of an independent variable by creating different conditions, such as a placebo pill versus a new medication, and then measuring a dependent variable, such as symptom severity. A <strong>between-subjects design<\/strong> (also called a between-groups design, independent measures design, or independent-groups design) is an experimental design in which every participant experiences only one condition. Researchers then compare outcomes between the separate groups of participants.<\/p>\n\n\n\n<p>It is the mirror image of a <a href=\"https:\/\/www.editage.com\/blog\/within-subjects-design\/\">within-subjects design<\/a>, in which every participant experiences every condition. The word <em>between<\/em> signals that you are comparing conditions between different groups of people, whereas the word <em>within<\/em> signals that you are comparing conditions within the same group of people.<\/p>\n\n\n\n<p>This guide defines the design, walks through worked examples from the social sciences and biomedical sciences, weighs its advantages and disadvantages, and explains how to decide whether it is the right choice for your study.<\/p>\n\n\n\n<h2><a id=\"_Toc231981518\">Glossary of Key Terms<\/a><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Term<\/strong><\/td><td><strong>Definition<\/strong><\/td><\/tr><tr><td>Between-subjects design<\/td><td>An experimental design in which each participant is exposed to only one condition, and group differences are compared across participants.<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/insights\/independent-vs-dependent-variables-key-differences-with-examples\">Independent variable<\/a> (IV)<\/td><td>The variable the researcher manipulates or uses to define groups (e.g., drug vs. placebo, slogan A vs. slogan B).<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/insights\/independent-vs-dependent-variables-key-differences-with-examples\">Dependent variable<\/a> (DV)<\/td><td>The outcome the researcher measures (e.g., blood pressure, test scores, sign-up rates).<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/blog\/control-group\/\">Control group<\/a><\/td><td>A group that receives no treatment, a standard treatment, or a fake treatment (placebo), serving as a baseline for comparison.<\/td><\/tr><tr><td>Experimental group<\/td><td>A group that receives the treatment or manipulation of interest.<\/td><\/tr><tr><td>Random assignment<\/td><td>Allocating participants to conditions by chance so each person has an equal probability of ending up in any group.<\/td><\/tr><tr><td>Masking (<a href=\"https:\/\/www.editage.com\/insights\/the-crucial-role-of-blinding-to-avoid-bias-in-research-and-publication\">blinding<\/a>)<\/td><td>Keeping participants (and often researchers) unaware of group assignment to prevent expectations from biasing results.<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/insights\/strategies-to-prevent-the-placebo-effect-from-obscuring-trial-results\">Placebo effect<\/a><\/td><td>Improvement caused by the expectation of receiving treatment rather than the treatment itself.<\/td><\/tr><tr><td>Individual differences<\/td><td>Stable characteristics (age, ability, health status, personality) that vary from person to person and can confound group comparisons.<\/td><\/tr><tr><td>Matching<\/td><td>Pairing or balancing participants across groups on key variables (e.g., age, baseline severity) to make groups more comparable.<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/insights\/importance-of-statistical-power-in-research-design\">Statistical power<\/a><\/td><td>The probability that a study will detect a true effect; influenced by sample size, effect size, and design choice.<\/td><\/tr><tr><td>Internal validity<\/td><td>The degree to which observed differences in the DV can be attributed to the IV rather than to alternative explanations.<\/td><\/tr><tr><td>Carryover effects<\/td><td>Lingering effects of one condition on a later condition; a threat in within-subjects designs that between-subjects designs avoid.<\/td><\/tr><tr><td>Factorial design<\/td><td>A design with two or more independent variables, in which every level of each IV is combined with every level of the others.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><a id=\"_Toc231981519\">What Is a Between-Subjects Design?<\/a><\/h2>\n\n\n\n<p>In a between-subjects design, the sample is divided into separate groups, and each group is exposed to exactly one treatment or condition. The defining features are:<\/p>\n\n\n\n<ul><li><strong>One condition per participant. <\/strong>No participant ever sees more than one level of the independent variable.<\/li><li><strong>Comparison across groups. <\/strong>The analysis compares unrelated (independent) measurements taken from different people.<\/li><li><strong>At least two groups. <\/strong>Typically one or more experimental groups and a control group, or multiple groups that differ on a key characteristic (e.g., age band, diagnosis, education level).