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Before you can enter all information from questionnaires and experiments in IBM SPSS, it is necessary to create a "codebook". This is a summary of the instructions you will use to convert each subject's information into a format that IBM SPSS can understand. Preparing a codebook consists of (1) defining and labeling each variable, and (2) assigning numbers to all possible answers.

How do you change the options in SPSS?

You can use the options for all kinds of possibilities to display variables, the type of tables you want to get as output and more. Options can be found under Edit. Make sure you first select what you want in all tabs and then click OK.

When you are sure that there is no error in your data file, you can start with the descriptive phase of your data analysis. We called this descriptive statistics. Descriptive statistics aim to:

• Describe the characteristics of your sample in the methods section of your article.
• Check your variables to investigate whether you meet certain assumptions associated with the statistical techniques you want to perform to answer your research questions.
• Ask specific research questions.

Once the raw data has been accurately entered into SPSS, the next step is to edit and prepare the data so that later analyses can be performed and hypotheses can be tested.

Make sure that you also adjust the codebook for everything you adjust. An alternative is to use the Syntax option, this means that you keep track of all actions to be performed in the Syntax Editor, so that a list of what has been changed is formed.

What statistical methods are there?

Some studies use a single method, but many studies use multiple methods. In any case, it is crucial to choose the right research method.

It is not yet discussed how exactly you apply the research methods. The overview below is intended to give a brief introduction to research methods, so that you can make a choice for which method you need based on that.

The partial correlation is similar to Pearson r, with the difference that you can check the partial correlation for an additional (confound) variable.

What is the procedure for calculating the partial correlation?

1. In the menu at the top of the screen, click on Analyze, then select Correlate and then Partial.
2. Click on the two continuous variables you want to correlate. Click the arrow to move these variables to the Variables box.
3. Click on the variable you want to check for and move it to the Controlling for box.
4. Click on Options.
5. In the Missing Values ​​section, click on Exclude cases pairwise.
6. In the Statistics section, click Zero order correlations.
7. Click Continue and then OK (or Paste to save the Syntax Editor).

With logistic regression you can test models that predict categorical outcomes - consisting of two or more categories. Using logistic regression you can measure how well your set of predictor variables is able to predict or explain your categorical dependent variable. It offers you an indication of the adequacy of your model by mapping the "goodness of fit". Your independent variable can be categorical or continuous, or a combination of both. In this chapter, we show how to perform a binomial (also called binary) logistic regression with a dichotomous dependent variable (i.e. with only two categories or values). If your dependent variable consists of several categories, you will have to perform a multinomial logistic regression. This is not discussed here, but is of course available in SPSS (see the Help menu).

Non-parametric statistics are ideal when your data is measured on a nominal or ordinal scale. They are also useful when you have very small samples and when your data does not meet the assumptions of the parametric techniques.

IBS SPSS provides various non-parametric techniques for different situations. The most commonly used non-parametric techniques are explained below.

T-tests are used to compare the scores of two different groups or conditions. In many research situations, however, we are interested in comparing average scores from more than two groups. In that case, variance analysis (ANOVA) is used. Variance analysis owes its name to the variance (variability in scores) between the different groups (which is expected to be explained by the independent variable) is compared with the variability within each of the groups (which is expected to be caused by chance ). The F ratio - or the variance between the groups divided by the variance within the groups - is calculated. A significant F value means that the null hypothesis, namely that the population means are equal, can be rejected. Because it says nothing about which groups differ from each other, posthoc tests still have to be performed. An alternative to posthoc testing is to perform specific comparisons (or planned comparisons ).

Various types of ANOVA have been discussed in the previous chapters. However, there are situations in which a between-subjects design, a within-subjects design, and a repeated measures design are combined. SPSS allows you to combine two different designs in one analysis. Sometimes this analysis is referred to as a split-plot ANOVA (SPANOVA).

An analysis of covariance is an extension of a regular ANOVA. An analysis of covariance (ANCOVA) analyzes the differences between groups while statistically controlling for an additional continuous variable. This continuous variable, the covariate, may influence the scores of the dependent variable. SPSS uses a procedure similar to regression analysis to remove the variation in the dependent variable scores caused by the covariate, and then performs a normal ANOVA on the adjusted values. With this method, SPSS increases the distinctiveness (power) and the sensitivity of the F-test.