{"id":23564,"date":"2024-06-19T14:28:04","date_gmt":"2024-06-19T14:28:04","guid":{"rendered":"http:\/\/staging.avdheshsharma.com\/ensuring-parity-between-experimental-and-control-groups-useful-statistical-techniques-researchers-need-to-know\/"},"modified":"2026-04-09T09:13:38","modified_gmt":"2026-04-09T03:43:38","slug":"ensuring-parity-between-experimental-and-control-groups-useful-statistical-techniques-researchers-need-to-know","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/ensuring-parity-between-experimental-and-control-groups-useful-statistical-techniques-researchers-need-to-know","title":{"rendered":"How to match experimental and control groups"},"content":{"rendered":"

What are experimental and control groups?<\/a><\/p>\n

Why must the experimental and control groups be similar\/matched?<\/a><\/p>\n

Strategies to Balance Your Experimental and Control Groups<\/a><\/p>\n

Randomization<\/a><\/p>\n

What is randomization?<\/a><\/p>\n

Propensity Score Matching<\/a><\/p>\n

What is propensity score matching?<\/a><\/p>\n

Further reading<\/a><\/p>\n

Exact Matching<\/a><\/p>\n

What is exact matching?<\/a><\/p>\n

Coarsened Exact Matching<\/a><\/p>\n

What is coarsened exact matching?<\/a><\/p>\n

Further reading<\/a><\/p>\n

Mahalanobis Distance Matching<\/a><\/p>\n

What is Mahalanobis distance matching?<\/a><\/p>\n

Inverse Probability Weighting<\/a><\/p>\n

What is inverse probability weighting?<\/a><\/p>\n

Further reading<\/a><\/p>\n

<\/h2>\n

<\/a>What are experimental and control groups?<\/h2>\n

In your study, you often need to divide your sample into two or more groups. You administer the intervention or \u201cexperiment\u201d on one group, and compare their results with those of the other group (your \u201ccontrol\u201d group). The group that receives the intervention or the variable you are testing is sometimes termed the \u201cexperimental group.\u201d<\/p>\n

<\/a>Why must the experimental and control groups be similar\/matched?<\/h2>\n

If the experimental and control groups are not <\/em>similar to each other, you have some more variables (\u201cconfounders\u201d) that can affect your results.<\/p>\n

For example, suppose you\u2019re examining the effects of classical music on short-term memory. Your experimental group listens to Mozart and Beethoven before performing a task designed to test short-term memory performance. The control group doesn\u2019t listen to any music before completing the same task. If the experimental group performs better on average than the control group, you\u2019ve got evidence that listening to classical music can improve short-term memory. However, if the experimental group was significantly younger than the control group, there\u2019s another variable—age—that might have affected the experimental group\u2019s performance. Here, age is a confounding variable, because it affects or obscures your results.<\/p>\n

For this reason, if you want to compile high-quality and powerful evidence in your research, you need to ensure that your experimental and control groups are \u201cmatched\u201d on all important variables. That\u2019s why, we\u2019ve put together a handy list of statistical techniques that you can use for this kind of matching and thus generate robust, high-quality statistical results.<\/p>\n

 <\/p>\n

<\/a>Strategies to Balance Your Experimental and Control Groups<\/h2>\n

<\/a>Randomization<\/h3>\n

<\/a>What is randomization?<\/h3>\n

Here, you randomly assign subjects to experimental and control groups. This method minimizes pre-existing differences between groups by relying on chance.<\/p>\n

 <\/p>\n

<\/a>Propensity Score Matching<\/h3>\n

<\/a>What is propensity score matching?<\/h3>\n

Using this method, you calculate the probability that each subject will be assigned to the experimental group based on observed characteristics, and then match subjects with similar probabilities from each group.<\/p>\n

<\/a>Further reading<\/h3>\n

Chen et al. (2022)<\/a> have compiled some useful guidelines on using propensity score matching.<\/p>\n

 <\/p>\n

<\/a>Exact Matching<\/h3>\n

<\/a>What is exact matching?<\/h3>\n

Just what it sounds like: you have to match subjects in the experimental group to subjects in the control group who have exactly the same characteristics. This is somewhat like finding an identical twin for every member of your experimental group, and can be pretty difficult to implement in real life.<\/p>\n

 <\/p>\n

<\/a>Coarsened Exact Matching<\/h3>\n

<\/a>What is coarsened exact matching?<\/h3>\n

Easier to implement that exact matching, this method involves grouping subjects into bins based on their characteristics and then matching subjects from each bin.<\/p>\n

<\/a>Further reading<\/h3>\n

Ripollone et al. (2020)<\/a> compared the utility of coarsened exact matching versus other techniques like propensity score matching and Mahalanobis distance matching, and found that although coarsened exact matching is not optimal, it is a useful alternative when there are less than 10 strong confounders in the study.<\/p>\n

 <\/p>\n

<\/a>Mahalanobis Distance Matching<\/h3>\n

<\/a>What is Mahalanobis distance matching?<\/h3>\n

Mahalanobis distance measures similarity considering correlations among characteristics. When you\u2019re using Mahalanobis distance matching, you have to use the Mahalanobis distance to find matches, and thus you\u2019re taking into account the correlations between characteristics. Simply put, you match people by considering how certain traits relate to each other, not just how similar each trait is.<\/p>\n

 <\/p>\n

<\/a>Inverse Probability Weighting<\/h3>\n

<\/a>What is inverse probability weighting?<\/h3>\n

Inverse probability weighting ensures groups are comparable by assigning weights to each subject based on their likelihood of being in the experimental or control group. This way, subjects less likely to be in their group are given more weight, balancing the groups and making comparisons fairer and more accurate.<\/p>\n

<\/a>Further reading<\/h3>\n

If you\u2019re interested in getting started with this method, you can refer to Chesnaye et al. (2021)<\/a>, especially for longitudinal or observational research.<\/p>\n

 <\/p>\n

Get help from an expert biostatistician in ensuring your experimental and control groups are adequately matched. Consult Editage’s Statistical Analysis & Review Service<\/a>.<\/em><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

What are experimental and control groups? Why must the experimental and control groups be similar\/matched? Strategies to Balance Your Experimental and Control Groups Randomization What is randomization? Propensity Score Matching What is propensity score matching? Further reading Exact Matching What is exact matching? Coarsened Exact Matching What is coarsened exact matching? Further reading Mahalanobis Distance […]<\/p>\n","protected":false},"author":15,"featured_media":28085,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2420],"tags":[1319],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-23564","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analysis","tag-statistical-analysis"],"acf":[],"yoast_head":"\nHow to match experimental and control groups | Editage Insights<\/title>\n<meta name=\"description\" content=\"Learn useful statistical techniques to match experimental and control groups: propensity score matching, coarsened exact matching, etc.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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