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?” […]
SPSS FORMATS sets formats -decimal places, dates, percent signs and more- for numeric variables. Setting variable formats in SPSS does not change your actual data values. However, formats determine how your data are displayed -in the data viewer as well as the output-window. Two main uses of FORMATS are increasing or decreasing the decimal places of standard numeric variables; displaying date, time and datetime values (consisting […]
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 […]
A car brand had 18 respondents rate 3 different car ads on attractiveness. The resulting data -part of which are shown above- are in adratings.sav. Some background variables were measured as well, including the respondent’s gender. The question we’ll try to answer is:are the 3 ads rated equally attractive and does gender play any role here?Since […]
