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. In this example, the dependent variable is "weekly screen time" and the two independent groups are "male" and "female" university students in the United States. Understanding why the independent-samples t-test is being used, Identifying your version of SPSS Statistics, Interpreting the results of an independent-samples t-test analysis, using an "estimation" approach (using 95% CI), using a "Null Hypothesis Significance Testing" (NHST) approach (using, Calculating an effect size based on your independent-samples t-test results. 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. This reflects the coding in the Value Labels dialogue box: "1" = "Diet" and "2" = "Exercise" for our dichotomous independent variable, Intervention. Based on the results from the independent-samples t-test and associated descriptive statistics in the previous section, we could report the results of this fictitious study using an estimation approach as follows: An independent-samples t-test was run on a random sample of 40 sedentary individuals to determine if there was a mean difference in cholesterol concentration between participants who underwent a 6-month exercise intervention and a 6-month dietary intervention. The generalization of "Student's" problem when several different population variances are involved. 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. 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. All 26 students undertook the same maths exam. The standard way to organize your data within the SPSS Data View when you want to run an independent samples t test is to have a dependent variable in one column and a grouping variable in a second column.Here’s what it might look like.In this example, Frisbee Throwing Distance in Metres is the dependent variable, and Dog Owner is the grouping variable. The unstandardised effect size would be 0.52 mmol/L. Deviation" column). We also introduce the importance of calculating an effect size for your results. The independent, or unpaired, t-test is a statistical measure of the difference between the means of two independent and identically distributed samples. So far, we know that the mean difference in cholesterol concentration between the diet group and exercise group in our two samples is 0.52 mmol/L (to 2 decimal places). If your data "passed/met" assumption #4 (i.e., you do not have problematic outliers), assumption #5 (i.e., your dependent variable is normally distributed for each category of your independent variable) and assumption #6 (i.e., you have homogeneity of variances), you only need to interpret the results in these two tables. In our example, the dependent variable, cholesterol concentration, is being measured using mmol/L. Based on this result, we can reject the null hypothesis of no mean difference in the population and accept the alternative hypothesis that there is a mean difference. As an introduction to effect size measures, these can be classified into two categories: unstandardised and standardised. However, imagine that it is not known whether exercise or weight loss through dieting is most effective for lowering blood cholesterol concentration. In these cases, you will need to use an independent-samples t test. Instead, you will have to make changes to your data and/or run a different statistical test to analyse your data. 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. To carry out an independent-samples t-test, you have to set up two variables in SPSS Statistics. In the section, Calculating an effect size based on your independent-samples t-test results, we highlight the need to discuss the practical significance of this result; in other words, the need to discuss whether from a health perspective, a mean difference in cholesterol concentration that could plausibly be between 0.17 mmol/L and 0.86 mmol/L amongst sedentary people in the population who undertake a 6-month exercise programme compared to a 6-month dietary programme may be important or at least interesting. As previously stated, the sample mean difference is the best estimate of the population mean difference, but since we have just one study where we took a single sample from each of our two populations, we know that this estimate of the population mean difference in cholesterol concentration between participants in the diet group and exercise group will vary (i.e., it will not always be the same as in this study). Your data is now set up correctly in SPSS Statistics. Nonetheless, when carrying out an independent-samples t-test, it is common to interpret and report both the p-value and 95% CI. We'll get to the other 3 dependent variables later. This group was called the "diet" group. The cell under the column should show , indicating that you have a continuous dependent variable, whilst the cell under the column should show . The second three assumptions related to your data and can be tested using SPSS Statistics. 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? Wanneer gebruik je de t-test. 