• Home
• Fundamentals
  Introduction What is statistics Terms connected to data Measurement levels Significance SPSS basics
• One variable
  Introduction a binary variable a nominal variable an ordinal variable a scale variable
• Two variables - unpaired
  Introduction binary vs binary binary vs ordinal binary vs scale nominal vs nominal nominal vs ordinal nominal vs scale ordinal vs ordinal ordinal vs scale scale vs scale
• Two variables - paired
  Introduction two nominal two ordinal two scale
• 3+ variables - paired
  Introduction 3+ binary 3+ ordinal 3+ scale
• Distributions
  Introduction Binomial Bivariate Normal Chi-Square (Standard) Normal Student t Wilcoxon (Signed Rank)
• Search

Analysing a single ordinal variable

1a: Sample results (frequency table and median)

To begin with analysing a single ordinal variable, a good starting point can be to generate a frequency table, such as the one shown in Table 1.

The first few columns are the same as discussed for a single nominal variable (see there as well for more details). The first column contains the various possible options, split into Valid and Missing. Valid are those who answered this question, missing are those who didn’t.
The column Frequency shows how many respondents answered each option. We can tell that 100 people in this survey reported to find accounting very scientific.
The Percent column shows the percentages, based on the grand total, so including the missing values. The 5,1 indicates that 5.1% of all respondents answered 'very scientific' (you can check that 100 / 1974 x 100 = 5.1).
The Valid Percent shows the percentage, based on the valid total, so excluding the missing values. The 10.5 indicates that 10.5% of all of those who answered this question reported 'very scientific'. The Valid Percent is usually the one being reported, and then confusingly enough as simple Percent.

New are the two ‘cumulative’ columns. The word ‘cumulative’ simply means to add up. The cumulative frequency can be defined as: “the total (absolute) frequency up to the upper boundary of that class” (Kenney, 1939, p. 16). This would only be useful if there is an order to the categories, so we can say that for example 299 respondents found accounting pretty scientific or even more. Which is why these cumulative frequencies will not have a meaningful interpretation for a nominal variable (e.g. 28 students study business or less?).

The Cumulative Percent is the running total of the Valid Percent, it is the addition of all previous and the current category’s valid percentages. We can see that 31.3% of the respondents that answered this question though accounting is pretty or very scientific.

Instead of a frequency table, we might prefer to visualise the result. This is the topic for the next section.

Single ordinal variable

Introduction

Impression of data

Visualisation of data

Center & Dispersion

Test for median

Effect size

Reporting

Google adds