Statistics is sometimes given a bad name because it is associated with complex mathematical knowledge such as probability theory. For many students in the social sciences however, knowledge about statistics is not an end in itself but a practical means to answer research questions. Therefore, the focus of Statistical Tools is not on complex statistical theory, but on the proper use of common statistical applications and on the correct interpretation of statistical results. As such, no mathematical knowledge is required to understand the content of this textbook. Using survey data sets from recent years, we illustrate why statistics is an indispensable tool in social science research. All data (in SPSS format) are available on the Internet, allowing the reader to replicate the statistical results presented throughout this text.
Manfred te Grotenhuis is an assistant professor of quantitative data analysis at Radboud University Nijmegen, the Netherlands, and an affiliate of the Interuniversity Center for Social Science Theory and Methodology (ICS). Recent statistical contributions include articles in Acta Psychiatrica Scandinavica, American Journal of Sociology, American Sociological Review, European Sociological Review, European Societies, Journal for the Scientific Study of Religion, Quality & Quantity, Review of Religious Research, and Psychiatric Services.
Theo van der Weegen is head of the statistical research department at Radboud University Nijmegen, the Netherlands. He has supervised statistical research projects for more than 25 years. Both authors have been teaching statistics to students from the Faculty of Social Sciences at Radboud University Nijmegen since 1995.
The data used in this textbook, the exercises, and other advanced statistical tools are available online at: www.ru.nl/mt/statistics/home.
De Nederlandstalige variant van Statistic tools is Statistiek als hulpmiddel.
Biennial University Teaching Award for Manfred te Grotenhuis
Radboud University Nijmegen has granted the biennial University Teaching Award to Manfred te Grotenhuis. The award honors and recognizes distinguished teaching and provides incentive and encouragement for achieving excellence in this field.
Manfred te Grotenhuis has outstanding teaching skills and he successfully applies examples from state-of-the-art research in his (7) courses which all are about quantitative data analysis (bachelor 1-3, master and research master). He wrote 4 books on statistics which are widely used. One of his students went as far to say ‘that it is unbelievable that dull statistics can be that interesting!'.Short movie about Manfred te Grotenhuis
Manfred te Grotenhuis is winnaar van de Facultaire Onderwijsprijs 2009 van de Radboud Universiteit Nijmegen voor beste onderwijsinnovatie.
1 Statistical Data
1.2 Four Levels of Measurement
1.3 Selecting Units of Analysis: Random Sampling
1.4 Collecting Statistical Data
1.5 Data Quality
1.6 From Collecting Data to Answering Research Questions
2 Descriptive Statistics
2.2 Graphical Description of a Single Variable
2.3 Numerical Description of a Single Variable
2.3.1 Measures of Central Tendency
2.3.2 Measures of Variability
2.3.3 Measures of Relative Standing
2.4 Statistical Relations between Two Variables
2.4.1 Graphical Description of a Bivariate Relation
3 Inferential Statistics
3.1 Introduction to Statistical Inference
3.2 One-Sample tests for Mean and Proportion
3.2.1 Test for a mean
3.2.2 Test for a proportion
3.3 Tests for Comparing Two Means
3.3.1 Paired Samples T-test (two dependent groups)
3.3.2 Two-Sample T-test (two independent groups)
3.3.3 Analysis of Variance (three or more independent groups)
3.4 Measures of Association for Nominal and Ordinal Variables
3.4.1 Associations in Contingency Tables
3.4.2 Measures of Association for Nominal Variables
3.4.3 Measures of Association for Ordinal Variables
3.5 Measures of Association for Interval and Ratio Variables
3.5.1 Pearson's Correlation Coefficient
3.5.2 Linear Regression Analysis
3.5.3 Odds Ratio
3.6 Multivariate Analysis
3.6.1 Five Different Causal Multivariate Models
3.6.2 Multiple Linear Regression Analysis
Concluding Remarks on Statistical Tools