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. For example, you may want to test to determine if there is a difference between the cholesterol levels of men and women. An independent-samples t-test is most often used to analyse the results of two different types of study design: (a) determining if there is a mean difference between two independent groups; and (b) determining if there is a mean difference between two interventions. The researcher analysed the data collected to determine whether salaries were greater (or smaller) in the internship group compared to the no internship group. Therefore, it provides far less information that the 95% CI discussed in the previous section, which is now the preferred approach. (2-tailed)" column of the Independent Samples Test table, as highlighted below: The p-value reported under the "Sig. This easy tutorial will show you how to run the Independent samples t-test in SPSS, and how to interpret the result.. Diezelfde informatie is weergegeven voor klas B. Je kan deze resultaten later rapporteren bij het beschrijven van je resultaten maar hoef je voor de test verder niks mee te doen. De t-testen zijn alleen geschikt voor 1 of 2 groepen. Wil jij weten wat we voor je kunnen betekenen en wat de kosten zijn? A standard error: Distinguishing standard deviation from standard error. We waren niet voldoende overtuigd (met 95% of meer) om de H0 te verwerpen en de HA aan te nemen. For example, it tests that null hypothesis that there is no mean difference in cholesterol concentration between participants in the diet group and exercise group in the population. Therefore, we use an independent-samples t-test to help us quantify this error (i.e., the error between the mean difference in our sample when compared to the mean difference in our population). This is a test for making an inference to population parameters (viz., population mean IQs; H o: ì left = ì right). De hypothese is dus tweezijdig. A researcher wanted to know whether exercise or diet is most effective at improving a person’s cardiovascular health. All of the variables in your dataset appear in the list on the left side. Therefore, after clicking on the button in the previous step, you should expect to see two pieces of information in the Grouping Variable: box: (a) the name of your independent variable (i.e., "Intervention" in our example); followed by (b), the coding of your two groups in brackets (i.e., "(1 2)" in our example). Since assumptions #1, #2 and #3 relate to your study design and how you measured your variables, if any of these three assumptions are not met (i.e., if any of these assumptions do not fit with your research), the independent-samples t-test is the incorrect statistical test to analyse your data. Een deadline kan voor veel stress zorgen. Researchers want to know if a new fuel treatment leads to a change in … De t-test wordt gebruikt om: twee groepen met elkaar te vergelijken (Independent samples t-test) één groep op 2 momenten te vergelijken (Paired samples t-test) If further research consistently suggests that an exercise programme leads to lower cholesterol concentrations in participants compared to a dietary programme by an "average" of around 0.52 mmol/L, this may influence the advice health practitioners give patients, as well as influencing what type of programmes the Government invests in to reduce cholesterol concentration amongst sedentary people. This may seem like an unnecessary step because our categorical independent variable clearly only has two groups (i.e., it is a dichotomous variable). 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 other 30 had undertaken a 3-year Finance degree that did not include an internship. The results from the independent-samples t-test analysis above are discussed in the next section: Interpreting the results of an independent-samples t-test analysis. Explanation: You are entering "1" into the Group 1: box and "2" into the Group 2: box because this is how we coded the two groups of our categorical independent variable, Intervention, in the Value Labels dialogue box, as explained earlier and as shown below: Wil jij weten wat wij voor jou kunnen betekenen? To take another example we used earlier in this guide, if the mean difference in weekly screen time between male and female university students was 27 minutes, then 27 minutes is the unstandardised effect size (i.e., the dependent variable, weekly screen time, was measured in minutes). Unfortunately, SPSS Statistics does not automatically produce an effect size as part of an independent-samples t-test analysis. In other words, you will have to run different or additional procedures/steps in SPSS Statistics in order to analyse your data. 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"). Perlu kita pahami bersama bahwa dalam statistik parametrik terdapat syarat-syarat yang harus terpenuhi sebelum kita dapat melakukan pengujian hipotesis (dalam hal ini uji hipotesis menggunakan uji independent sample t-test). 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. However, in the next section we first discuss how to interpret the independent-samples t-test results using a Null Hypothesis Significance testing (NHST) approaching using p-values. In summary, the Group Statistics table presents the sample size (i.e., under the "N" column), the sample mean (i.e., under the "Mean" column), the sample standard deviation (i.e., under the "Std. 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. 