Introduction to Statistics with SPSS for Social Science 1st edition by Gareth Norris, Faiza Qureshi, Dennis Howitt, Prof Duncan Cramer ISBN 1408237598 978-1408237595
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Introduction to Statistics with SPSS for Social Science 1st edition by Gareth Norris, Faiza Qureshi, Dennis Howitt, Prof Duncan Cramer – Ebook PDF Instant Download/Delivery: 1408237598 , 978-1408237595
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ISBN 10: 1408237598
ISBN 13: 978-1408237595
Author: Gareth Norris, Faiza Qureshi, Dennis Howitt, Prof Duncan Cramer
This is a complete guide to statistics and SPSS for social science students. Statistics with SPSS for Social Science provides a step-by-step explanation of all the important statistical concepts, tests and procedures. It is also a guide to getting started with SPSS, and includes screenshots to illustrate explanations. With examples specific to social sciences, this text is essential for any student in this area.
Introduction to Statistics with SPSS for Social Science 1st Table of contents:
1 Descriptive statistics
1 Why you need statistics Types of data
1.1 Introduction
1.2 Variables and measurement
Levels of measurement
1.3 Statistical significance
1.4 SPSS guide: an introduction
What is SPSS?
To access SPSS
To enter data
Moving within a window with the mouse
Moving within a window using the keyboard keys with the mouse
Saving data to disk
Opening up a data file
Using Variable View to create and label variables
More on Data View
A simple statistical calculation
The SPSS output
Major types of analysis and suggested SPSS procedures
2 Describing Variables Tables and diagrams
2.1 Introduction
2.2 Choosing tables and diagrams
Tables and diagrams for nominal (category) data
Pie diagrams
Bar charts
Tables and diagrams for numerical score data
2.3 Errors to avoid
2.4 SPSS analysis
The data to be analysed
Entering summarised categorical or frequency data by weighting
Percentage frequencies
Interpreting the output
Reporting the output
2.5 Pie diagram of category data
2.6 Bar chart of category data
2.7 Histograms
3 Describing variables numerically Averages, variation and spread
3.1 Introduction: mean, median and mode
The arithmetic mean
The median
The mode
3.2 Comparison of mean, median and mode
3.3 The spread of scores: variability
Mean deviation
Variance
3.4 Probability
The principles of probability
3.5 Confidence intervals
3.6 SPSS analysis
The data to be analysed
Entering the data
Conducting the analysis
Interpreting the output
Reporting the output
4 Shapes of distributions of scores
4.1 Introduction
4.2 Histograms and frequency curves
4.3 The normal curve
4.4 Distorted curves
Skewness
Kurtosis (or steepness/shallowness)
4.5 Other frequency curves
Bimodal and multimodal frequency distributions
Cumulative frequency curves
Percentiles
4.6 SPSS analysis
The data to be analysed
Entering the data
Frequency tables
Interpreting the output
Reporting the output
5 Standard deviation, z-scores and standard error The standard unit of measurement in statistics
5.1 Introduction
5.2 What is standard deviation?
5.3 When to use standard deviation
5.4 When not to use standard deviation
5.5 Data requirements for standard deviation
5.6 Problems in the use of standard deviation
5.7 SPSS analysis
The data to be analysed
Entering the data
Standard deviation
Interpreting the output
Z-scores
Other features
Reporting the output
5.8 Standard error: the standard deviation of the means of samples
5.9 When to use standard error
5.10 When not to use standard error
5.11 SPSS analysis for standard error
The data to be analysed
Entering the data
Estimated standard error of the mean
Interpreting the output
Reporting the output
6 Relationships between two or more variables Diagrams and tables
6.1 Introduction
6.2 The principles of diagrammatic and tabular presentation
6.3 Type A: both variables numerical scores
6.4 Type B: both variables nominal categories
6.5 Type C: one variable nominal categories, the other numerical scores
6.6 SPSS analysis
The data to be analysed
Entering the data
Weighting the data
Crosstabulation with frequencies
Displaying frequencies as a percentage of the total number
Displaying frequencies as a percentage of the column total
Compound (stacked) percentage bar chart
Compound histogram (clustered bar chart)
