1st Edition

# Understanding Statistics for the Social Sciences with IBM SPSS

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Modern statistical software provides the ability to compute statistics in a timely, orderly fashion. This introductory statistics textbook presents clear explanations of basic statistical concepts and introduces students to the IBM SPSS program to demonstrate how to conduct statistical analyses via the popular point-and-click and the "syntax file" methods. The focal point is to show students how easy it is to analyse data using SPSS once they have learned the basics.

- Provides clear explanation of basic statistical concepts that provides the foundation for the beginner students’ statistical journey.
- Introduces the SPSS software program.
- Gives clear explanation of the purpose of specific statistical procedures (e.g., frequency distributions, measures of central tendencies, measures of variability, etc.).
- Avoids the conventional cookbook approach that contributes very little to students’ understanding of the rationale of how the correct results were obtained.

The advantage of learning the IBM SPSS software package at the introductory class level is that most social sciences students will employ this program in their later years of study. This is because SPSS is one of the most popular of the many statistical packages currently available. Learning how to use this program at the very start not only familiarizes students with the utility of this program but also provides them with the experience to employ the program to conduct more complex analyses in their later years.

Preface

About the author

Introduction to the Scientific Methodology of Research

Introduction

The scientific approach versus the layperson’s approach

to knowledge

Sampling

Research designs

Between-groups design

The univariate approach

The multivariate approach

Correlational design

Hypothesis testing and probability theory

Probability

Statistics and scientific research

Definition of statistics

Descriptive statistics

Inferential statistics

Introduction to SPSS

Learning how to use the SPSS software program

.Introduction to SPSS

Setting up a data file

Preparing a code-book

Data set

Creating SPSS data file

Data entry

Saving and editing data file

SPSS analysis: Windows method versus syntax method

SPSS analysis: Windows method

SPSS analysis: Syntax method

SPSS output

Results and interpretation

DESCRIPTIVE STATISTICS

Basic mathematical concepts and measurement

Basic mathematical concepts

Mathematical notations

Measurement scales (levels of measurement)

Nominal scales

Ordinal scales

Interval scales

Ratio scales

Types of variables

Independent and dependent variables

Continuous and discrete variables

Real limits of continuous variables

Rounding

Frequency distributions

Ungrouped frequency distributions

SPSS: Data entry format

SPSS Windows method

SPSS syntax method

SPSS output

Results and interpretation

Grouped frequency distributions

Grouping scores into class intervals

Computing a frequency distribution of grouped scores

SPSS method

SPSS Windows method

SPSS syntax method

SPSS output

Percentiles and percentile ranks

Percentiles

Computation of percentiles (finding the score below which a specified percentage of scores will fall)

SPSS syntax method

Data entry format

SPSS syntax method

SPSS output

Another example

Data entry format

SPSS syntax method

SPSS output

Percentile rank

Computation of percentile ranks (finding the percentage of scores that fall below a given score)

Data entry format

SPSS syntax method

SPSS output

Another example

Data Entry Format

SPSS Syntax Method

SPSS Output

Graphing

Graphing frequency distributions

Bar graph

An example

Data entry format

SPSS Windows method

SPSS syntax method

SPSS bar graph output

Histogram

An example

SPSS Windows method

SPSS syntax method

SPSS histogram output

Frequency polygon

An example

SPSS Windows method

SPSS syntax method

SPSS frequency polygon output

Cumulative percentage curve

An example

SPSS Windows method

SPSS syntax method

SPSS Cumulative percentage Output

Measures of central tendency

Why is central tendency important?

Measures of central tendency

The arithmetic mean

How to calculate the arithmetic mean

SPSS Window method

SPSS syntax method

SPSS output

How to calculate the mean from a grouped frequency distribution

An example

Calculating the mean from grouped frequency distribution using SPSS

Data entry format

SPSS syntax method

SPSS output

The overall mean

An example

How to calculate the overall mean

using SPSS

Data entry format

SPSS syntax method

SPSS output

Properties of the mean

The median

Calculating the median for ungrouped scores

Calculating the median for grouped scores

Properties of the median

The mode

SPSS windows method

SPSS syntax method

SPSS histogram output

The mode for grouped scores

Comparison of the mean, median and mode

Measures of central tendency: symmetry and skewness

Measures of variability/dispersion

What is variability?

