Statistics in Psychology Using R and SPSS
Published on 27. October 2011
448 pages
978-1-119-95202-2 (ISBN)
Description
Statistics in Psychology covers all statistical methodsneeded in education and research in psychology. This book looks atresearch questions when planning data sampling, that is to designthe intended study and to calculate the sample sizes in advance. Inother words, no analysis applies if the minimum size is notdetermined in order to fulfil certain precision requirements.
The book looks at the process of empirical research into thefollowing seven stages:
* Formulation of the problem
* Stipulation of the precision requirements
* Selecting the statistical model for the planning andanalysis
* The (optimal) design of the experiment or survey
* Performing the experiment or the survey
* Statistical analysis of the observed results
* Interpretation of the results.
More details
Other editions
Additional editions
Dieter Rasch | Klaus Kubinger | Takuya Yanagida
Statistics in Psychology Using R and SPSS
Book
12/2011
Wiley
€100.50
Shipment within 10-20 days
Software
10/2011
Wiley
€75.73
No shipping information available
Persons
Dieter Rasch, Department of Applied Statistics, University of Life Sciences, Vienna
Klaus D. Kubinger, Division of Psychological Assessment and Applied Psychometrics, University of Vienna
Takuya Yanagida, Division of Psychological Assessment and Applied Psychometrics, University of Vienna
Content
Introduction.
1 Concept of the Book.
2 Measuring in Psychology.
2.1 Types of psychological measurements.
2.2 Measurement techniques in psychological assessment.
2.3 Quality criteria in psychometrics.
2.4 Additional psychological measurement techniques.
2.5 Statistical models of measurement with psychologicalroots.
3 Psychology: An Empirical Science.
3.1 Gain of insight in psychology.
3.2 Steps of empirical research.
4 Definition: Character, Chance, Experiment, andSurvey.
4.1 Nominal scale.
4.2 Ordinal scale.
4.3 Interval scale.
4.4 Ratio scale.
4.5 Characters and factors.
II Descriptive Statistics.
5 Numerical and graphical Data Analysis.
5.1 Introduction to data analysis.
5.2 Frequencies and empirical distributions.
5.3 Statistics.
5.4 Frequency distribution for several characters.
III Inferential Statistics for one Character.
6 Probability and distribution.
6.1 Relative frequencies and probabilities.
6.2 Random variable and theoretical distributions.
6.3 Quantiles of theoretical distribution functions.
6.4 Mean and variance of theoretical distributions.
6.5 Estimation of unknown parameters.
7 Assumptions: Random Sampling and Randomization.
7.1 Simple random sampling in surveys.
7.2 Principles of random sampling and randomization.
8 One Sample from one Population.
8.1 Introduction.
8.2 The Parameter mof acharacter modeled by a normallydistributed random variable.
8.3 Planning a study for hypothesis testing with respect tom.
8.4 Sequential tests for the unknown parameter m.
8.5 Estimation, hypothesis testing, planning the study, andsequential testing concerning other parameters.
9 Two Samples from two Populations.
9.1 Hypothesis testing, study planning and sequential testingregarding the unknown parameters m1 andm2.
9.2 Hypothesis testing, study planning and sequential testingfor other parameters.
9.3 Equivalence testing.
10 Samples from more than two Populations.
10.1 The various problem situations.
10.2. Selection procedures.
10.3 Multiple comparisons of means.
10.4 Analysis of variance.
IV Descriptive and Inferential Statistics for twoCharacters.
11 Regression and Correlation.
11.1 Introduction.
11.2 Regression model.
11.3 Correlation coefficients and measures of association.
11.4 Hypothesis testing and planning the study concerningcorrelation coefficients.
11.5 Correlation analysis in two samples.
V Inferential Statistics for more than twoCharacters.
12 One Sample from one Population.
12.1 Association between three or more characters.
12.2 Hypothesis testing concerning a vector of meansm.
12.3 Comparisons of means and "homological" methods for matchedobservations.
13 Samples from more than one Population.
13.1 General linear model.
13.2 Analysis of covariance.
13.3. Multivariate analysis of variance.
13.4 Discriminant analysis.
VI Model Generation and Theory-Generating Procedures.
14 Model Generation.
14.1 Theoretical basics of model generation.
14.2 Methods for determining the quality and excellence of amodel.
14.2.1 Goodness of fit tests.
14.2.2 Coefficients of the goodness of fit.
14.2.3 Cross-validation.
14.4 Simulation: Non-analytical solutions to statisticalproblems.
15 Theory-Generating Procedures.
15.1 Descriptive statistics' methods.
15.2 Methods of inferential statistics.
