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E-grāmata: Statistics in Psychology Using R and SPSS [Wiley Online]

(University of Natural Resources and Applied Life Sciences, Austria), (Division of Psychological Assessment, Austria), (Division of Psychological Assessment, Austria)
  • Formāts: 568 pages
  • Izdošanas datums: 09-Dec-2011
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119979633
  • ISBN-13: 9781119979630
Citas grāmatas par šo tēmu:
  • Wiley Online
  • Cena: 97,78 €*
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Statistics in Psychology Using R and SPSSPalielināt
  • Formāts: 568 pages
  • Izdošanas datums: 09-Dec-2011
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119979633
  • ISBN-13: 9781119979630
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119979630
Statistics in Psychology covers all statistical methods needed in education and research in psychology. This book looks at research questions when planning data sampling, that is to design the intended study and to calculate the sample sizes in advance. In other words, no analysis applies if the minimum size is not determined in order to fulfil certain precision requirements.

The book looks at the process of empirical research into the following seven stages:

  • Formulation of the problem
  • Stipulation of the precision requirements
  • Selecting the statistical model for the planning and analysis
  • The (optimal) design of the experiment or survey
  • Performing the experiment or the survey
  • Statistical analysis of the observed results
  • Interpretation of the results.
Preface x
Acknowledgments xii
Part I INTRODUCTION
1(42)
1 Concept of the Book
3(9)
References
11(1)
2 Measuring in Psychology
12(10)
2.1 Types of Psychological Measurements
13(1)
2.2 Measurement Techniques in Psychological Assessment
13(3)
2.2.1 Psychological Tests
13(1)
2.2.2 Personality Questionnaires
14(1)
2.2.3 Projective Techniques
15(1)
2.2.4 Systematical Behavior Observation
16(1)
2.3 Quality Criteria in Psychometrics
16(1)
2.4 Additional Psychological Measurement Techniques
17(3)
2.4.1 Sociogram
17(1)
2.4.2 Survey Questionnaires
17(1)
2.4.3 Ratings
18(1)
2.4.4 Q-Sort
18(1)
2.4.5 Semantic Differential
19(1)
2.4.6 Method of Pair - Wise Comparison
19(1)
2.4.7 Content Analysis
19(1)
2.5 Statistical Models of Measurement with Psychological Roots
20(2)
References
20(2)
3 Psychology -- An Empirical Science
22(8)
3.1 Gain of Insight in Psychology
23(3)
3.2 Steps of Empirical Research
26(4)
References
29(1)
4 Definition -- Character, Chance, Experiment, and Survey
30(13)
4.1 Nominal Scale
35(1)
4.2 Ordinal Scale
35(2)
4.3 Interval Scale
37(1)
4.4 Ratio Scale
38(2)
4.5 Characters and Factors
40(3)
References
41(2)
Part II DESCRIPTIVE STATISTICS
43(56)
5 Numerical and Graphical Data Analysis
45(54)
5.1 Introduction to Data Analysis
45(4)
5.2 Frequencies and Empirical Distributions
49(28)
5.2.1 Nominal-Scaled Characters
50(5)
5.2.2 Ordinal-Scaled Characters
55(8)
5.2.3 Quantitative Characters
63(10)
5.2.4 Principles of Charts
73(1)
5.2.5 Typical Examples of the Use of Tables and Charts
74(3)
5.3 Statistics
77(17)
5.3.1 Mean and Variance
77(2)
5.3.2 Other Measures of Location and Scale
79(12)
5.3.3 Statistics Based on Higher Moments
91(3)
5.4 Frequency Distribution for Several Characters
94(5)
References
97(2)
Part III INFERENTIAL STATISTICS FOR ONE CHARACTER
99(202)
6 Probability and Distribution
101(29)
6.1 Relative Frequencies and Probabilities
101(6)
6.2 Random Variable and Theoretical Distributions
107(16)
6.2.1 Binomial Distribution
109(7)
6.2.2 Normal Distribution
116(7)
6.3 Quantiles of Theoretical Distribution Functions
123(2)
6.4 Mean and Variance of Theoretical Distributions
125(1)
6.5 Estimation of Unknown Parameters
126(4)
References
129(1)
7 Assumptions -- Random Sampling and Randomization
130(17)
7.1 Simple Random Sampling in Surveys
132(2)
7.2 Principles of Random Sampling and Randomization
134(13)
7.2.1 Sampling Methods
134(6)
7.2.2 Experimental Designs
140(6)
References
146(1)
8 One Sample from One Population
147(53)
8.1 Introduction
147(1)
8.2 The Parameter μ of a Character Modeled by a Normally Distributed Random Variable
148(21)
8.2.1 Estimation of the Unknown Parameter μ
148(2)
8.2.2 A Confidence Interval for the Unknown Parameter μ
150(6)
8.2.3 Hypothesis Testing Concerning the Unknown Parameter μ
156(9)
8.2.4 Test of a Hypothesis Regarding the Unknown Parameter μ in the Case of Primarily Mutually Assigned Observations
165(4)
8.3 Planning a Study for Hypothesis Testing with Respect to μ
169(10)
8.4 Sequential Tests for the Unknown Parameter μ
179(4)
8.5 Estimation, Hypothesis Testing, Planning the Study, and Sequential Testing Concerning Other Parameters
183(17)
8.5.1 The Unknown Parameter σ2
