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- SPSS Tutorials: Paired Samples t Test
- Paired Sample t Test
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- Dependent T-Test using SPSS Statistics

Our tutorials reference a dataset called "sample" in many examples. If you'd like to download the sample dataset to work through the examples, choose one of the files below:. The Paired Samples t Test compares the means of two measurements taken from the same individual, object, or related units. These "paired" measurements can represent things like:. The purpose of the test is to determine whether there is statistical evidence that the mean difference between paired observations is significantly different from zero.

The Paired Samples t Test is a parametric test. Note: The Paired Samples t Test can only compare the means for two and only two related paired units on a continuous outcome that is normally distributed. Note: When testing assumptions related to normality and outliers, you must use a variable that represents the difference between the paired values - not the original variables themselves.

Note: When one or more of the assumptions for the Paired Samples t Test are not met, you may want to run the nonparametric Wilcoxon Signed-Ranks Test instead. The hypotheses can be expressed in two different ways that express the same idea and are mathematically equivalent:.

The test statistic for the Paired Samples t Test, denoted t , follows the same formula as the one sample t test. If the calculated t value is greater than the critical t value, then we reject the null hypothesis and conclude that the means are significantly different. Your data should include two continuous numeric variables represented in columns that will be used in the analysis. The two variables should represent the paired variables for each subject row.

If your data are arranged differently e. The Paired-Samples T Test window opens where you will specify the variables to be used in the analysis. All of the variables in your dataset appear in the list on the left side. Move variables to the right by selecting them in the list and clicking the blue arrow buttons. You will specify the paired variables in the Paired Variables area. You may choose to run multiple Paired Samples t Tests simultaneously by selecting multiple sets of matched variables.

Each new pair will appear on a new line. B Variable1: The first variable, representing the first group of matched values. C Variable2: The second variable, representing the second group of matched values. D Options : Clicking Options will open a window where you can specify the Confidence Interval Percentage and how the analysis will address Missing Values i.

The sample dataset has placement test scores out of points for four subject areas: English, Reading, Math, and Writing. Suppose we are particularly interested in the English and Math sections, and want to determine whether English or Math had higher test scores on average. We could use a paired t test to test if there was a significant difference in the average of the two tests. Variable English has a high of The mean English score is much higher than the mean Math score Additionally, there were cases with non-missing English scores, and cases with non-missing Math scores, but only cases with non-missing observations for both variables.

Recall that the sample dataset has cases in all. Let's create a comparative boxplot of these variables to help visualize these numbers. We'll also need to tell SPSS to put these two variables on the same chart. Click the Plots button, and in the Boxplots area, change the selection to Dependents Together. You can also uncheck Stem-and-leaf. Click Continue. Then click OK to run the procedure.

We can see from the boxplot that the center of the English scores is much higher than the center of the Math scores, and that there is slightly more spread in the Math scores than in the English scores. Both variables appear to be symmetrically distributed.

It's quite possible that the paired samples t test could come back significant. Paired Samples Statistics gives univariate descriptive statistics mean, sample size, standard deviation, and standard error for each variable entered. Notice that the sample size here is ; this is because the paired t-test can only use cases that have non-missing values for both variables. Paired Samples Correlations shows the bivariate Pearson correlation coefficient with a two-tailed test of significance for each pair of variables entered.

Paired Samples Test gives the hypothesis test results. The Paired Samples Statistics output repeats what we examined before we ran the test. For more information about correlation, check out the Pearson Correlation tutorial. Search this Guide Search. Paired Samples t Test The Paired Samples t Test compares the means of two measurements taken from the same individual, object, or related units.

These "paired" measurements can represent things like: A measurement taken at two different times e. This test is also known as: Dependent t Test Paired t Test Repeated Measures t Test The variable used in this test is known as: Dependent variable, or test variable continuous , measured at two different times or for two related conditions or units.

Common Uses The Paired Samples t Test is commonly used to test the following: Statistical difference between two time points Statistical difference between two conditions Statistical difference between two measurements Statistical difference between a matched pair Note: The Paired Samples t Test can only compare the means for two and only two related paired units on a continuous outcome that is normally distributed.

