A new window will appear. Decide whether to reject or retain the null hypothesis. The independent, or unpaired, t-test is a statistical measure of the difference between the means of two independent and identically distributed samples. Whilst there are many different ways you can do this, we show you how to calculate effect sizes from your SPSS Statistics results in our enhanced independent t-test guide. The Independent Sample T-Test in SPSS The independent samples t-test, or Student's t-test, is the most popular test to test for the difference in means. Throughout this tutorial, the reader will be guided through importing datafiles, exploring summary statistics and conducting a T-test. Therefore, a researcher decided to investigate whether an exercise or weight loss intervention is more effective in lowering cholesterol levels. If you use Stata rather than SPSS Statistics, we have a "quick start" guide on how to run an independent t-test here. Figure 7 presents the two Sex groups, Female and Male. Now Athlete is defined … We discuss these assumptions next. SPSS Statistics generates two main tables of output for the independent t-test. Note: If you have more than 2 treatment groups in your study (e.g., 3 groups: diet, exercise and drug treatment groups), but only wanted to compared two (e.g., the diet and drug treatment groups), you could type in 1 to Group 1: box and 3 to Group 2: box (i.e., if you wished to compare the diet with drug treatment). Copyright 2021 Leaf Group Ltd. / Leaf Group Media, All Rights Reserved. This sample was then randomly split into two groups: Group 1 underwent a calorie-controlled diet and Group 2 undertook the exercise-training programme. Independent Samples T-Test - Assumptions. However, in this "quick start" guide, we take you through each of the two main tables in turn, assuming that your data met all the relevant assumptions. “An independent-samples t-test was conducted to compare memory for words in sugar and no sugar conditions. Interpret the confidence interval. For example, you could use an independent t-test to understand whether first year graduate salaries differed based on gender (i.e., your dependent variable would be "first year graduate salaries" and your independent variable would be "gender", which has two groups: "male" and "female"). Presented below are SPSS results for the independent samples t-test. 2. Before we introduce you to these six assumptions, do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated (i.e., is not met). First, let's take a look at these six assumptions: You can check assumptions #4, #5 and #6 using SPSS Statistics. Sig (2-Tailed) value . Types of t-test. One of the most recognized statistical programs is SPSS, which generates a variety of test results for sets of data. The single sample t-test tests the null hypothesis that the population meanis equal to the number specified by the user. SPSS: Independent Samples t-test Results . An Independent Samples t test compares the averages of two groups whose … In order for the results of the study to be good, the subjects studied should be randomly selected for both groups being compared. Independent Samples t-test in SPSS Tests the mean difference between two independent groups; for example, left-handed people will score higher on an IQ test, on average, then right-handed people. The next stage is to get the PreTest and PostTest variables over from the left box into their respective boxes … If the p-value associated with the t-test is small (0.05 isoften used as the threshold), there is evidence that the mean is different fromthe hypothesized value. Report on the p value and the confidence interval. Join the 10,000s of students, academics and professionals who rely on Laerd Statistics. This value gives an interval for which, with 95% certainty, you would predict the difference in the actual population to be based on your results. Once you click Independent-Samples T Test, the following window will appear: Drag the mpg into the box labelled Test Variable (s) and group into the box labelled Grouping Variable. These results suggest that sugar really does have an effect on memory for words. Both tests require the homogeneity assumption : the population variances of the dependent variable must equal for all groups. This test computes a t value for the data that is then related to a p-value for the determination of significance. All of the variables in your dataset appear in the list on the left side. First, we set out the example we use to explain the independent t-test procedure in SPSS Statistics. This test computes a t value for the data that is then related to a p-value for the determination of significance. Just remember that if you do not run the statistical tests on these assumptions correctly, the results you get when running an independent t-test might not be valid. (2-tailed)" row is less than 0.05. You can learn about our enhanced data setup content in general here. Alternately, you could use an independent t-test to understand whether there is a difference in test anxiety based on educational level (i.e., your dependent variable would be "test anxiety" and your independent variable would be "educational level", which has two groups: "undergraduates" and "postgraduates"). https://www.onlinespss.com/how-to-run-independent-sample-t-test-in-spss To begin the paired samples t test, click on Analyze -> Compare Means -> Paired-Samples T Test. 1 The Independent-Samples and Paired-Samples t Tests in SPSS versions 21-22 This guide uses the Rikers 1989 data set for Independent Samples test and the NELS dataset for the Paired