Book contents
- Research Methods and Applied Statistics
- Reviews
- Research Methods and Applied Statistics
- Copyright page
- Dedication
- Contents
- Guided Tour
- Preface
- Acknowledgments
- 1 Developing a Research Study Proposal
- 2 Developing a Research Question and Hypotheses, Choosing Statistics, and Estimating the Sample Size
- 3 Experimental vs. Correlational Studies, Measuring Constructs, and Internal Consistency Reliability
- 4 Demographics, Creating Surveys, and Data Management
- 5 Qualitative and Mixed-Methods Approaches
- 6 Beginning Your Analysis: Descriptive Statistics and Correlations
- 7 Relationships Between Nominal Data: Chi-Square and McNemar’s Tests
- 8 Comparing Means with Two Groups: The t and U Tests
- 9 Comparing Means with Multiple Groups: The Analysis of Variance (ANOVA)
- 10 Multiple Regression Analysis: Predicting a Continuous Variable
- 11 Binary Logistic Regression Analysis: Predicting a Dichotomous Variable
- 12 Data Visualization and Interpretation
- 13 Pulling it All Together: The Research Paper, the Research Poster, and Everything
- Appendix A Installing R and Required Libraries
- Further Reading
- Index
10 - Multiple Regression Analysis: Predicting a Continuous Variable
Published online by Cambridge University Press: 17 December 2025
Book contents
- Research Methods and Applied Statistics
- Reviews
- Research Methods and Applied Statistics
- Copyright page
- Dedication
- Contents
- Guided Tour
- Preface
- Acknowledgments
- 1 Developing a Research Study Proposal
- 2 Developing a Research Question and Hypotheses, Choosing Statistics, and Estimating the Sample Size
- 3 Experimental vs. Correlational Studies, Measuring Constructs, and Internal Consistency Reliability
- 4 Demographics, Creating Surveys, and Data Management
- 5 Qualitative and Mixed-Methods Approaches
- 6 Beginning Your Analysis: Descriptive Statistics and Correlations
- 7 Relationships Between Nominal Data: Chi-Square and McNemar’s Tests
- 8 Comparing Means with Two Groups: The t and U Tests
- 9 Comparing Means with Multiple Groups: The Analysis of Variance (ANOVA)
- 10 Multiple Regression Analysis: Predicting a Continuous Variable
- 11 Binary Logistic Regression Analysis: Predicting a Dichotomous Variable
- 12 Data Visualization and Interpretation
- 13 Pulling it All Together: The Research Paper, the Research Poster, and Everything
- Appendix A Installing R and Required Libraries
- Further Reading
- Index
Summary
Using a linear regression to make predictions from a data set can be found everywhere. From Amazon.com suggesting books for customers to businesses predicting revenue from advertising, regression analyses can be found in everyday life. A researcher can use a multiple regression analysis for predicting cause-and-effect relationships between the dependent and independent variables in a study. Unlike an ANOVA, regressions allow for categorical and scalar independent variables to be included together within the analysis. In this chapter, student researchers are taught what a regression analysis is, what it can be used for, and how to perform a multiple linear regression using SPSS and R. Students are also taught how to interpret regression results for testing hypotheses.
Information
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- Research Methods and Applied StatisticsA Practical Guide to Conducting Research, pp. 324 - 379Publisher: Cambridge University PressPrint publication year: 2025
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