9. Missing Values per Case (Overview and data file are are found here) Missing values complicate some analyses or may sometimes indicate that respondents didn’t fill out the questionnaire very seriously. In any case, it’s a good idea to at least check how missing values are distributed over respondents and we often do so for sections […]
6. Inconvenient Distributions (Overview and data file are are found here) Let’s first state this clearly: we don’t exclude variables simply because their distributions are not to our liking. What we mean by “inconvenient distributions” are variables that don’t really provide us with useful information. This point is perhaps best explained by an example. Let’s inspect q4. […]
In SPSS, SELECT IF deletes cases (rows of data) that don’t satisfy some condition(s). Like so, SELECT IF basically means “don’t delete if …” A minimal example isSELECT IF(GENDER = 1). SPSS SELECT IF – Some Examples Say we collected data on our employees and the resulting data file is employees.sav. Our client is only interested in female […]
The vast majority of statistical tests really just compare several populations on one variable or compare several variables in one population. Now, if we add to this comparison the number of variables or populations and the measurement levels of our variables then choosing the appropriate statistical test suddenly becomes remarkably easy.* The overviews below also nicely visualize how basic tests relate […]
What Does It Really Mean? Statistical significance is the probability of finding a given deviation from the null hypothesis -or a more extreme one- in a sample. Statistical significance is often referred to as the p-value (short for “probability value”) or simply p in research papers. A small p-value basically means that your data are unlikely under some null hypothesis. […]