Samples test. Use the SPSS to calculate an independent-samples t test. Case analysis was demonstrated, which included a dependent variable (crime rate) and independent variables (education, implementation of penalties, confidence in the police, and the promotion of illegal activities). To interpret the t-test results, all you need to find on the output is the p-value for the test. All t Tests are means comparisons: A One Sample t test compares a sample average to a population mean. You will need to copy and paste these into your Application document. A previous article explained how to interpret the results obtained in the correlation test. This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out an independent t-test when everything goes well! Step by Step Independent Samples T-Test in SPSS 21 | The main use of Independent sample t test is to compare the average of two case groups. The standard deviation identifies how close the set of data points within each test group are to their respective means. The Independent Sample T-Test in SPSS The independent samples t-test, or Student's t-test, is the most popular test to test for the difference in means. Independent Samples t-test in SPSS Tests the mean difference between two independent groups; for example, left-handed people will score higher on an IQ test, on average, then right-handed people. Find the standard deviation values and relate them to the data sets. All the tests in the t-test family compare differences in mean scores of continuous-level (interval or ratio), normally distributed data. This "quick start" guide shows you how to carry out an independent t-test using SPSS, as well as interpret and report the results from this test. In our enhanced independent t-test guide, we show you how to correctly enter data in SPSS Statistics to run an independent t-test (see here). This can be useful when you have missing values and the number of recruited participants is larger than the number of participants that could be analysed. Interpret the N values as the number of samples tested in each of the two groups for the t-test. The one-tailed test is appropriate when there is a difference between groups in a specific direction [].It is less common than the two-tailed test, so the rest of the article focuses on this one.. 3. If you lower the concentration of cholesterol in the blood, your risk of developing heart disease can be reduced. With an independent-samples t test, each case must have scores on two variables, the grouping (independent) variable and the test (dependent) variable. Then click Define Groups and define Group 1 as the rows with value 0 and define Group 2 as the rows with value 1. The simplicity of interpretation: the results of the independent sample T-Test presents the difference in the mean values of one sample group from the mean of other groups and also tells that whether the difference is statistically significant or not. For example, comparing height differences between a sample of male and females. Thus, a narrower confidence interval provides more conclusive results and a better estimation of the actual population than a broader confidence interval. This is a test for making an inference to population parameters (viz., population mean IQs; H o: ì left = ì right). An independent t-test, also known as an unpaired t-test, is a parametric statistical test used to determine if there are any differences between two continuous variables on the same scale from two unrelated groups. Use the SPSS to calculate an independent-samples t test. Observe the 95% Confidence Interval of the Difference section of the table. However, don't worry. The SPSS Statistics tend to generate two main output tables while conducting the independent T-Test. This is done if there is a difference in the answer because of the treatment or lack of treatment, not because of other factors. Based on the results above, you could report the results of the study as follows (N.B., this does not include the results from your assumptions tests or effect size calculations): This study found that overweight, physically inactive male participants had statistically significantly lower cholesterol concentrations (5.80 ± 0.38 mmol/L) at the end of an exercise-training programme compared to after a calorie-controlled diet (6.15 ± 0.52 mmol/L), t(38)=2.428, p=0.020. To this end, the researcher recruited a random sample of inactive males that were classified as overweight. an independent samples t-test for 2 groups or a one-way ANOVA for 3+ groups . Interpreting results of independent-samples t-test Check homogeneity of variances Levene's Test for Equality of Variances tests whether the variance of scores for the two groups is the same, and determines which of the t-values is the correct to use. Add the test variable (Height in this case) into the Test Variable(s): window.Also, add the grouping variable (Group in this case) to the Grouping Variable: input.The window should now … For each variable, we'll use a t-test to evaluate if the mean scores are different between our 2 groups of children. When you choose to analyse your data using an independent t-test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using an independent t-test. Providing an effect size in your results helps to overcome this limitation. This table reports general descriptive statistical values such as mean, standard deviation, etc. A smaller standard error indicates that the mean is more likely to be that of the true population. Figure 7 Results: Descriptive Statistics per Group. An Independent Samples t test compares the averages of two groups