Quantitative Psychological Research The Complete Student’s Companion 5th Edition by David Clark Carter ISBN 1000965805 9781000965803

Part 1: Introduction Chapter 1: The methods used in psychological research

• Introduction

• What is the purpose of research?

• What is a method?

• Why have a method?

• Tensions between control and ecological validity

• Distinctions between quantitative and qualitative methods

  • Quantitative methods

  • Qualitative methods

• Is psychology a science?

• Ethical issues in psychological research

• Summary

Part 2: Choice of Topic, Measures and Research Design Chapter 2: The preliminary stages of research

• Introduction

• The choice of topic

• Reviewing the literature

• Focusing on a specific area of research

• The choice of method

• The choice of hypotheses

• The choice of research design

• Measurement in psychology

• The choice of measures

• The choice of analysis

• The choice of participants – the sample

• The procedure

• Pilot studies

• Summary

Chapter 3: Variables and the validity of research designs

• Introduction

• Variables

• The validity of research designs

• Efficacy and effectiveness

• The choice of hypotheses

• Summary

Chapter 4: Research designs and their internal validity

• Introduction

• Types of designs

• Terminology

• Specific examples of research designs

• Summary

Part 3: Methods Chapter 5: Asking questions I: Interviews and surveys

• Introduction

• Topics for questions

• The formats for asking questions

• Choosing between the formats

• The settings for asking questions

• The relative merits of the different settings

• The choice of setting

• The choice of participants

• What questions to include

• Types of questions

• The layout of the questionnaire

• The pilot study

• Summary

Chapter 6: Asking questions II: Measuring attitudes and meaning

• Introduction

• Reliability of measures

• Dimensions

• Attitude scales

• Techniques to measure meaning

• Summary

Chapter 7: Observation and content analysis

• Introduction

• Observation

• Issues shared between observation and content analysis

• Structured observation

• Content analysis

• Summary

Part 4: Data and Analysis Chapter 8: Scales of measurement

• Introduction

• Examples of measures

• Scales of measurement

• The relevance of the four scales

• Indicators

• Statisticians and scales

• Summary

Chapter 9: Summarising and describing data

• Introduction

• Numerical methods

• Graphical methods

• The distribution of data

• Summary

Chapter 10: Going beyond description

• Introduction

• Hypothesis testing

• Probability

• Statistical significance

• Error types

• Calculating the probability of the outcome of research

• One- and two-tailed tests

• Summary

Chapter 11: Samples and populations

• Introduction

• Statistics

• Parameters

• Choosing a sample

• Confidence intervals

• Summary

Chapter 12: Analysis of differences between a single sample and a population

• Introduction

• z-Tests

• One-group t-tests

• Confidence intervals for means

• z-Test comparing a sample proportion with a population proportion

• Further graphical displays

• Identifying outliers with standardised scores

• Summary

Chapter 13: Effect size and power

• Introduction

• Limitations of statistical significance testing

• Effect size

• Statistical power

• Finding a sample size via confidence intervals

• Summary

Chapter 14: Parametric and non-parametric tests

• Introduction

• Parametric tests

• The assumptions of parametric tests

• Non-parametric tests for one-group designs

• Summary

Chapter 15: Analysis of differences between two levels of an independent variable

• Introduction

• Parametric tests

• Non-parametric tests

• Summary

Chapter 16: Preliminary analysis of designs with one independent variable with more than two levels

• Introduction

• Parametric tests

• Non-parametric equivalents of ANOVA

• Summary

Chapter 17: Analysis of designs with more than one independent variable

• Introduction

• Interactions between IVs

• Parametric tests

• Non-parametric tests

• Summary

Chapter 18: Subsequent analysis after ANOVA or χ2

• Introduction

• Contrasts

• Trend tests

• Simple effects

• Interpreting main effects

• Beyond two-way ANOVA

• Summary

Chapter 19: Analysis of relationships I: Correlation

• Introduction

• Correlation

• Non-parametric correlation

• Other uses of correlation

• The use of correlation to evaluate reliability and validity of measures

• Summary

Chapter 20: Analysis of relationships II: Linear regression

• Introduction

• Simple regression

• Multiple regression

• Reporting a multiple regression

• Mediation analysis

• The similarity between ANOVA and multiple regression

• Summary

Chapter 21: Analysis of relationships III: Logistic regression

• Introduction

• The generalised linear model

• Maximum likelihood estimation

• Simple logistic regression

• Multiple logistic regression

• Assumptions

• Sample size

• Summary

Chapter 22: Analysis of covariance (ANCOVA)

• Introduction

• An IV with two levels

• Assumptions of ANCOVA

• Reporting an ANCOVA

• Statistical power and ANCOVA

• Pre-treatment values as covariates

• Alternatives to ANCOVA for pre–post designs

• ANCOVA with more than two levels in an IV

• Follow-up analysis

• Regression and ANCOVA

• Summary

Chapter 23: Screening data

• Introduction

• Checking for sensible values

• Missing data

• Intention to treat

• Assumptions of statistical tests

• Outliers and influential data

• Distribution of data

• Homogeneity/heterogeneity of variance

• Order of checks

• Bootstrap

• Analysis of unbalanced factorial designs

• ANOVA types

• Summary

**Chapter 24: Explor