7 Correlation coefficients The Pearson correlation and Spearman’s rho
7.1 Introduction
7.2 Principles of the correlation coefficient
Covariance
7.3 Some rules to check out
7.4 Coefficient of determination
7.5 Data requirements for correlation coefficients
7.6 SPSS analysis
The data to be analysed
Entering the data
Pearson’s correlation
Interpreting the output
Reporting the output
7.7 Spearman’s rho – another correlation coefficient
7.8 SPSS analysis for Spearman’s rho
Calculation
Interpreting the output
Reporting the output
7.9 Scatter diagram using SPSS
Interpreting the output
Reporting the output
7.10 Problems in the use of correlation coefficients
8 Regression and standard error
8.1 Introduction
8.2 Theoretical background and regression equations
8.3 When and when not to use simple regression
8.4 Data requirements for simple regression
8.5 Problems in the use of simple regression
8.6 SPSS analysis
The data to be analysed
Entering the data
Simple regression
Interpreting the output
8.7 Regression scatterplot
Interpreting the output
Reporting the output
8.8 Standard error: how accurate are the predicted score and the regression equations?
2 Inferential statistics
9 The analysis of a questionnaire/survey project
9.1 Introduction
9.2 The research project
9.3 The research hypothesis
9.4 Initial variable classification
9.5 Further coding of data
9.6 Data cleaning
9.7 Data analysis
A relatively simple approach
A more complex approach
An alternative complex approach
9.8 SPSS analysis
10 The related t-test Comparing two samples of correlated/related scores
10.1 Introduction
10.2 Dependent and independent variables
10.3 Theoretical considerations
10.4 SPSS analysis
Entering the data
The related t-test
Interpreting the output
Reporting the output
10.5 A cautionary note
11 The unrelated t-test Comparing two samples of unrelated/uncorrelated scores
11.1 Introduction
11.2 Theoretical considerations
11.3 Standard deviation and standard error
11.4 A cautionary note
11.5 Data requirements for the unrelated t-test
11.6 When not to use the unrelated t-test
11.7 Problems in the use of the unrelated t-test
11.8 SPSS analysis
The data to be analysed
Entering the data
Carrying out the unrelated t-test
Interpreting the output
Reporting the output
12 Chi-square Differences between samples of frequency data
12.1 Introduction
12.2 Theoretical considerations
12.3 When to use chi-square
12.4 When not to use chi-square
12.5 Data requirements for chi-square
12.6 Problems in the use of chi-square
12.7 SPSS analysis
The data to be analysed
Entering the data of Table 12.7 using the ‘Weighting Cases’ procedure
Entering the data of Table 12.7 case by case
Conducting a chi-square on Table 12.7
Interpreting the output
Reporting the output
12.8 The Fisher exact probability test
12.9 SPSS analysis for the Fisher exact test
Interpreting the output
Reporting the output
12.10 Partitioning chi-square
12.11 Important warnings
12.12 Alternatives to chi-square
12.13 Chi-square and known populations
Recommended further reading
3 Introduction to analysis of variance
13 Analysis of variance (ANOVA) Introduction to one-way unrelated or uncorrelated ANOVA
13.1 Introduction
13.2 Theoretical considerations
13.3 Degrees of freedom
13.3 When to use one-way ANOVA
13.5 When not to use one-way ANOVA
13.6 Data requirements for one-way ANOVA
13.7 Problems in the use of one-way ANOVA
13.8 SPSS analysis
The data to be analysed
Quick calculation methods for ANOVA
13.9 Computer analysis for one-way unrelated ANOVA
Interpreting the output
Reporting the output
14 Two-way analysis of variance for unrelated/uncorrelated scores Two studies for the price of one?
14.1 Introduction
14.2 Theoretical considerations
14.3 Steps in the analysis
Step 1
Step 2
Step 3
Step 4
Step 5
14.4 When to use two-way ANOVA
14.5 When not to use two-way ANOVA
14.6 Data requirements for two-way ANOVA
14.7 Problems in the use of two-way ANOVA
14.8 SPSS analysis
14.9 Computer analysis for two-way unrelated ANOVA
Interpreting the output
Editing the graph
Reporting the output
14.10 Three or more independent variables
14.11 Multiple-comparisons testing in ANOVA
Multiple-comparisons tests using SPSS
Interpreting the output
Reporting the output
15 Analysis of covariance (ANCOVA) Controlling for additional variables
15.1 Introduction
15.2 Example of the analysis of covariance
15.3 When to use ANCOVA
15.4 When not to use ANCOVA
15.5 Data requirements for ANCOVA
15.6 SPSS analysis
The data to be analysed
One-way ANCOVA
Testing that the slope of the regression line within the cells is similar
Interpreting the output
Running ANCOVA
Interpreting the output from running ANCOVA
Reporting the output
Recommended further reading
16 Multivariate analysis of variance (MANOVA)
16.1 Introduction
16.2 Questions for MANOVA
Why not do several ANOVAs?
How to combine dependent variables?