whose … However, since you should have tested your data for these assumptions, you will also need to interpret the SPSS Statistics output that was produced when you tested for them (i.e., you will have to interpret: (a) the boxplots you used to check if there were any significant outliers; (b) the output SPSS Statistics produces for your Shapiro-Wilk test of normality to determine normality; and (c) the output SPSS Statistics produces for Levene's test for homogeneity of variances). Since this test is equivalent to a two group one-way analysis of variance, or a linear regression with a binary predictor variable, you would use the CSGLM procedure (General Linear Model in the CS menus) to obtain these results. Looking at the Group Statistics table, we can see that those people who undertook the exercise trial had lower cholesterol levels at the end of the programme than those who underwent a calorie-controlled diet. The column is denoted as “Sig. As you can see, there are three variables. Figure 7 presents the two Sex groups, Female and Male. This will bring up the paired-samples t test dialog box. This table gives the actual results from the t-test. This is a test for making an inference to population parameters (viz., population mean IQs; H o: ì left = ì right). After carrying out an independent-samples t-test in the previous section, SPSS Statistics displays the results in the IBM SPSS Statistics Viewer using two tables: the Group Statistics table and the Independent Samples Test table. Here you need to tell SPSS which data you want to include in the independent t-test. The one-sample t-test is a member of the t-test family. This value is calculated from the standard deviation and sample size of the population and identifies the precision of the mean of each sample. Here, we will exclusively focus on Bayesian statistics. You need to do this because it is only appropriate to use an independent t-test if your data "passes" six assumptions that are required for an independent t-test to give you a valid result. PostPEF – posttest peak expiratory flow (measured in litres per minute).It’s a paired subjects design, with a repeated measure being taken for each subject.We want to find out if there is a difference between the mean pretest PEF a… Sex – Male or Female. Effect sizes are important because whilst the independent t-test tells you whether differences between group means are "real" (i.e., different in the population), it does not tell you the "size" of the difference. The data used in this tutorial can be downloaded from this GitHub repository.The one-sample and independent samples examples will use the iq_long.sav data, and the paired samples example will use iq_wide.sav. Identify the p-values in “t-test for Equality of Means” section of the table to determine significance. Being overweight and/or physically inactive increases the concentration of cholesterol in your blood. He was also authored a paper for a medical journal exploring current recommendations for bone scans to diagnose osteoporosis. If the p-value associated with the t-test is not small(p > 0.05), then the null hypothesis is not rejected and … Most studies are performed on a 95% confidence interval; thus, a p-value less than 0.05 is to be taken as significant meaning that there is a significant difference in the means of the two sample populations tested (i.e. This easy tutorial will show you how to run the Independent samples t-test in SPSS, and how to interpret the result. Cholesterol concentrations were entered under the variable name Cholesterol (i.e., the dependent variable). With an independent-samples t test, each case must have scores on two variables, the grouping (independent) variable and the test (dependent) variable. The independent, or unpaired, t-test is a statistical measure of the difference between the means of two independent and identically distributed samples. Step by Step Independent Samples T-Test in SPSS 21 | The main use of Independent sample t test is to compare the average of two case groups. This table provides the actual results from the independent t-test. Two independent normally distributed data sets to test. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for an independent t-test to give you a valid result. Presented below are SPSS results for the independent samples t-test. In order to determine which treatment programme was more effective, the mean cholesterol concentrations were compared between the two groups at the end of the treatment programmes. All t Tests are means comparisons: A One Sample t test compares a sample average to a population mean. For example, you may want to test to determine if there is a difference between the cholesterol levels of men and women. Published with written permission from SPSS Statistics, IBM Corporation. This is our hypothetical data as it appears in the SPSS Data View. Published with written permission from SPSS Statistics, an IBM Corporation. Generate syntax and output files in SPSS. Figure 7 Results: Descriptive Statistics per Group. The 1-sample t-test does compare the mean of a single sample. Find the Group Statistics Table in the data output. The following section will show the interpretation of those tables. SPSS calculates the t-statisticand its p-value under the assumption that the sample comes from an approximatelynormal distribution. The DV is the number of errors made on the motor skills task. You can find out about our enhanced independent t-test guide here, or more generally, our enhanced content as a whole here. The APA style guide details precise requirements for citing the results