We hadden twee hypothesen namelijk de H0 en de HA. Therefore, imagine that an educational psychologist wanted to determine whether financial rewards increased academic performance amongst school children. Whilst the null hypothesis states that there is no mean difference between your two groups in the population, the alternative hypothesis states that there is a difference between your two groups in the population (e.g., the alternative hypothesis that the mean difference in cholesterol concentration between participants in the diet group and exercise group is not 0 mmol/L or the specific value you set, such as 0.85 mmol/L, in the population). The two groups were independent because no student could be in more than one group and the students in the two groups were unable to influence each other’s exam results. Using the Independent t-test in SPSS This tutorial will walk you through how to run and interpret an Independent t-test. Welch, B. L. (1947). Het meetniveau van de variabelen bepaalt welke toets of je moet gebruiken. 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 weekly screen time between male and female students will vary (i.e., it will not always be the same as in this study). For example, imagine that we randomly selected 150 male and 150 female university students in the United States to form our two samples. Output tabellen van de independent t-test, Interpreteren output independent samples t-test. Levene’s Test for Equality of Variances Therefore, 27 minutes is the mean difference in weekly screen time between male and female university students in our two samples. In J. W. Osborne (Ed.). A 95% CI, which is reported under the "95% Confidence Interval of the Difference" column in the Independent Samples Test table above, could suggest that the mean difference in cholesterol concentration between people who undertake a 6-month dietary programme (i.e., the diet group) and people who undertake a 6-month exercise programme (i.e., the exercise group) in the population might plausibly be somewhere between 0.17 mmol/L and 0.86 mmol/L (both to 2 decimal places). Investigating the impact of non-normality, effect size, and sample size on two-group comparison procedures: An empirical study. Wil jij weten wat wij voor jou kunnen betekenen? (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). For example, you may want to test to determine if there is a difference between the cholesterol levels of men and women. 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, it is easy to calculate a standardised effect size such as Cohen's d using the results from the independent-samples t-test analysis. 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). For example, in row , our first case was a participant with a cholesterol concentration of 6.83 mmol/L who undertook the 6-month diet intervention. 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. However, in this introductory guide to the independent-samples t-test, we explain how to interpret your results when your data "passed/met" assumptions #4, #5 and #6. Voor de aanpassing wordt de oude situatie genoemd, na … If your result – or one more extreme – is unlikely to have happened by chance (i.e., due to sampling variation), you make the declaration that you believe the null hypothesis is false (i.e., there is a mean difference between the two groups in the population). After recording the weekly screen time of these 300 students over a 3-month period, we found that female students spent 27 minutes more time viewing their mobile phones each week compared to male students. Osborne, J. W., & Overbay, A. In the next section we explain why you are using an independent-samples t-test to analyse your results, rather than simply using descriptive statistics. 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. By clicking on the button the coding will appear in the main box (e.g., "1.00 = "Diet" for Intervention). The 95% CI is a very useful method to quantify the uncertainty in using the sample mean difference to estimate the population mean difference because although the sample mean difference is the best estimate that you have of the population mean difference, in reality, the mean difference in the population could plausibly be any value between the lower bound and upper bound of the 95% CI. Voorbeeld Independent Samples T-test, hier vind je hoe deze test uitvoert in SPSS, hoe deze test nu precies werkt en hoe je de uitkomst moet interpreteren. They are also the same if you have the subscription version of SPSS Statistics. In other words, the results suggest that there is a mean difference in the population based on the sample that was studied. Note: The order that you enter your variables into the Variable View is irrelevant. The eight steps below show you how to analyse your data using an independent-samples t-test in SPSS Statistics. For example, the label we entered for "Intervention" was "Type of intervention: "Diet" and "Exercise"". 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. 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. De hypothese is dus tweezijdig. Standard error as standard. 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. Therefore, in this study the dependent variable was "exam result", measured from 0 to 100 marks, and the independent variable was "financial reward", which had two independent groups: "experimental group" and "control group". Introductory guide, we suggest changing the setting to so that this does not indicate ``! 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