<\/li><li><strong>Random assignment whenever possible. <\/strong>Random allocation makes the groups comparable at baseline, so any post-treatment difference can be attributed to the manipulation.<\/li><\/ul>\n\n\n\n<p>If the groups differ significantly on the dependent variable at the end of the study, the researcher can conclude that the manipulation of the independent variable likely caused that difference, provided the groups were equivalent to begin with.<\/p>\n\n\n\n<h2><a id=\"_Toc231981520\">How a Between-Subjects Design Works<\/a><\/h2>\n\n\n\n<h3>Control and experimental groups<\/h3>\n\n\n\n<p>Most between-subjects experiments include at least one control group and one experimental group:<\/p>\n\n\n\n<ul><li><strong>Experimental groups<\/strong> receive the treatment that the researcher predicts will affect the outcome.<\/li><li><strong>Control groups<\/strong> receive no treatment, a standard (usual-care) treatment, or a fake treatment such as a placebo pill or sham procedure, which controls for the placebo effect.<\/li><\/ul>\n\n\n\n<p>The dependent variable is then measured in all groups and compared statistically.<\/p>\n\n\n\n<h3>Random assignment and masking<\/h3>\n\n\n\n<p>Two procedures protect the validity of group comparisons:<\/p>\n\n\n\n<ul><li><strong>Random assignment<\/strong> ensures every participant has an equal chance of landing in any condition, so baseline characteristics (motivation, health, ability) are distributed evenly across groups on average.<\/li><li><strong>Masking (blinding)<\/strong> prevents participants from knowing which group they are in. Participants who guess their assignment may consciously or unconsciously change their behavior to match (or defy) the researcher\u2019s expectations, producing demand characteristics, social desirability bias, self-selection bias, or a Hawthorne effect.<\/li><\/ul>\n\n\n\n<h3>Comparing pre-existing groups<\/h3>\n\n\n\n<p>A between-subjects design is also used when groups differ on a characteristic that cannot be randomly assigned, such as age, gender identity, ethnicity, diagnosis, or test-score band. The characteristic itself serves as the independent variable, every group follows an identical procedure, and there is no separate control or experimental group. Because assignment is not random, these are quasi-experimental comparisons, and causal claims must be made more cautiously.<\/p>\n\n\n\n<h2><a id=\"_Toc231981521\">Examples of Between-Subjects Designs<\/a><\/h2>\n\n\n\n<h3>Example from the social sciences: testing a new slogan<\/h3>\n\n\n\n<p>Suppose you want to test whether a new website slogan (independent variable) increases newsletter sign-ups (dependent variable). You recruit 140 participants and randomly split them into two groups:<\/p>\n\n\n\n<ul><li><strong>Control group: <\/strong>sees the current slogan on the website.<\/li><li><strong>Experimental group: <\/strong>sees the new slogan on the website.<\/li><\/ul>\n\n\n\n<p>You then compare the percentage of sign-ups between the two groups with a statistical test. Because each person sees only one slogan, no one can compare versions or guess the purpose of the study from the contrast itself.<\/p>\n\n\n\n<h3>Example from the social sciences: comparing age groups<\/h3>\n\n\n\n<p>To study whether age influences reaction time on a new cognitive task, you assign participants to groups based on their age: 21\u201330, 31\u201340, and 41\u201350. Every participant completes the identical reaction-time task, and you compare mean reaction times across the three age groups. Here the grouping variable (age band) is the independent variable, and there is no treatment at all.<\/p>\n\n\n\n<h3>Example from the biomedical sciences: a randomized controlled trial<\/h3>\n\n\n\n<p>A pharmaceutical team wants to know whether a new antihypertensive drug lowers systolic blood pressure more than a placebo. In a parallel-group randomized controlled trial (RCT), the classic biomedical application of a between-subjects design:<\/p>\n\n\n\n<ul><li>400 patients with hypertension are randomly assigned to the new drug or to an identical-looking placebo (200 per arm).<\/li><li>The trial is double-blind: neither patients nor clinicians know who receives the active drug.<\/li><li>After 12 weeks, mean systolic blood pressure is compared between the two arms.<\/li><\/ul>\n\n\n\n<p>Randomization balances factors such as age, baseline blood pressure, body mass index, and comorbidities across arms, while double-blinding controls placebo and observer effects. Any reliable difference at follow-up can be attributed to the drug.<\/p>\n\n\n\n<h2><a id=\"_Toc231981522\">Between-Subjects vs. Within-Subjects Design<\/a><\/h2>\n\n\n\n<p>The main alternative is the within-subjects (repeated measures) design, in which the same participants are tested in every condition, often before and after a treatment, so each person serves as their own control. The table below summarizes the key contrasts.