Statistical Package for the Social Sciences or SPSS is the commonly used statistical software by PhD research scholars who need to conduct effective data analysis and interpretation in less time. However, many scholars get stuck right at the stage of beginning SPSS analysis for their theses. This happens when they are unsure of which statistical tests to use for analysing their research data. If you are also facing problems in selecting the right tests, then we guide you through some tips. Read on.
Choosing the Right Tests during SPSS Data Analysis
SPSS is a versatile tool that lets you conduct a variety of analyses, as well as a wide range of data transformations to leave you with varied forms of output. Thus, it is critical for you to choose the right tests to conduct in SPSS in order to get meaningful output. However, every research study follows a specific research design and asks different research questions that may affect the choice of statistical tests. Let us share with you a few basic questions you need to ask yourself and that would help you in making the most suitable test choices.
What kind of research questions do you have to answer?
- Descriptive: These types of research questions are asked when you study some existing characteristics of your population. It is usually done through observational design. For such studies, the commonly used statistical tests based on what you want to examine are the measures of central tendency, percentiles, standard deviation, and frequency.
- Predictive/correlational: These questions are asked when relationships among variables are studied or certain predictions are made on the basis of such studies. If your research questions fall into this type, you may use statistical tests like Pearson’s correlation, Spearman’s correlation, and regression.
- Cause-effect/group differences: These questions are asked when the differences between/among groups are analyzed on certain variables or when the cause-effect relationships are examined. Based on the number of groups and study situations, the commonly used statistical tests, in this case, include the t-test, One-Way ANOVA, Factorial ANOVA, and MANOVA.
What are your variables?
- Variable type: To use the right test for SPSS data analysis, it is important to identify the independent and dependent variables in your study. While not all types of studies are based on variable relationships, you may need to identify them at least in cause-effect and/or correlational research studies.
- The number of variables: The choice of statistical tests may vary based on the number of your independent and dependent variables. Thus, identify all variables clearly.
- Defining variables: Assess how you operationally define each of the variables and how would you measure your dependent variables. You also need to set a normal range of scores for each variable so the data can be compared and analyzed against that range.
What are the characteristics of your research data?
- Measurement level: Identify whether you are using nominal, ordinal, interval or ratio scale of measurement of variables.
- Other data qualities: Check how your data distribution is in case of interval/ratio scale and whether your groups are balanced in case of nominal scale. You should also consider your sample size while checking the normality. Looking at such qualities of your data plays a major role in finalising the statistical tests you would use for data analysis.
Now, based on the above-listed parameters or guidelines, you may assess the need for using either parametric or non-parametric test. Parametric tests like t-test and ANOVA are more suitable when your data follow a normal distribution and meets some other stringent criteria. Still, there are exceptions. Else, you may choose non-parametric tests that are distribution-free. These include tests like correlation, Wilcoxon Signed Rank, Mann-Whitney U, Chi-square, Friedman, and Kruskal-Wallis. We suggest you to know the detailed requirements of using any test before making your final choice.