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. Om te tabel beter te kunnen lezen, hebben wij hem bij de uitleg opgeknipt in twee losse delen. Judd, C. M., McClelland, G. H., & Ryan, C. S. (2009). We explain how to test whether your data "passes/meets" these assumptions in our enhanced independent-samples t-test guide, which you can access by subscribing to Laerd Statistics. Unfortunately, SPSS Statistics does not automatically produce a standardised effect size as part of an independent-samples t-test analysis. Independent-samples t-test using SPSS Statistics. 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. 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". Misuse of standard error of the mean (SEM) when reporting variability of a sample. A two sample t-test is used to test whether or not the means of two populations are equal.. 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. Om te kijken of er verschillen tussen twee variabelen zijn, worden er toetsen gebruikt. Presenting a mean difference with a 95% CI to understand what the population mean difference is, and your uncertainty in its value, is an approach called "estimation". Biostatistics: How to detect, correct and prevent errors in the medical literature. 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. Therefore, the Grouping Variable: box above includes the text, "Intervention(1 2)". We willen met 95% (standaard) zekerheid kunnen zeggen dat we de nul hypothese moeten verwerpen en de alternatieve hypothese aannemen. De t-test vergelijkt gemiddelde(s) en wordt gebruikt om hypotheses te toetsen. 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. Therefore, you would typically report the sample mean and sample standard deviation (and not the standard error of the mean). Therefore, in this study the dependent variable was "salary", measured in US dollars, and the independent variable was "course type", which had two independent groups: "internship group" and "no internship group". (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). When you report the results of an independent-samples t-test, it is good practice to include the following information: Note: Whilst you can present your results in-text, using a table and a graph, many reporting styles do not favour the duplication of results using all three methods. Wij staan voor je klaar staat zijn wanneer jij dat nodig hebt, luisteren naar je en nemen jouw vragen serieus (naast dat we die natuurlijk ook gaan beantwoorden), Is SPSS als Chinees voor je? Indien je daarna vragen hebt staat het team van Afstudeerbegeleider voor je klaar om je persoonlijk te helpen! Kang, Y., & Harring, J. R. (2012). It is important that you understand both approaches when analysing your data using an independent-samples t-test because they affect how you: (a) carry out an independent-samples t-test using SPSS Statistics, as discussed in the Procedure section later; and (b) interpret and report your results, as discussed in the Interpreting Results and Reporting sections respectively. 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. This is the next box you will look at. (2008). At first glance, you can see a lot of information and that might feel intimidating. 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. An unstandardised effect size is reported using the units of measurement of the dependent variable. 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. 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). Barde, M. P, & Barde, P. J. In order that these p-values and confidence intervals (CI) are accurate/valid, your sample data must "pass/meet" a number of basic requirements and assumptions of the independent-samples t-test. Group Statistics De eerste tabel, Group Statistics, bevat beschrijvende statistieken van beide groepen, zoals het gemiddelde, de standaarddeviatie en de standaardfout van het gemiddelde. After the students have taken the maths exam, their scores (between 0 and 100 marks) were recorded. When you are confident that your data has met all six assumptions described above, you can carry out an independent-samples t-test to determine whether there is a difference between the two groups of your independent variable in terms of the mean of your dependent variable. Example Data, SPSS Data Entry, and Value Labels. As we discussed in the previous section, we know that the mean difference in cholesterol concentration between the diet group and exercise group in our two samples is 0.52 mmol/L (to 2 decimal places). The One-Sample T Test window opens where you will specify the variables to be used in the analysis. Met onze begeleiding weet jij je weg te vinden in SPSS. Myers, J. L., Well, A. D., & Lorch, R. F., Jr. (2010). 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). You should use the Group Statistics table to understand: (a) whether there are an equal number of participants in each of your groups (i.e., under the "N" column): (b) which group had the higher/lower mean score (i.e., under the "Mean" column), and what this means for your results; and (c) if the variation of scores in each group is similar (e.g., under the "Std. Zaugg, C. E. (2003). Hieronder zie je de volledige tabel voor de independent samples t-test. When checking if your data meets these three assumptions, do not be surprised if this process takes up the majority of the time you dedicate to carrying out your analysis. Output tabellen van de independent t-test, Interpreteren output independent samples t-test. However, if your data violates/does not meet Assumption #4 (i.e., you have problematic outliers) and/or Assumption #5 (i.e., your dependent variable is not normally distributed for each category of your independent variable), the eight steps below are not relevant. These results suggest that sugar really does have an effect on memory for words. All rights reserved. Now, you simply have to enter your data into the cells under each column. In de eerste tabel in de output van de independent t-test worden de statistieken van de 2 groepen gegeven. 