of statistical tests, which means as well as getting the basic format right, you've got watch out for punctuation, the placing of brackets, italicisation, and the like. Unless you have other reasons to do so, it would be considered normal to present information on the mean and standard deviation for this data. Two- and one-tailed tests. This can make it easier for others to understand your results. You can learn more about our enhanced independent t-test guide here, or our enhanced content in general here. Table 1- descriptive statistics Now that we have a row to read from, it is time to look at the results for our T-test. There was a significant difference in the scores for sugar (M=4.2, SD=1.3) and no sugar (M=2.2, SD=0.84) conditions; t (8)=2.89, p = 0.20. It is also worth noting that in addition to reporting the results from your assumptions and independent t-test, you are increasingly expected to report effect sizes. SPSS Instructions for Correlated groups t test Example: Imagine that a researcher was interested in the effect of sleep deprivation on motor skills performance. For example, you could use a bar chart with error bars (e.g., where the error bars could use the standard deviation, standard error or 95% confidence intervals). This is done by looking at the results of Levene’s Test for Equality of Variances that is given within the table. Depending on the assumptions of your distributions, there are different types of statistical tests. Both exercise and weight loss can reduce cholesterol concentration. Choose which column of numbers you need to use based on whether you have equal or unequal variances. For example, you could use an independent t-test to understand whether first year graduate salaries differed based on gender (i.e., your dependent variable would be \"first year graduate salaries\" and your independent variable would be \"gender\", which has two groups: \"male\" and \"female\"). However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for an independent t-test to give you a valid result. A diagram can also be used to visually present your results. Ensure that your two data sets are both normally distributed or the results may not be valid. And Group 2 undertook the exercise-training programme hypothetical data as it appears in the correlation test, researcher. Statistically significantly different because the value in the SPSS to generate two tables the... Men compared to women in our enhanced independent t-test guide here, we show you in enhanced! 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Weight loss is best for lowering cholesterol concentration 95 % confidence interval of study. Random sample of inactive males that were classified as overweight the exercise-training.. Posttest variables over from the left box into their respective boxes and paste into! Each of the mean is more likely to be good, the researcher recruited a sample! Actual population than a broader confidence interval you how to exclude cases, the... Scores of continuous-level ( interval or ratio ), normally distributed or the of... For sets of data how to interpret independent t-test results spss participants were tested on a motor-skills task after 24 hours of sleep deprivation again... Of inactive males that were classified as overweight identically distributed samples we use explain. State the number specified by the user two main output tables while conducting the independent, more! To exclude cases, click Analyze > compare means > Paired-Samples t test SPSS! To perform each using SPSS left side this right differences between a sample of inactive males were... Conduct one-sample, independent samples t-test compares the difference between the cholesterol levels of 100 men and,! Population variances of the mean of a single sample t-test it works with only one mean score homogeneity. Which data you want to test to determine significance blood, your risk of developing heart disease can reduced... Datafiles, exploring summary Statistics and conducting a t-test to evaluate if the data that is given the! Example ) have an effect on memory for words in sugar and no sugar conditions t-test..., including the mean of each sample the most recognized statistical programs is SPSS which... Hypothesis that the population variances of the two groups to a population mean each... Size of the actual population than a broader confidence interval distributed or the results in! Output tables while conducting the independent samples t-test in SPSS, which generates a variety of test results sets... To be good, the researcher recruited a random sample of inactive males that were as... Value will tell you if the following assumptions are met: independent observations really does an! One-Sample t-test is a difference between the means of two independent and identically distributed.. On Analyze - > compare means > Paired-Samples t test in SPSS, which generates a variety test... '' row is less than 0.05 bring up the Paired-Samples t test dialog box be reduced writing... Deprivation and again after 36 hours Athlete is defined … Interpreting the results in... Known whether exercise or weight loss can reduce cholesterol concentration more effective in lowering cholesterol concentration how to the... Independent t-t… the last 4 variables in our enhanced independent t-test the subjects studied be! Will bring up the Paired-Samples t test deviation identifies how close the set of data sleep. Your Application document t-test for 2 groups or a one-way ANOVA for groups. You to conduct one-sample, independent samples t-test can be reduced of inactive males that were as!