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Feature<\/strong><\/td><td><strong>Between-subjects design<\/strong><\/td><td><strong>Within-subjects design<\/strong><\/td><\/tr><tr><td>Conditions per participant<\/td><td>One<\/td><td>All<\/td><\/tr><tr><td>Other names<\/td><td>Independent measures, between-groups<\/td><td>Repeated measures, dependent groups<\/td><\/tr><tr><td>Comparison<\/td><td>Across separate groups of people<\/td><td>Across conditions within the same people<\/td><\/tr><tr><td>Control<\/td><td>Separate control group (no treatment, usual care, or placebo)<\/td><td>Participants act as their own control (e.g., pretest vs. posttest)<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/insights\/an-introduction-to-sample-size-effect-size-and-statistical-power-for-biomedical-researchers\">Sample size<\/a> needed<\/td><td>Larger (often double or more for the same power)<\/td><td>Smaller<\/td><\/tr><tr><td>Session length per person<\/td><td>Shorter (one condition only)<\/td><td>Longer (multiple conditions or time points)<\/td><\/tr><tr><td>Carryover and fatigue effects<\/td><td>Prevented by design<\/td><td>Major threat; require counterbalancing<\/td><\/tr><tr><td>Individual differences<\/td><td>Major threat; managed with randomization and matching<\/td><td>Controlled, since the same people appear in every condition<\/td><\/tr><tr><td>Typical analyses<\/td><td><a href=\"https:\/\/www.editage.com\/insights\/what-biomedical-researchers-need-to-know-about-t-tests\">Independent-samples t test<\/a>, <a href=\"https:\/\/www.editage.com\/blog\/anova-types-uses-assumptions-a-quick-guide-for-biomedical-researchers\/\">one-way ANOVA<\/a>, <a href=\"https:\/\/www.editage.com\/blog\/chi-square-test-types-explained-for-biomedical-researchers\/\">chi-square<\/a><\/td><td><a href=\"https:\/\/www.editage.com\/insights\/what-biomedical-researchers-need-to-know-about-t-tests\">Paired t test<\/a>, <a href=\"https:\/\/www.editage.com\/blog\/anova-types-uses-assumptions-a-quick-guide-for-biomedical-researchers\/\">repeated measures ANOVA<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4>Worked contrast:<\/h4>\n\n\n\n<p>Imagine studying whether a 20-minute nap after a learning session improves test scores. With a between-subjects design, one group naps after learning while a control group does an unrelated task for 20 minutes, and you compare the two groups\u2019 test scores. With a within-subjects design, everyone learns, takes a pretest, naps, and then takes a posttest, and you compare each person\u2019s pretest and posttest scores.<\/p>\n\n\n\n<h2><a id=\"_Toc231981523\">Advantages of a Between-Subjects Design<\/a><\/h2>\n\n\n\n<p>A between-subjects design has fewer threats to internal validity than a within-subjects design, mainly because each participant is tested only once.<\/p>\n\n\n\n<ul><li><strong>No carryover effects. <\/strong>Because no one experiences a second condition, there are no lingering effects of an earlier treatment, including practice or learning effects in which earlier exposure improves later performance.<\/li><li><strong>No fatigue or boredom effects. <\/strong>Participants complete a single, relatively brief session, so they do not tire of repeated treatments.<\/li><li><strong>Shorter sessions and quicker administration. <\/strong>Each participant receives one treatment, so individual sessions are fast and logistically simple, even though the total sample is larger.<\/li><li><strong>Lower risk of participants guessing the <a href=\"https:\/\/www.editage.com\/insights\/everything-you-need-to-know-about-framing-a-research-hypothesis\">hypothesis<\/a>. <\/strong>Without a contrast between conditions to notice, demand characteristics are weaker.<\/li><li><strong>Suits irreversible or one-shot treatments. <\/strong>Surgery, vaccination, a training program, or a one-time persuasive message cannot be \u201cundone\u201d for a second condition, so a between-subjects comparison is the only sensible option.<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc231981524\">Disadvantages of a Between-Subjects Design<\/a><\/h2>\n\n\n\n<ul><li><strong>Requires more participants and resources. <\/strong>To match the statistical power of a within-subjects design, a between-subjects study often needs at least twice as many participants, which means more recruitment, more sessions to run, and higher costs.<\/li><li><strong>Individual differences can threaten validity. <\/strong>Different people provide the data in each condition, so the groups may differ in motivation, ability, health, or demographics, and those differences become rival explanations for any effect.<\/li><li><strong>Less sensitive to small effects. <\/strong>Person-to-person variability inflates the error term, making modest treatment effects harder to detect at a given sample size.<\/li><li><strong>Cannot track change within individuals. <\/strong>Questions about development, learning curves, or disease progression inherently require repeated measurement of the same people.<\/li><\/ul>\n\n\n\n<h3>How to reduce the impact of individual differences<\/h3>\n\n\n\n<p>Several strategies make groups more comparable in a between-subjects design:<\/p>\n\n\n\n<ul><li><strong>Random assignment <\/strong>with an adequately large sample, so chance balances participant characteristics across groups.