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). In stap 2 hebben we bepaald dat er geen verschil is tussen het gemiddelde van klas A en klas B. Note: Technically, it is the residuals that must be approximately normally distributed within each group rather than the data within each group, but in an independent-samples t-test, the results will be the same. Good Luck Cite in terms of the mean of a continuous dependent variable (e.g., first-year salary after graduation in US dollars, time to complete a 100 meter sprint in seconds, etc.) These 40 participants were randomly assigned to one of two groups. Therefore, you can simply click into the cells under the column and change these to "0" using the arrows, which is why "Diet" is coded as "1" and not "1.00" in the Value Labels box above. The independent-samples t-test, also known as the independent t-test, independent-measures t-test, between-subjects t-test or unpaired 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.) As an example, a practical application would be to find out the effect of a new drug on blood pressure. It is likely that there will be other statistical tests you can use instead, but the independent-samples t-test is not the correct test. Furthermore, the independent-samples t-test is typically used to test the null hypothesis that the mean difference between the two groups in the population is zero (e.g. Clicking Paste creates the syntax below. Dat is bij een Sig. Of these 60 graduates, 30 had undertaken a 3-year Finance degree that included a mandatory 1-year internship. Independent-samples t test (two-sample t test) This is used to compare the means of one variable for two groups of cases. In these cases, you will need to use an independent-samples t test. This is not uncommon when working with real-world data, which is often "messy", as opposed to textbook examples. De SPSS-output voor de independent samples t-test (t-test voor onafhankelijke steekproeven) bevat twee tabellen. Independent-Samples t Test with SPSS In your research, you may find that you must compare the means of two samples related to one particular variable. There are many different types of standardised effect size, with different types often trying to "capture" the importance of your results in different ways. 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. SPSS Independent Samples T-Test Syntax For example, we know that the mean difference in cholesterol concentration between participants in the diet group and exercise group in our two samples was 0.52 mmol/L, as highlighted in the "Mean Difference" column in the table above. We could then simply compare the mean difference in weekly screen time between all male and all female students using descriptive statistics to understand whether there was a difference. As an introduction to effect size measures, these can be classified into two categories: unstandardised and standardised. Dat is het geval als onder Sig. Wij helpen je die deadline toch te halen! In other words, you are using an independent-samples t-test because you are not only interested in determining whether there is a mean difference in the dependent variable between your two groups in your single study (i.e., the sample of 150 male students and sample of 150 female students), but whether there is a mean difference in these two samples in the wider populations from which these two samples were drawn. in terms of the mean of a continuous dependent variable (e.g., first-year salary after graduation in US dollars, time to complete a 100 meter sprint in seconds, etc.) een waarde van .120, dus groter dan .05 (5% kans op een fout) dus mogen we niet zeggen dat er een (significant) verschil is tussen klas A en klas B en het gemiddelde cijfer. We do this over the four sections that follow: (1) understanding descriptive statistics; (2) the independent-samples t-test results using an "estimation" approach (using 95% CI); (3) the independent-samples t-test results using a "Null Hypothesis Significance Testing" (NHST) approach (using p-values); and (4) effect size calculations after carrying out an independent-samples t-test. Set out the effect of a continuous dependent variable … the independent-samples t test: options dialogue will... Two independent and identically distributed samples up correctly in SPSS, which is a difference... Know the sample that was studied to enter your data into the data View window above, can... Columns based on the order that you can also provide a standardised effect size as part of independent-samples. Of data: standard deviation and sample standard deviation or standard error the... 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