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Quantitative Analysis of Questionnaires: Techniques to Explore Structures and Relationships [Pehme köide]

(Newcastle University, UK)
  • Formaat: Paperback / softback, 216 pages, kõrgus x laius: 234x156 mm, kaal: 335 g, 124 Tables, black and white; 37 Line drawings, black and white; 25 Halftones, black and white; 72 Illustrations, black and white
  • Ilmumisaeg: 23-Jan-2020
  • Kirjastus: Routledge
  • ISBN-10: 0367022834
  • ISBN-13: 9780367022839
  • Formaat: Paperback / softback, 216 pages, kõrgus x laius: 234x156 mm, kaal: 335 g, 124 Tables, black and white; 37 Line drawings, black and white; 25 Halftones, black and white; 72 Illustrations, black and white
  • Ilmumisaeg: 23-Jan-2020
  • Kirjastus: Routledge
  • ISBN-10: 0367022834
  • ISBN-13: 9780367022839
Bringing together the techniques required to understand, interpret and quantify the processes involved when exploring structures and relationships in questionnaire data, Quantitative Analysis of Questionnaires provides the knowledge and capability for a greater understanding of choice decisions. The ideal companion for non-mathematical students with no prior knowledge of quantitative methods, it highlights how to uncover and explore what lies within data that cannot be achieved through descriptive statistics. This book introduces significance testing, contingency tables, correlations, factor analysis (exploratory and confirmatory), regression (linear and logistic), discrete choice theory and item response theory.

Using simple and clear methodology, and rich examples from a range of settings, this book:











provides hands-on analysis with data sets from both SPSS and Stata packages;





explores how to articulate the calculations and theory around statistical techniques;





offers workable examples in each chapter with concepts, applications and proofs to help produce a higher quality of research outputs;





discusses the use of formulas in the appendix for those who wish to explore a greater mathematical understanding of the concepts.

Quantitative Analysis of Questionnaires is the ideal introductory textbook for any student looking to begin and or improve statistical learning as well as interpretation.
List of illustrations
viii
About the author xiii
About the book xiv
1 Introduction
1(19)
Criteria for statistical testing
4(1)
Types of data
5(1)
Data sets used as example studies
6(10)
Missing data
16(4)
2 Statistical significance and contingency tables
20(10)
Statistical significance
20(1)
Contingency tables
21(6)
How to report contingency tables
27(3)
3 Factor analysis: Exploratory
30(21)
Exploratory factor analysis
31(1)
Discovering latent factors
32(9)
Factor analysis for data reduction
41(3)
Calculating and using latent factors in future analysis
44(2)
Missing values
46(1)
How to report factor analysis
47(4)
4 Correlation and linear regression
51(28)
Scatter diagram
51(3)
Correlation
54(10)
Spearman's rank correlation coefficient (Spearman's rho)
64(1)
Kendall's Tau correlation (x)
64(1)
Correlations between two variables of different scales
65(1)
How to report correlations
65(1)
Calculating correlation with Stata and SPSS
66(1)
Linear regression
67(4)
Multicollinearity
71(1)
Multivariate linear regression
71(6)
Linear regression sample size conditions
77(1)
How to report linear regression
77(2)
5 Factor analysis: Confirmatory
79(21)
Constructing First Order CFA Models
80(7)
More complex CFA models
87(3)
Uncovering structures in questionnaires
90(5)
Longitudinal measurement invariance
95(1)
How to report confirmatory factor analysis
96(4)
6 Regression: Logistic
100(23)
Simple logistic regression
100(10)
Multivariable analysis
110(7)
Complex multinomial models
117(3)
How to report logistic regression
120(3)
7 Making choices: Discrete choice theory
123(27)
Stated and revealed preference
124(1)
A simple consumer choice model
124(8)
Multinomial logistic regression model with socio-economic factors
132(6)
Ordered logit choice model
138(9)
The range of discrete choice models
147(1)
How to calculate ordered and ordinal regression
148(2)
8 Item response theory
150(22)
Item response model
151(6)
Differential item testing
157(2)
Graded Response Model (GRM)
159(2)
Partial Credit Models (PCM)
161(1)
Information function
161(5)
Reliability of measures when collapsing Likert scale categories
166(6)
Appendix
172(33)
Multiple imputation
172(1)
Distribution fitting
173(3)
Factor analysis
176(4)
Correlation
180(4)
Linear regression
184(4)
Sample size
188(1)
Confirmatory Factor Analysis (CFA)
189(1)
Logistic regression
190(6)
Marginal effects
196(1)
Discrete choice theory
197(2)
Longitudinal data analysis
199(2)
Item response theory
201(4)
References 205(9)
Index 214
Steve Humble MBE is a senior lecturer and Head of Education at Newcastle University, UK. He teaches undergraduate and graduate advanced quantitative methods and is an expert around collecting and analysing data from large samples using advanced statistical techniques in both SPSS and Stata.