One such graph is a jittered dot plot with superimposed mean and standard deviation "error bars", as highlighted below: A jittered dot plot with superimposed mean and standard deviation "error bars" shows: The information from this jittered dot plot helps to highlight that: The jittered dot plot is also useful when discussing outliers, which is an important assumption of the independent-samples t-test. In our example, this level of probability is set at the alpha level (α) of .05, which is why we assess whether our result is statistically significant (or not) based on a p-value that is less than or greater than .05 respectively. For example, 1 – (an alpha level of) .05 = .95 (i.e., 95%), so the number "95" is entered into the Confidence Interval Percentage box. The SPSS t-test procedure allows the testing of hypothesis when variances are assumed to be equal or when are not equal and also provide the t-value for both assumptions. The other 30 had undertaken a 3-year Finance degree that did not include an internship. If you would like us to let you know when we add this guide to the site, please contact us. Making sure that your study design, variables and data pass/meet these assumptions is critical because if they do not, the independent-samples t-test is likely to be the incorrect statistical test to use. After reporting the unstandardised effect size, we might also report a standardised effect size such as Cohen's d (Cohen, 1988). The button will appear in the cell. As discussed earlier in the Procedure section, the most common confidence interval (CI) is the 95% CI, which is the default in SPSS Statistics (and most statistics packages), and is what is reported under the "95% Confidence Interval of the Difference" column in the Independent Samples Test table, as highlighted in orange below: A confidence interval (CI) will give you, based on your sample data, a likely/plausible range of values that the mean difference might be in the population. Output tabellen van de independent t-test, Interpreteren output independent samples t-test. Based on the file setup for the dependent variable and independent variable in the Variable View above, the Data View window should look as follows: Note: On the left above, the responses for our independent variable are shown in text (e.g., "Diet" and "Exercise" under the column). However, since it can be challenging to understand how the independent-samples t-test is used under NHST, we will be adding a guide dedicated to explaining this, including concepts such as the t-distribution, alpha (ɑ) levels, statistical power, Type I and Type II errors, p-values, and more. Note: To ensure that the assumption of independence of observations was met, as discussed earlier, participants could only be in one of these two groups and the two groups did not have any contact with each other. All of the variables in your dataset appear in the list on the left side. If p > .05 (i.e., if p is greater than .05), there is not a statistically significant mean difference in cholesterol concentration between the diet group and exercise group. This tutorial explains how to conduct a two sample t-test in SPSS. Difference between two TREATMENT/EXPERIMENTAL GROUPS In other words, there will be some variation in the sample mean difference each time we sample our populations. Kom je er niet uit met de output independent samples t-test? In the next section, we explain how to set up your data in SPSS Statistics to run an independent-samples t-test using these two variables: Cholesterol and Intervention. This test also provide the relevant descriptive statistics for both of the groups. We also explain what options you have when these assumptions are "violated/not met", as well as providing guides to help you continue with your analysis. However, it is easy to calculate a standardised effect size such as Cohen's d using the results from the independent-samples t-test analysis. The independent-samples t test is commonly referred to as a between-groups design, and can also be used to analyze a control and experimental group. If you choose to increase the CI when carrying out an independent-samples t-test from, for example, 95% to 99%, you increase your level of confidence that the population mean difference is somewhere between the lower and upper bounds that are reported. For example, you may have population samples from two ethnic groups that you wish to compare against income level. This alpha (ɑ) level is usually set at .05, which means that if the p-value is less than .05 (i.e., p < .05), you declare the result to be statistically significant, such that you reject the null hypothesis and accept the alternative hypothesis. A two sample t-test is used to test whether or not the means of two populations are equal.. Therefore, there was a mean difference of 0.52 mmol/L (to 2 decimal places) between the diet group and exercise group in our two samples, with cholesterol concentration being 0.52 mmol/L higher in the diet group (i.e., 6.3235 – 5.8082 = 0.52 mmol/L to 2 decimal places). Therefore, two more groups have been added to the Value Labels dialogue box above: 3 = "Drug" and 4 = "Control". In the Procedure section, we set out the simple 8-step procedure in SPSS Statistics to carry out an independent-samples t-test, including useful descriptive statistics. Therefore, it provides far less information that the 95% CI discussed in the previous section, which is now the preferred approach. Therefore, we show