<\/li><li><strong>Matching, <\/strong>in which individuals or subgroups are paired across conditions on variables likely to affect the outcome, such as age, baseline severity, or ability level.<\/li><li><strong>Blocking or stratified randomization, <\/strong>which randomizes within strata (e.g., within each sex or disease-severity band) so every group contains the same mix.<\/li><li><strong>Measuring covariates <\/strong>at baseline and adjusting for them statistically (e.g., <a href=\"https:\/\/www.editage.com\/blog\/anova-types-uses-assumptions-a-quick-guide-for-biomedical-researchers\/\">ANCOVA<\/a> or <a href=\"https:\/\/www.editage.com\/insights\/choosing-the-right-regression-method-a-handy-guide-for-biomedical-researchers\">regression<\/a>).<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc231981525\">Combining Between- and Within-Subjects Designs<\/a><\/h2>\n\n\n\n<p>The two approaches are not mutually exclusive. When a study has two or more independent variables, it is a factorial design, and each variable can be manipulated in a different way:<\/p>\n\n\n\n<ul><li>In a <strong>factorial design<\/strong>, every level of one independent variable is combined with every level of every other independent variable to create the full set of conditions.<\/li><li>In a <strong>mixed factorial design<\/strong>, one variable is manipulated between subjects and another within subjects. For example, patients might be randomized to a new rehabilitation protocol or usual care (between subjects), with mobility measured before treatment, at discharge, and at six-month follow-up (within subjects).<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc231981526\">How to Choose: Validity, Causality, and Power<\/a><\/h2>\n\n\n\n<p>Methodologists recommend weighing three factors when choosing between a between-subjects and a within-subjects design, rather than defaulting to whichever needs fewer participants:<\/p>\n\n\n\n<ul><li><strong>Validity. <\/strong>Within-subjects designs face carryover effects, participant awareness of the manipulation, and repeated-measurement artifacts; between-subjects designs avoid all of these.<\/li><li><strong>Causality. <\/strong>The assumptions needed for causal inference differ. Between-subjects designs lean on the assumption of no confounding between groups (secured by randomization), while within-subjects designs require the assumption of no carryover from one condition to the next.<\/li><li><strong>Statistical power. <\/strong>Simulation work on mediation analysis shows within-subjects designs typically need roughly half the sample of between-subjects designs to detect the same effect, though the advantage varies with effect sizes and the correlation between repeated measures. Higher power alone should not decide the design; validity and causality considerations can favor a between-subjects approach.<\/li><\/ul>\n\n\n\n<p>Data from a simple two-group between-subjects experiment are usually analyzed with an independent-samples t test (two groups), a one-way ANOVA (three or more groups), or a chi-square test (categorical outcomes such as sign-up vs. no sign-up).<\/p>\n\n\n\n<h2><a id=\"_Toc231981527\">Frequently Asked Questions<\/a><\/h2>\n\n\n\n<h3>What is the difference between a between-subjects design and a within-subjects design?<\/h3>\n\n\n\n<p>In a between-subjects design, every participant experiences only one condition, and researchers compare group differences between participants assigned to different conditions. In a within-subjects design, each participant experiences all conditions, and researchers test the same people repeatedly to compare conditions. \u201cBetween\u201d means comparing conditions between groups; \u201cwithin\u201d means comparing conditions within the same group.<\/p>\n\n\n\n<h3>How many participants do you need for a between-subjects experiment?<\/h3>\n\n\n\n<p>There is no universal number; it depends on the expected effect size, the desired power (commonly 80%), and the significance level. As a rule of thumb, a between-subjects design needs at least double the participants of an equivalent within-subjects design to reach the same power, because between-person variability adds noise. Run a <a href=\"https:\/\/www.editage.com\/insights\/importance-of-statistical-power-in-research-design\">formal power analysis<\/a> (e.g., in G*Power or R) before collecting data.<\/p>\n\n\n\n<h3>Is a randomized controlled trial an example of a between-subjects design?<\/h3>\n\n\n\n<p>Yes. A standard parallel-group RCT, in which patients are randomly assigned to a treatment arm or a control\/placebo arm and each patient receives only one intervention, is a between-subjects design. (A crossover trial, by contrast, is a within-subjects design because every patient receives every intervention in sequence.)<\/p>\n\n\n\n<h3>When should you use a between-subjects design instead of a within-subjects design?