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Comprehensive Guide to Statistical Analysis Methods in Social Sciences

This guide covers various statistical analysis methods including χ², logistic regression, nonparametric tests, and more. It provides insights on analysis methods for different measurement levels and variables in social science research.

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Comprehensive Guide to Statistical Analysis Methods in Social Sciences

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  1. Non-metric Nonparametric tests, χ², logistic analysis of variance, loglinear analysis Non-metric 1 Binary Mann-Whitney test, χ² Metric Logistic regression analysis Non-metric χ², Cramér’s coefficient C Binary 1 Dependent variable Independent variable Analysis method Number Measurement level* Number Measurement level Metric χ², logistic regression analysis Non-Metric χ² Counts 1 Metric Regression Non-metric Cramér’s coefficient C 1 Nominal 1 Metric (Logistic) regression analysis, loglikelihood Binary Wilcoxon’s two sample test, χ², Mann-Whitney test, Kolmogorov-Smirnov test Counts Nonparametric correlation Ordinal 1 Nominal Kruskal-Wallis test, analysis of variance Ordinal Nonparametric correlation, Spearman’s rho, Kendall’s tau Metric Nonparametric correlation, isotonic regression analysis Binary Correlation, t-test Counts Regression analysis Metric 1 Nominal Analysis of variance Ordinal Nonparametric correlation Metric Regression analysis, correlation

  2. Non-metric Logistic analysis of variance, loglinear analysis ,canonical correlation analysis Non-metric 2 or more Metric Logistic regression analysis 1 Nominal 2 or more Metric Discriminant analysis Non-metric Analysis of varaince Metric 2 or more Dependent variable Independent variable Analysis method Number Measurement level* Number Measurement level Metric Multiple regression analysis 1 Non-metric Multivariate regression with dummy variables Non-metric Non-metric Multivariate analysis of variance with dummy variables 2 or more 2 or more Metric Multivariate multiple regression with dummy variables Non-metric Multivariate analysis of variance 1 Metric Metric Multivariate regression analysis Non-metric Multivariate analysis of variance 2 or more Metric Multivariate multiple regression analysis, Canonical correlation analysis, redundancy analysis

  3. Binary Frequencies, proportions, counts Nonparametric tests, binomial test Counts Frequencies, mean, median, mode Nonparametric tests, t-test 1 Nominal Frequencies, proportions, mode Nonparametric tests, χ² Ordinal Frequencies, mean, median, mode Nonparametric tests, binomial test Metric Mean, median, mode, variance, kurtosis Confidence intervals, t-test Number of Measurement Analysis methods variables level* Description Confirmation Binary Tetrachoric correlation, crosstabulations Nonparametric tests Counts Crosstabulations Nonparametric tests, χ² 2 Nominal Contingency tables, correspondence analysis Loglinear analysis, χ² Ordinal Kendall’s tau, Spearman’s rho Nonparametric tests Metric Correlation coefficient, scatter plot Correlation coefficient Binary Rasch models, Guttman scaling Loglinear analysis Counts Crosstabulations Factor analysis More than 2 Ordinal Nonlinear principal component analysis, multidimensional scaling Factor analysis Metric Principal component analysis, one-dimensional scaling, cluster analysis Factor analysis, F-test 3 Nominal Latent class analysis, contingency tables, correspondence analysis, homogeneity analysis Loglinear analysis 4 or 5 Nominal Latent class analysis, homogeneity analysis Loglinear analysis 6 or more Nominal Homogeneity analysis Loglinear analysis

  4. Analysis methods suitable for issues with both dependent en independent variables. Derived from Van den Berg (1992, Appendix) Analysis methods for issues without distinction between dependent en independent variables. Derived from Van den Berg (1992, Appendix) * In this diagram, binary variables, counts, nominal variables and ordinal variables have been distinguished in addition to the rough distinction between metric (or numerical) and non-metric (or categorical) variables. Ordinal variables are most often treated like metric variables, if their distributions are normal. However, there (often) are special analysis methods for cases in which ordinal variables are involved. Binary variables, nominal variables and counts are often considered as non-metric variables, so if there is no analysis method which is designed specifically for one of these measurement levels, please use the method suitable for a non-metric variable. (See also Van de Berg, 1992, pages 31 and 32). Gerda van den Berg (1992). Choosing an Analysis Method: An Empirical Study of Statisticians' Ideas in View of the Design of Computerized Support. Leiden: DSWO Press (ISBN 9090046348)

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