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STATA 12 STUDENT HOW TO

For example, you might have measured 50 participants' typing speed using a keyboard (i.e., the dependent variable) before and after they underwent a touch-typing course designed to improve typing speed (i.e., the two "time points" where participants' typing speed was measured – "before" and "after" the touch-typing course – reflect the two "related groups" of the independent variable). The reason that it is possible to have the same subjects in each group is because each subject has been measured on two occasions on the same dependent variable. "Related groups" indicates that the same subjects are present in both groups.

Assumption #2: Your independent variable should consist of two categorical, "related groups" or "matched pairs".

If you are unsure whether your dependent variable is continuous (i.e., measured at the interval or ratio level), see our Types of Variable guide. Examples of such dependent variables include height (measured in feet and inches), temperature (measured in oC), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), reaction time (measured in milliseconds), test performance (measured from 0 to 100), sales (measured in number of transactions per month), and so forth.

Assumption #1: Your dependent variable should be measured at the interval or ratio level (i.e., they are continuous).

However, you should decide whether your study meets these assumptions before moving on. Since assumptions #1 and #2 relate to your study design and choice of variables, they cannot be tested for using Stata. If any of these four assumptions are not met, you cannot analyse your data using a paired t-test because you will not get a valid result.

There are four "assumptions" that underpin the paired t-test. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a paired t-test to give you a valid result.

STATA 12 STUDENT HOW TO

In this guide, we show you how to carry out a paired t-test using Stata, as well as interpret and report the results from this test. Note: In Stata 12, you will see that the paired t-test is referred to as the "Mean-comparison test, paired data", whereas in Stata 13, it comes under "t test (mean-comparison tests)". Specifically, you use a paired t-test to determine whether the mean difference between two groups is statistically significantly different to zero. Alternately, you could use a paired t-test to understand whether there was a difference in smokers' daily cigarette consumption 6 week after wearing nicotine patches compared with wearing patches that did not contain nicotine, known as a "placebo" (i.e., your dependent variable would be "daily cigarette consumption", and your two related groups would be the two different "conditions" participants were exposed to that is, cigarette consumption values after wearing "nicotine patches" (the treatment group) compared to after wearing the "placebo" (the control group)). For example, you could use a paired t-test to understand whether there was a difference in managers' salaries before and after undertaking a PhD (i.e., your dependent variable would be "salary", and your two related groups would be the two different "time points" that is, salaries "before" and "after" undertaking the PhD). The paired t-test, also referred to as the paired-samples t-test or dependent t-test, is used to determine whether the mean of a dependent variable (e.g., weight, anxiety level, salary, reaction time, etc.) is the same in two related groups (e.g., two groups of participants that are measured at two different "time points" or who undergo two different "conditions").