you how to calculate and interpret Cohen's d in our enhanced independent-samples t-test guide, which you can access by subscribing to Laerd Statistics. An independent-samples t-test was used to determine whether there was a statistically significant mean difference in the exam results between the experimental group and the control group. Using Independent samples t-test in Research. Click on this button and the Value Labels dialogue box will appear. In order to quantify this uncertainty in our estimate of the population mean difference, we can use the independent-samples t-test to provide a 95% confidence interval (CI), which is a way of providing a measure of this uncertainty. If you are unsure which version of SPSS Statistics you are using, see our guide: Identifying your version of SPSS Statistics. Determining whether to reject or fail to reject the null hypothesis is based on a preset probability level (i.e., sometimes called an alpha (ɑ) level). Myers, J. L., Well, A. D., & Lorch, R. F., Jr. (2010). At the end of the data setup process, your Variable View window will look like the one below, which illustrates the setup for both the dependent variable, Cholesterol, and the independent variable, Intervention: Published with written permission from SPSS Statistics, IBM Corporation. Therefore, do not think that you have done anything wrong if 2 decimals places have been added to the values you set up in the Value Labels box. If we carried out a second study with a sample of 150 male students and a sample of 150 female students, or a third study with a sample of 150 male students and a sample of 150 female students, or a fourth study with a sample of 150 male students and a sample of 150 female students, it is likely that the mean difference in weekly screen time will be different each time, or at least, most of the time (e.g., 31 minutes in the second study, 25 minutes in the third study, 28 minutes in the fourth study). However, do not worry if you do not understand these terms. (2) The independent variable, Intervention, which has two groups – "Diet" and "Exercise" – to reflect the 20 participants who underwent the 6-month dietary intervention (i.e., the diet group) and the 20 participants who underwent the 6-month exercise intervention (i.e., the exercise group). Therefore, a health practitioner analysing the results from this single study would have to ask the question: If the unstandardised effect size was only 0.17 mmol/L (i.e., the "lower bound" of the 95% CI), would the results from this study still be of practical importance? However, a one-way ANCOVA is more commonly recommended for this type of study design. To briefly recap, an independent-samples t-test is used to determine whether there is a difference between two independent, unrelated groups (e.g., undergraduate versus PhD students, athletes given supplement A versus athletes given supplement B, etc.) Since we have just one study and we know that there will be some variation in the mean difference and standard deviation in cholesterol concentration between participants in the diet group and exercise group each time we sample our population, we need a way to assess the uncertainty in estimating the population mean difference based on our sample mean difference of 0.52 mmol/L (i.e., there is some error in the sample mean difference that is being used to estimate the population mean difference). We use this sample mean difference to estimate the population mean difference. This sample mean difference, which is called a "point estimate", is the best estimate that we have of what the population mean difference is (i.e., what the mean difference in weekly screen time is between all male and females university students in the United States, which is the population being studied). Welch, B. L. (1947). This includes: (1) setting out the procedures in SPSS Statistics to test these assumptions; (2) explaining how to interpret the SPSS Statistics output to determine if you have met or violated these assumptions; and (3) explaining what options you have if your data does violate one or more of these assumptions. All 26 students undertook the same maths exam. To learn more about these two types of study design where the independent-samples t-test can be used, see the examples below: Note 1: An independent-samples t-test can also be used to determine if there is a mean difference between two change scores (also known as gain scores). In de eerste tabel in de output van de independent t-test worden de statistieken van de 2 groepen gegeven. To set up these variables, SPSS Statistics has a Variable View where you define the types of variables you are analysing and a Data View where you enter your data for these variables. Note: For your own clarity, you can also provide a label for your variables in the column. The first-year graduate salaries of all 60 graduates were recorded in US dollars. You can access our enhanced independent-samples t-test guide by subscribing to Laerd Statistics. Note: As we mentioned earlier, unless you are familiar with statistics, the idea of NHST can be a little challenging at first and benefits from a detailed description, but we will try to give a brief overview in this section. NHST does not say what this difference might be. Therefore, imagine that a researcher wanted to determine whether students who enrolled in a 3-year degree course that included a mandatory 1-year internship (also known as a placement) received better graduate