16.3 MANOVA’s two stages
Stage 1: MANOVA
Stage 2: The relative importance of each dependent variable
16.4 Doing MANOVA
Step 1
Step 2
Step 3
16.5 When to use MANOVA
16.6 When not to use MANOVA
16.7 Data requirements for MANOVA
16.8 Problems in the use of MANOVA
16.9 SPSS analysis
The data to be analysed
Applying MANOVA
Interpreting the output
Reporting the output
Recommended further reading
4 More advanced statistics and techniques
17 Partial correlation Spurious correlation, third or confounding variables (control variables), suppressor variables
17.1 Introduction
17.2 Theoretical considerations
17.3 The calculation
17.4 Multiple control variables
17.5 Suppressor variables
17.6 An example from the research literature
17.7 When to use partial correlation
17.8 When not to use partial correlation
17.9 Data requirements for partial correlation
17.10 Problems in the use of partial correlation
17.11 SPSS analysis
The data to be analysed
Partial correlation
Interpreting the output
Reporting the output
18 Factor analysis Simplifying complex data
18.1 Introduction
18.2 Data issues in factor analysis
18.3 Concepts in factor analysis
18.4 Decisions, decisions, decisions
1 Rotated or unrotated factors?
2 Orthogonal or oblique rotation?
3 How many factors?
4 Communality
1 Factor scores
18.5 When to use factor analysis
18.6 When not to use factor analysis
18.7 Data requirements for factor analysis
18.8 Problems in the use of factor analysis
18.9 SPSS analysis
The data to be analysed
Principal component analysis with orthogonal rotation
Interpreting the output
Reporting the output
Recommended further reading
19 Multiple regression and multiple correlation
19.1 Introduction
19.2 Theoretical considerations
Regression equations
Selection
19.3 Stepwise multiple regression example
19.4 Reporting the results
19.5 What is stepwise multiple regression?
19.6 When to use stepwise multiple regression
19.7 When not to use stepwise multiple regression
19.8 Data requirements for stepwise multiple regression
19.9 Problems in the use of stepwise multiple regression
19.10 SPSS analysis
The data to be analysed
Stepwise multiple regression analysis
Interpreting the output
Reporting the output
19.11 What is hierarchical multiple regression?
19.12 When to use hierarchical multiple regression
19.13 When not to use hierarchical multiple regression
19.14 Data requirements for hierarchical multiple regression
19.15 Problems in the use of hierarchical multiple regression
19.16 SPSS analysis
The data to be analysed
Hierarchical multiple regression analysis
Interpreting the output
Reporting the output
Recommended further reading
20 Multinomial logistic regression Distinguishing between several different categories or groups
20.1 Introduction
20.2 Dummy variables
20.3 What can multinomial logistic regression do?
20.4 Worked example
20.5 Accuracy of the prediction
20.6 How good are the predictors?
20.7 The prediction
20.8 What have we found?
20.9 Reporting the results
20.10 When to use multinomial logistic regression
20.11 When not to use multinomial logistic regression
20.12 Data requirements for multinomial logistic regression
20.13 Problems in the use of multinomial logistic regression
20.14 SPSS analysis
The data to be analysed
Entering the data
Stepwise multinomial logistic regression
Interpreting the output
Reporting the output
21 Binomial logistic regression
21.1 Introduction
21.2 Simple logistic regression
21.3 Typical example
21.4 Applying the logistic regression procedure
21.5 The regression formula
21.6 Reporting the results
21.7 When to use binomial logistic regression
21.8 When not to use binomial logistic regression
21.9 Data requirements for binomial logistic regression
21.10 Problems in the use of binomial logistic regression
21.11 SPSS analysis
The data to be analysed
Binomial logistic regression
Interpreting the output
Reporting the output
22 Log-linear methods The analysis of complex contingency tables
22.1 Introduction
22.2 A two-variable example
Step 1: The equal frequencies model
Step 2: The saturated model
Step 3: Preparing to test for the main effects components of the model
Step 4: Degree choice main effect
Step 5: Sex main effect
Step 6: The main effects of degree choice plus sex
22.3 A three-variable example
Step 1: The equal frequencies model
Step 2: The saturated model
Step 3: Building up the main-effects model
Step 4: The two-variable interactions
Step 5: Which components account for the data?
Step 6: More on the interpretation of log-linear analysis
22.4 Reporting the results
22.5 When to use log-linear analysis
22.6 When not to use log-linear analysis
22.7 Data requirements for log-linear analysis
22.8 Problems in the use of log-linear analysis
22.9 SPSS analysis
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Tags: Gareth Norris, Faiza Qureshi, Dennis Howitt, Prof Duncan Cramer, Social Science