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Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. J., Berger, J., Johannesson, M., Nosek, B. Therefore, it becomes clear that making a decision only based on a p-value is not sufficient, and other measures should be considered as well.īenjamin, D. Besides that, Cohen’s d indicates there is a small to medium effect.
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However, we also couldn’t say the null hypothesis is ‘true’, or the difference between the groups is equal to 0. Question: Write down the null and alternative hypothesis that represent this question.īased on the above measures, we cannot reject the null hypothesis, because p =. The data can be found in the file phd-delays.csv. So, in our model the PhD delays is the dependent variable and having a child is the predictor. Out of the 333 respondents, 18% reported to have had at least one child. The variable B3_difference_extra measures the difference between planned and actual project time in months (mean=9.96, minimum=-31, maximum=91, sd=14.43).įor the current exercise we would like to answer the question why some Ph.D. recipients took longer than others by investigating whether having had any children (up to the age 18) throughout the Ph.D. trajectory affects delays (0=No, 1=Yes). It appeared that Ph.D. recipients took an average of 59.8 months (five years and four months) to complete their Ph.D. trajectory. Among many other questions, the researchers asked the Ph.D. recipients how long it took them to finish their Ph.D. thesis (n=333). © W.The data we will be using for this exercise is based on a study about predicting PhD-delays ( Van de Schoot, Yerkes, Mouw and Sonneveld 2013). Note that again you may list several variables, all of which are tested against the values indicated after keyword TESTVAL. Typically, such a pre-defined value may come from previous knowledge about the subject, but you may choose any value you are interested in. The difference between the two examples provided is a follows: In the first example, all three possible pairs of comparisons between variables are tested in the second example, the mean of var31 and var32, respectively, is tested against var33.įinally, the one-sample test compares the mean of var31 against a pre-defined value, which is indicated as 3.5 in this example. The final clause (PAIRED) obviously is redundant and may be omitted in newer versions of SPSS. All you have to do is to name the variables on which the cases are to be compared. In the paired sample case, each case in the data set is compared to itself therefore, no indication of group membership is necessary. – More than one variable can be provided in the variables list. If the variances differ significantly, the latter test statistic and the significance value that accompanies it should be used. SPSS then computes two test statistics for the T test, one for the case of equal variances in both groups and one for unequal variances. Note that SPSS will first display a test on homogeneity of variances. After keyword VARIABLES, the (metric) variable(s) is or are mentioned with respect to which the groups are compared.
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The values indicating group membership are made explicit in parentheses. In the command for the independent samples case, after keyword GROUPS the variable is named that indicates to which group each case belongs. T-TEST PAIRS = var31 var32 WITH var33 (PAIRED). T-TEST PAIRS = var31 var32 var33 (PAIRED). Finally, one or more means from a single sample may be tested against a pre-defined value.Įxample for independent samples: T-TEST GROUPS = var17 (1,2) The latter cases arises if a sample is compared "to itself", as it were, e.g., if the first and the present salary of individuals is compared. These group means may come from two independent samples (say, one sample of boys and one of girls, or samples from two school classes) or from "paired" samples. The T-test is used to test whether a difference between two group means is statistically significant.