salaries than students who did not undertake an internship. Geen nood, wij helpen je om het te begrijpen zodat jij weer verder kan, Wil jij weten hoe je met SPSS moet werken? Knowing this information, sometimes the main goal of a study is simply to answer the question: Is there a mean difference between your two groups in the population? Therefore, if you get an error message and you would like us to add an SPSS Statistics guide to explain what these illegal characters are, please contact us. For example, the label we entered for "Intervention" was "Type of intervention: "Diet" and "Exercise"". Put simply, we want to know whether owning a dog (independent var… The independent-samples t-test was supplemented with an effect size calculation to assess the practical/clinical importance of the mean difference in exam results between the experimental group and the control group. As explain in the earlier section, Understanding why the independent-samples t-test is being used, the independent-samples t-test: (a) using the NHST approach, gives you an idea of whether there is a mean difference between your two groups in the population, based on your sample data; and (b) as a method of estimation using confidence intervals (CI), provides a plausible range of values that the mean difference between your two groups could be in the population, based on your sample data. Since you may not want to transfer these variables, we suggest changing the setting to so that this does not happen automatically. To determine which intervention – the diet intervention or the exercise intervention – was most effective in improving cardiovascular health (i.e., by lowering participants' cholesterol concentrations), the researcher carried out an independent-samples t-test to: Using the results from the independent-samples t-test, the researcher then: Explanation: As explain in the earlier section, Understanding why the independent-samples t-test is being used, the independent-samples t-test: (a) using the NHST approach, gives you an idea of whether there is a mean difference between your two groups in the population, based on your sample data; and (b) as a method of estimation using confidence intervals (CI), provides a plausible range of values that the mean difference between your two groups could be in the population, based on your sample data. This is the goal of Null Hypothesis Significance Testing (NHST). een waarde staat van .05 (5% foutkans) of lager. The Define Groups dialogue box is telling SPSS Statistics to carry out an independent-samples t-test using these two groups from our Value Labels dialogue box: 1 = "Diet" and 2 = "Exercise". This is called the "control group". When interpreting the results from an independent-samples t-test, descriptive statistics help you get a "feel" for your data, as well as being used when reporting the results of your independent-samples t-test analysis. In our example, we first entered the continuous dependent variable, Cholesterol, so this appears in the first column, entitled . Using SPSS for t Tests. 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. Wil jij weten wat wij voor jou kunnen betekenen? Independent-Samples T Test Het voorbeeldbestand bij Independent Sample T Test Het bestand bevat schadebedragen bij ongelukken voor en na een aanpassing van een voorrangsregel bij een kruispunt. To understand these two concepts – sample versus population – and how the independent-samples t-test is used to make inferences from a sample to a population, imagine a study where a researcher wanted to know if there was a mean difference in the amount of time male and female university students in the United States use their mobile phones each week. In the Independent Samples Test table above, the obtained t-value is 3.06 (to 2 decimal places), reported under the "t" column, and the degrees of freedom (df) are 38, reported under the "df" column (i.e., for an independent-samples t-test with equal variances, the degrees of freedom are equal to the sample size minus 2, so in our example, 40 – 2 = 38). For example, imagine that we randomly selected 150 male and 150 female university students in the United States to form our two samples. (2-tailed)" column of the Independent Samples Test table, as highlighted below: The p-value reported under the "Sig. For example, the label we entered for "Cholesterol" was "Cholesterol concentration (measured in mmol/L)". These descriptive statistics have the following meaning: The sample mean (i.e., the "average" score) for each group of the independent variable, which is the measure of central tendency used in an independent-samples t-test. Een voorbeeld van zo'n hypothese is "mannen en vrouwen verschillen van lengte". After all, to any health practitioner who deals with cholesterol levels in patients, a mean difference of 0.52 mmol/L in cholesterol concentration amongst sedentary people suggests that the findings from this (fictitious) study "might be" of practical importance. T- test for two independent samples used for scale and normally distributed data, and for ordinal data or non-normal distributed data you can use nonparametric test. For example, you could use an independent-samples t-test to understand whether the mean (average) number of hours students spend revising for exams in their first year of university differs based on gender. The Independent-Samples T Test window opens where you will specify the variables to be used in the analysis. This tutorial