183(1)
8.5.2 The Unknown Parameter p of a Dichotomous Character
184(5)
8.5.3 The Unknown Parameter p of a Dichotomous Character which is the Result of Paired Observations
189(3)
8.5.4 The Unknown Parameter pj of a Multi-Categorical Character
192(3)
8.5.5 Test of a Hypothesis about the Median of a Quantitative Character
195(1)
8.5.6 Test of a Hypothesis about the Median of a Quantitative Character which is the Result of Paired Observations
196(3)
References
199(1)
9 Two Samples from Two Populations
200(35)
9.1 Hypothesis Testing, Study Planning, and Sequential Testing Regarding the Unknown Parameters μ1 and μ2
201(13)
9.2 Hypothesis Testing, Study Planning, and Sequential Testing for Other Parameters
214(16)
9.2.1 The Unknown Location Parameters for a Rank-Scaled Character
214(4)
9.2.2 The Unknown Parameters σ21 and σ22
218(3)
9.2.3 The Unknown Parameters p1 and p2 of a Dichotomous Character
221(8)
9.2.4 The Unknown Parameters pi of a Multi-Categorical Nominal-Scaled Character
229(1)
9.3 Equivalence Testing
230(5)
References
233(2)
10 Samples from More than Two Populations
235(66)
10.1 The Various Problem Situations
236(1)
10.2 Selection Procedures
237(1)
10.3 Multiple Comparisons of Means
238(3)
10.4 Analysis of Variance
241(60)
10.4.1 One-Way Analysis of Variance
241(22)
10.4.2 One-Way Analysis of Variance for Ordinal-Scaled Characters
263(2)
10.4.3 Comparing More than Two Populations with Respect to a Nominal-Scaled Character
265(1)
10.4.4 Two-Way Analysis of Variance
266(23)
10.4.5 Two-Way Analysis of Variance for Ordinal-Scaled Characters
289(1)
10.4.6 Bivariate Comparison of Two Nominal-Scaled Factors
289(1)
10.4.7 Three-Way Analysis of Variance
289(10)
References
299(2)
Part IV DESCRIPTIVE AND INFERENTIAL STATISTICS FOR TWO CHARACTERS
301(60)
11 Regression and Correlation
303(58)
11.1 Introduction
303(5)
11.2 Regression Model
308(10)
11.3 Correlation Coefficients and Measures of Association
318(31)
11.3.1 Linear Correlation in Quantitative Characters
318(7)
11.3.2 Monotone Relation in Quantitative Characters and Relation between Ordinal-Scaled Characters
325(1)
11.3.3 Relationship between a Quantitative or Ordinal-Scaled Character and a Dichotomous Character
326(4)
11.3.4 Relationship between a Quantitative Character and a Multi-Categorical Character
330(5)
11.3.5 Correlation between Two Nominal-Scaled Characters
335(10)
11.3.6 Nonlinear Relationship in Quantitative Characters
345(4)
11.4 Hypothesis Testing and Planning the Study Concerning Correlation Coefficients
349(8)
11.5 Correlation Analysis in Two Samples
357(4)
References
360(1)
Part V INFERENTIAL STATISTICS FOR MORE THAN TWO CHARACTERS
361(86)
12 One Sample from One Population
363(38)
12.1 Association between Three or More Characters
363(22)
12.1.1 Partial Correlation Coefficient
365(6)
12.1.2 Comparison of the Association of One Character with Each of Two Other Characters
371(1)
12.1.3 Multiple Linear Regression
372(2)
12.1.4 Intercorrelations
374(3)
12.1.5 Canonical Correlation Coefficient
377(1)
12.1.6 Log-Linear Models
377(8)
12.2 Hypothesis Testing Concerning a Vector of Means μ
385(3)
12.3 Comparisons of Means and `Homological' Methods for Matched Observations
388(13)
12.3.1 Hypothesis Testing Concerning Means
388(10)
12.3.2 Hypothesis Testing Concerning the Position of Ordinal-Scaled Characters
398(2)
References
400(1)
13 Samples from More than One Population
401(46)
13.1 General Linear Model
401(2)
13.2 Analysis of Covariance
403(11)
13.3 Multivariate Analysis of Variance
414(13)
13.4 Discriminant Analysis
427(20)
References
445(2)
Part VI MODEL GENERATION AND THEORY-GENERATING PROCEDURES
447(73)
14 Model Generation
449(22)
14.1 Theoretical Basics of Model Generation
449(5)
14.1.1 Generalized Linear Model
450(3)
14.1.2 Model with Latent Variables
453(1)
14.2 Methods for Determining the Quality and Excellence of a Model
454(10)
14.2.1 Goodness of Fit Tests
454(4)
14.2.2 Coefficients of Goodness of Fit
458(4)
14.2.3 Cross-Validation
462(2)
14.3 Simulation -- Non-Analytical Solutions to Statistical Problems
464(7)
References
470(1)
15 Theory-Generating Methods
471(49)
15.1 Methods of Descriptive Statistics
471(23)
15.1.1 Cluster Analysis
471(11)
15.1.2 Factor Analysis
482(10)
15.1.3 Path Analysis
492(2)
15.2 Methods of Inferential Statistics
494(26)
15.2.1 Further Analysis Methods for Classifying Research Units
494(7)
15.2.2 Confirmatory Factor Analysis
501(5)
15.2.3 Models of Item Response Theory
506(12)
References
518(2)
Appendix A Data Input 520(9)
Appendix B Tables 529(9)
Appendix C Symbols and Notation 538(4)
References 542(5)
Index 547
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
Permanent link: https://www.kriso.lv/db/9781119979630_pe.html
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