Range

Standard deviation

Calculating the standard deviation using the deviation scores method

Calculating the standard deviation using the raw scores method

Variance

Using SPSS to calculate the range, the standard deviation, and the variance

SPSS Windows method

SPSS syntax method

SPSS output

The normal distribution and standard scores

The normal distribution

Areas contained under the standard normal distribution

Standard scores (*z* scores) and the normal curve

Calculating the percentile rank with *z* scores

SPSS Windows method

SPSS syntax method

SPSS data file containing the first 0

computed *z* scores

Calculating the percentage of scores that fall between

two known scores

Calculating the percentile point with *z* scores

SPSS Windows method

SPSS syntax method

Table showing the 90th percentile for the set

of 0 exam scores

Calculating the scores that bound a specified area of the

distribution

SPSS Windows method

SPSS syntax method

Table showing the approximate lower and upper bound scores that bound the middle 70% of the statistics exam’s distribution

Using *z* scores to compare performance between different distributions

Correlation

The concept of correlation

Linear and non-linear relationships

Characteristics of correlation

Magnitude (strength) of relationships

Direction of relationships

Correlation coefficient and *z* scores

Scatter plot (SPSS Windows method)

Scatter plot (SPSS syntax method)

Scatter plot

Converting raw scores into *z *scores (SPSS Windows method)

Converting raw scores into *z* scores (SPSS syntax method)

SPSS data file containing the pairs of computed *z* scores

Pearson *r* and the linear correlation coefficient

Example of the Pearson *r* calculation

SPSS Windows method

SPSS syntax method

The calculated Pearson *r*

Some issues with correlation

Can correlation show causality?

Spurious correlation

Linear Regression

What is linear regression?

Linear regression and imperfect relationships

Scatter plot and the line of best fit

SPSS Windows method (scatter plot and line of best fit)

SPSS syntax method (scatter plot)

Scatter plot with line of best fit

Least-squares regression (line of best fit): Predicting *Y* from *X*

How to construct the least-squares regression line: Predicting *Y* from *X*

SPSS Windows method (constructing the least-squares regression line equation)

SPSS syntax method (constructing the least-squares regression line equation)

SPSS output

Results and interpretation

INFERENTIAL STATISTICS

Statistical inference and probability

Introduction to inferential statistics

Probability

The classical approach to probability

The empirical approach to probability

Expressing probability values

Computing probability: The addition rule and the multiplication rule

The addition rule

The multiplication rule

Using the multiplication and addition rules together

Computing probability for continuous variables

Sampling

Simple random sampling

Stratified proportionate random sampling

Cluster sampling

Non-random sampling techniques: Systematic sampling; quota sampling

Sampling with or without replacement

Confidence interval and confidence level

How to calculate the confidence interval

SPSS Windows method

SPSS syntax method

Introduction to hypothesis testing

Introduction to hypothesis testing .

Types of hypotheses

Research/alternative hypothesis

Null hypothesis

Hypotheses: Non-directional or directional

Testing hypotheses

Level of significance

Two-tailed and one-tailed test of significance

Type I and Type II errors

Hypothesis testing: *t* test for independent and correlated groups

Introduction to the *t* test

Independent *t* test

SPSS Windows method: Independent *t *test

SPSS syntax method

SPSS output

Results and interpretation

Dependent/correlated *t* test

SPSS Windows Method: Dependent *t* test

SPSS syntax method

SPSS output

Results and interpretation

Hypothesis testing: One-way analysis of variance

One-way analysis of variance (ANOVA)

An example

Scheffé post hoc test

SPSS Windows method: One-way ANOVA

SPSS syntax method

SPSS output

Results and interpretation

Post hoc comparisons

Hypothesis testing: Chi-square test

Non-parametric tests

Chi-square (²) test

Chi-square goodness-of-fit test

SPSS Windows method

SPSS syntax method

SPSS output

Results and interpretation

Chi-square (²) test of independence between two variables

SPSS Windows method

SPSS syntax method

SPSS output

Results and interpretation

Appendices

Bibliography

Index

### Biography

Doctor Robert Ho, now deceased, received his DPhil from Waikato University, New Zealand.. He retired from Central Queensland University in Australia in 2007 and was an affiliate of the Graduate School of Psychology at the Assumption University of Thailand.