SPSS Survival Manual
A Step by Step Guide to Data Analysis Using IBM SPSS
ByJulie Pallant
7th Edition
2020
9781003117452
VERY extensive summary in English of the latest edition of the SPSS Survival Manual.
ALL chapters are covered:
▪ Chapter 1 Designing study
▪ Chapter 2 Preparing a codebook
▪ Chapter 3 Getting to know IBM SPSS Statistics
▪ Chapter 4 Creating a data file and entering data
▪ Chapter 5 Screening and cleaning the data
▪ Chapter 6 Descriptive statistics
▪ Chapter 7 Using graphs to describe and explore the data
▪ Chapter 8 Manipulating the data
▪ Chapter 9 Checking the reliability of a scale
▪ Chapter 10 Choosing the right statistic
▪ Chapter 11 Correlation
▪ Chapter 12 Partial correlation
▪ Chapter 13 Multiple regression
▪ Chapter 14 Logistic regression
▪ Chapter 15 Factor analysis
▪ Chapter 16 Non-parametric statistic
▪ Chapter 17 T–tests
▪ Chapter 18 One-way analysis of variance
▪ Chapter 19 Two-way between-groups ANOVA
▪ Chapter 20 Mixed between-within subjects analysis of variance
▪ Chapter 21 Multivariate analysis of variance
▪ Chapter 22 Analysis of covariance
, Chapter 1 Designing a study
Setting up a study:
Proper research is highly dependent on detailed planning. The book provides the following tips
when setting up a study:
• Choose the design of your research, for example in the form of an experiment, a
questionnaire, or an observational study. Weigh all the pros and cons of each method.
• If you opt for an experiment: decide whether to opt for a between-groups design (different
subjects in each experimental condition) or a repeated measures design (all subjects in all
conditions).
• If you choose an experiment, make sure you have enough levels of your independent
variable.
• Always select more test subjects than you actually need, as there is a high chance that test
subjects will drop out.
• If possible, randomly assign subjects to each experimental condition. It is important that
these groups do not differ from each other on other matters. You can check this by
performing a covariance analysis.
• Choose reliable and valid dependent variables.
• Anticipate possible confounding variables. These are variables other than the independent
variable that can give a possible explanation for your result. Check for these confounding
variables if possible.
• If you opt for the method of a survey, check in advance whether the instructions, questions
and scales are formulated clearly. You do this by means of pilot testing.
How do you choose the right scales and methods?
When choosing the right scale and method, two concepts are important: reliability and validity.
Both concepts can influence the quality of your data.
What does reliability mean?
The reliability of a scale indicates to what extent the scale is free from random error. There are two
types of reliability:
1. Test-retest reliability: this is measured by presenting the relevant scale to two different
persons in two different situations and then calculating the correlation between these two
scores. The higher this correlation, the greater the test-retest reliability.
2. Internal consistency: the degree to which the items of a scale are interrelated. This can be
calculated, for example, with the Cronbach's coefficient alpha in SPSS. A Cronbach's alpha
of .7 or greater indicates a reliable scale.
What does validity mean?
The validity of a scale refers to the extent to which the methods measure what they intend to
measure. There are different types of validity:
1. Content validity: the degree of accuracy with which the method or scale tests the intended
domain or content.
2. Criterion validity: the relationship between different scale scores and a specified
A Step by Step Guide to Data Analysis Using IBM SPSS
ByJulie Pallant
7th Edition
2020
9781003117452
VERY extensive summary in English of the latest edition of the SPSS Survival Manual.
ALL chapters are covered:
▪ Chapter 1 Designing study
▪ Chapter 2 Preparing a codebook
▪ Chapter 3 Getting to know IBM SPSS Statistics
▪ Chapter 4 Creating a data file and entering data
▪ Chapter 5 Screening and cleaning the data
▪ Chapter 6 Descriptive statistics
▪ Chapter 7 Using graphs to describe and explore the data
▪ Chapter 8 Manipulating the data
▪ Chapter 9 Checking the reliability of a scale
▪ Chapter 10 Choosing the right statistic
▪ Chapter 11 Correlation
▪ Chapter 12 Partial correlation
▪ Chapter 13 Multiple regression
▪ Chapter 14 Logistic regression
▪ Chapter 15 Factor analysis
▪ Chapter 16 Non-parametric statistic
▪ Chapter 17 T–tests
▪ Chapter 18 One-way analysis of variance
▪ Chapter 19 Two-way between-groups ANOVA
▪ Chapter 20 Mixed between-within subjects analysis of variance
▪ Chapter 21 Multivariate analysis of variance
▪ Chapter 22 Analysis of covariance
, Chapter 1 Designing a study
Setting up a study:
Proper research is highly dependent on detailed planning. The book provides the following tips
when setting up a study:
• Choose the design of your research, for example in the form of an experiment, a
questionnaire, or an observational study. Weigh all the pros and cons of each method.
• If you opt for an experiment: decide whether to opt for a between-groups design (different
subjects in each experimental condition) or a repeated measures design (all subjects in all
conditions).
• If you choose an experiment, make sure you have enough levels of your independent
variable.
• Always select more test subjects than you actually need, as there is a high chance that test
subjects will drop out.
• If possible, randomly assign subjects to each experimental condition. It is important that
these groups do not differ from each other on other matters. You can check this by
performing a covariance analysis.
• Choose reliable and valid dependent variables.
• Anticipate possible confounding variables. These are variables other than the independent
variable that can give a possible explanation for your result. Check for these confounding
variables if possible.
• If you opt for the method of a survey, check in advance whether the instructions, questions
and scales are formulated clearly. You do this by means of pilot testing.
How do you choose the right scales and methods?
When choosing the right scale and method, two concepts are important: reliability and validity.
Both concepts can influence the quality of your data.
What does reliability mean?
The reliability of a scale indicates to what extent the scale is free from random error. There are two
types of reliability:
1. Test-retest reliability: this is measured by presenting the relevant scale to two different
persons in two different situations and then calculating the correlation between these two
scores. The higher this correlation, the greater the test-retest reliability.
2. Internal consistency: the degree to which the items of a scale are interrelated. This can be
calculated, for example, with the Cronbach's coefficient alpha in SPSS. A Cronbach's alpha
of .7 or greater indicates a reliable scale.
What does validity mean?
The validity of a scale refers to the extent to which the methods measure what they intend to
measure. There are different types of validity:
1. Content validity: the degree of accuracy with which the method or scale tests the intended
domain or content.
2. Criterion validity: the relationship between different scale scores and a specified