will show you how to use SPSS version 12.0 to perform one-sample t-tests, independent samples t-tests, and paired samples t-tests.. The other group underwent an exercise intervention where participants took part in a 6-month exercise programme consisting of 4 x 1-hour exercise sessions per week. We willen met 95% (standaard) zekerheid kunnen zeggen dat we de nul hypothese moeten verwerpen en de alternatieve hypothese aannemen. In other words, there will be some variation in the sample mean difference each time we sample our populations. The setup for our independent variable is shown in the Value Labels dialogue box below: Note: You will typically enter an integer (e.g., "1") into the Value: box to represent the group/levels of your independent variable and not a decimal (e.g., "1.00"). kleiner of gelijk aan .05. Together, the mean difference, 95% CI of the mean difference, statistical significance value (i.e., p-value), and effect size calculation are used to determine whether students who enrolled in a 3-year degree course that included a mandatory 1-year internship (also known as a placement) received better graduate salaries than students who did not undertake an internship. Therefore, both approaches are briefly discussed below: Note: Unless you are familiar with statistics, the idea of NHST can be a little challenging at first and benefits from a detailed description, but we will try to give a brief overview in this section. (2012). Note: If you are unfamiliar with the idea of p-values using a NHST approach or confidence intervals (CI) using an estimation approach, we introduced these concepts earlier in the section: Understanding why the independent-samples t-test is being used. In this introductory guide to the independent-samples t-test, we first describe two study designs where the independent-samples t-test is most often used, before explaining why an independent-samples t-test is being carried out to analyse your data rather than simply using descriptive statistics. In our example, the dependent variable, cholesterol concentration, is being measured using mmol/L. Independent Sample T-Test. The One-Sample T Test window opens where you will specify the variables to be used in the analysis. In other words, if the effect of a 6-month exercise programme compared to 6-month dietary programme is only a reduction in cholesterol concentration of 0.17 mmol/L, would these results still influence the decisions make by health practitioners or influence health policy more broadly (i.e., would the results still influence whether health practitioners recommended a 6-month exercise programme rather than a dietary programme to help sedentary people reduce their cholesterol concentration)? In fact, it is being more common practice to report both p-values and confidence intervals (CI) in journal articles and student reports (e.g., dissertations/theses). However, it is important to also take into account the 95% CI that were produced as part of our analysis. De independent-samples t-test (of onafhankelijke t-test) wordt gebruikt wanneer twee groepen aan twee verschillende condities worden onderworpen en je de scores van de groepen met elkaar wil vergelijken. Note: If the button is not active (i.e., it looks faded, like this ), click into the Grouping Variable: box to make sure that the independent variable (e.g., in our example, Intervention) is highlighted in yellow, as it is in Step 2 above. Hier staat simpelweg (in de bovenste rij) dat klas A uit 54 personen bestaat, een gemiddelde had van 6,574, een SD had van 1,78 en een SE van ,24. We also know that this sample mean difference of 0.52 mmol/L is based on just a single study of one sample of 20 participants in the diet group and another sample of 20 participants in the exercise group, and not from the millions of sedentary people that this study could theoretically represent. The Independent-Samples T Test procedure compares means for two groups of cases. If we carried out a second study with a sample of 20 participants in the diet group and a sample of 20 participants in the exercise group, or a third study with a sample of 20 participants in the diet group and a sample of 20 participants in the exercise group, or a fourth study with a sample of 20 participants in the diet group and a sample of 20 participants in the exercise group, it is likely that the mean difference in cholesterol concentration will be different each time, or at least, most of the time (e.g., 0.18 mmol/L in the second study, 0.92 mmol/L in the third study, 0.57 mmol/L in the fourth study). Wil jij weten wat we voor je kunnen betekenen en wat de kosten zijn? In some cases, failure to meet one or more of these assumptions will make the independent-samples t-test the incorrect statistical test to use. Hieronder nemen we je stap voor stap door de tabellen die je uit SPSS krijgt wanneer je de independent samples t-test hebt uitgevoerd. Standard deviations and standard errors. Judd, C. M., McClelland, G. H., & Ryan, C. S. (2009). Therefore, the Grouping Variable: box above includes the text, "Intervention(1 2)". Together, the mean difference, 95% CI of the mean difference, statistical significance value (i.e., p-value), and effect size calculation are used to determine whether financial rewards increased academic performance amongst school children. SPSS: Independent Samples t-test Commands SPSS: Independent Samples t-test Results ; SPSS: Selecting Two Groups When More than Two Groups Are Present 1.