To compare unpaired means between more than two groups on a continuous outcome that is normally distributed, choose ANOVA. To compare paired means for continuous data that are not normally distributed, choose the nonparametric Wilcoxon Signed-Ranks Test. To compare paired means for ranked data, choose the nonparametric Wilcoxon Signed-Ranks Test. Data Requirements Your data must meet the following requirements: Dependent variable that is continuous i.

This means that the subjects in the first group are also in the second group. Random sample of data from the population Normal distribution approximately of the difference between the paired values No outliers in the difference between the two related groups Note: When testing assumptions related to normality and outliers, you must use a variable that represents the difference between the paired values - not the original variables themselves.

Test Statistic The test statistic for the Paired Samples t Test, denoted t , follows the same formula as the one sample t test. Data Set-Up Your data should include two continuous numeric variables represented in columns that will be used in the analysis. Setting the confidence interval percentage does not have any impact on the calculation of the p-value.

If you are only running one paired samples t test, the two "missing values" settings will produce the same results. There will only be differences if you are running 2 or more paired samples t tests.

This would look like having two or more rows in the main Paired Samples T Test dialog window. Example Problem Statement The sample dataset has placement test scores out of points for four subject areas: English, Reading, Math, and Writing.

Before the Test Variable English has a high of Then select the variable Math and move it to the Variable2 slot in the Paired Variables box. Click OK.

Reading from left to right: First column: The pair of variables being tested, and the order the subtraction was carried out. If you have specified more than one variable pair, this table will have multiple rows.

Mean: The average difference between the two variables. Standard deviation: The standard deviation of the difference scores. Standard error mean: The standard error standard deviation divided by the square root of the sample size. Used in computing both the test statistic and the upper and lower bounds of the confidence interval. On average, English scores were Report a problem. Subjects: Statistical Software. Tags: statistics , tutorials. University Libraries. Street Address Risman Dr. Contact Us library kent.

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We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience, read our Cookie Policy. Two-sample t-tests are statistical tests used to compare the means of two populations. These statistical tests are commonly used in research in the fields of biology, business, and psychology. This article will explain when it is appropriate to use a paired t-test versus an unpaired t-test, as well as the hypothesis and assumptions of each.

The dependent t-test called the paired-samples t-test in SPSS Statistics compares the means between two related groups on the same continuous, dependent variable. For example, you could use a dependent t-test to understand whether there was a difference in smokers' daily cigarette consumption before and after a 6 week hypnotherapy programme i. If your dependent variable is dichotomous, you should instead use McNemar's test. This "quick start" guide shows you how to carry out a dependent t-test using SPSS Statistics, as well as interpret and report the results from this 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 dependent t-test to give you a valid result. We discuss these assumptions next.

Our tutorials reference a dataset called "sample" in many examples. If you'd like to download the sample dataset to work through the examples, choose one of the files below:. The Paired Samples t Test compares the means of two measurements taken from the same individual, object, or related units.

Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. An investigator wishes to produce a combined analysis of several datasets.

Open topic with navigation. This function gives an unpaired two sample Student t test with a confidence interval for the difference between the means. The unpaired t method tests the null hypothesis that the population means related to two independent, random samples from an approximately normal distribution are equal Altman, ; Armitage and Berry,

Statistics: Unpaired t-tests. Rosie Shier. 1 Introduction. An unpaired t-test is used to compare two population means. The following notation will.

Language: English Chinese. In clinical research, comparisons of the results from experimental and control groups are often encountered. The two-sample t -test also called independent samples t -test and the paired t -test are probably the most widely used tests in statistics for the comparison of mean values between two samples. However, confusion exists with regard to the use of the two test methods, resulting in their inappropriate use. In this paper, we discuss the differences and similarities between these two t -tests. Three examples are used to illustrate the calculation procedures of the two-sample t -test and paired t -test.

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A paired t-test is used when we are interested in the difference between two variables for the same subject. Often the two variables are separated by time. For example, in the Dixon and Massey data set we have cholesterol levels in and cholesterol levels in for each subject.

In paired sample hypothesis testing, a sample from the population is chosen and two measurements for each element in the sample are taken.

Genevre D. 28.04.2021 at 11:09A paired t-test is used to compare two population means where you have two samples in which observations in one sample can be paired with observations in.