<\/h3>\n\n\n\n<p>Choose a between-subjects design when carryover, practice, or fatigue effects would contaminate repeated testing; when the treatment is irreversible (surgery, vaccination, training); when comparing naturally existing groups (age bands, diagnoses); when sessions must be short; or when participants must not be able to infer the hypothesis by contrasting conditions. Choose it only if you can recruit a large enough sample for adequate power.<\/p>\n\n\n\n<h3>Can you combine between-subjects and within-subjects designs in the same study?<\/h3>\n\n\n\n<p>Yes. With two or more independent variables you can use a factorial design, and in a mixed factorial design one variable is manipulated between subjects while another is measured within subjects, for example, randomizing students to teaching methods (between) and testing them before, midway through, and after the course (within).<\/p>\n","protected":false},"excerpt":{"rendered":"Contents Introduction Glossary of Key Terms What Is a Between-Subjects Design? How a Between-Subjects Design Works Examples of Between-Subjects Designs Between-Subjects vs. Within-Subjects Design Advantages of a Between-Subjects Design Disadvantages of a Between-Subjects Design Combining Between- and Within-Subjects Designs How to Choose: Validity, Causality, and Power Frequently Asked Questions Introduction In any experiment, researchers test [&hellip;]","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[14],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Using a Between-Subjects Design in Research: Steps, Examples, Pros &amp; Cons<\/title>\n<meta name=\"description\" content=\"Learn what is a between-subjects design, difference from within-subjects design, pros and cons of a between-subjects design, with worked examples\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.editage.com\/blog\/between-subjects-design\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Using a Between-Subjects Design in Research: Steps, Examples, Pros &amp; Cons\" \/>\n<meta property=\"og:description\" content=\"Learn what is a between-subjects design, difference from within-subjects design, pros and cons of a between-subjects design, with worked examples\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.editage.com\/blog\/between-subjects-design\/\" \/>\n<meta property=\"og:site_name\" content=\"Educational Articles For Researchers, Students And Authors - Editage Blog\" \/>\n<meta property=\"article:published_time\" content=\"2026-06-10T05:28:41+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-06-10T05:28:42+00:00\" \/>\n<meta name=\"author\" content=\"Marisha Rodrigues\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Marisha Rodrigues\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"11 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.editage.com\/blog\/between-subjects-design\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.editage.com\/blog\/between-subjects-design\/\"},\"author\":{\"name\":\"Marisha Rodrigues\",\"@id\":\"https:\/\/www.editage.com\/blog\/#\/schema\/person\/60d7626072744221b2260692486b6ff1\"},\"headline\":\"Using a Between-Subjects Design in Research: Steps, Examples, Pros &#038; Cons\",\"datePublished\":\"2026-06-10T05:28:41+00:00\",\"dateModified\":\"2026-06-10T05:28:42+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.editage.com\/blog\/between-subjects-design\/\"},\"wordCount\":2367,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.editage.com\/blog\/#organization\"},\"articleSection\":[\"Get Published\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.editage.com\/blog\/between-subjects-design\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.editage.com\/blog\/between-subjects-design\/\",\"url\":\"https:\/\/www.editage.com\/blog\/between-subjects-design\/\",\"name\":\"Using a Between-Subjects Design in Research: Steps, Examples, Pros & Cons\",\"isPartOf\":{\"@id\":\"https:\/\/www.editage.com\/blog\/#website\"},\"datePublished\":\"2026-06-10T05:28:41+00:00\",\"dateModified\":\"2026-06-10T05:28:42+00:00\",\"description\":\"Learn what is a between-subjects design, difference from within-subjects design, pros and cons of a between-subjects design, with worked examples\",\"breadcrumb\":{\"@id\":\"https:\/\/www.editage.com\/blog\/between-subjects-design\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.editage.com\/blog\/between-subjects-design\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.editage.com\/blog\/between-subjects-design\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.editage.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Using a Between-Subjects Design in Research: Steps, Examples, Pros &#038; Cons\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.editage.com\/blog\/#website\",\"url\":\"https:\/\/www.editage.com\/blog\/\",\"name\":\"Educational Articles For Researchers, Students And Authors - Editage Blog\",\"description\":\"Get insightful educational articles from the world of academia for researchers, students and authors. 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