The assumption of equal intervals says that all distances between adjacent answer categories are equal in the repondents’ perception. This assumption is an attempt to justify treating ordinal variables as if they were metric variables. Doing so enables one to compute means, standard deviations and Pearson correlations on ordinal variables. This is simpler than using more appropriate techniques. Assumption of Equal […]
“I have a data file on which I’d like to carry out several regression analyses. I have four dependent variables, v1 through v4. The independent variables (v5 through v14) are the same for all analyses. How can I carry out these four analyses in an efficient way that would also work for 100 dependent variables?” […]
When analyzing your data, you sometimes just want to gain some insight into variables separately. The first step in doing so is creating appropriate tables and charts. This tutorial shows how to do so for dichotomous or categorical variables. We recommend you follow along by downloading and opening smartphone_users.sav. SPSS Frequency Tables We’d like to know which smartphone brands were […]
Summary (Overview and data file are are found here) SPSS has two variable types: string variables and numeric variables. String variables have “String” under Type in Variable View. All other variables are numeric. The screenshot below illustrates this point for hotel_evaluation.sav. 3. Undesirable Variable Types A problem with some data files is that they contain string variables that should have been numeric. A […]
The median test for independent medians tests if two or more populations have equal medians on some variable. That is, we’re comparing 2(+) groups of cases on 1 variable at a time. Rating Car Commercials We’ll demonstrate the median test on adratings.sav. This file holds data on 18 respondents who rated 3 different car commercials on […]
