# Statistical Methods for Communication Researchers and Professionals

Edition: 1

Pages: 320

## \$95.52

ISBN 9781465216557

Details Electronic Delivery EBOOK 180 days

Every day we are inundated with information in our professional as well as personal lives…

We simply cannot attend to everything, and need help in separating out and evaluating claims. Understanding statistical methods provides us with an efficient way to explore, analyze and interpret data, as well as evaluate the strength of evidence presented in support of or opposition to claims.

Statistical Methods for Communication Researchers and Professionals helps the reader develop the statistical competency necessary to become good researchers and good “statistical citizens” in the information age.

Statistical Methods for Communication Researchers and Professionals:

• Provides an overview of statistical methods in the context of communication research and practice.
• Prepares the reader for careers such as research scientists, professional communicators, or anyone that is responsible for describing, evaluating, and interpreting data they confront daily in their professions.
• Draws on a range of recently published examples in different arenas in communication.
• Addresses three primary goals of statistical methods:
• The design of samples, including those based on probability and non- probability procedures.
• Describing, exploring, and summarizing data in samples or non- samples.
• Making predictions and generalizations from a sample to the population we are interested in, or testing assumptions in populations with data in samples.

CHAPTER 1 - Introduction to Statistical Methods and Measurement
Levels of Measurement
Nominal
Ordinal
Interval
Ratio
Recommendations
Summary
Terms
Chapter 1 Problem Set
References

CHAPTER 2 - Descriptive Statistics
Tabular and Graphical Descriptive Methods
Frequencies: Nominal- Level Measurement
Frequencies: Interval/Ratio- Level Measurement
Numerical Descriptive Methods
Measures of Central Tendency
Recommendations
Measures of Variation
Measures of Position
Summary
Terms
Chapter 2 Problem Set
References

CHAPTER 3 - Probability and Probability Distributions
Probability
Calculation Rules for Probabilities
Probability Distributions
Discrete Variables
Continuous Variables
Finding Probabilities in a Normal Distribution
Summary
Terms
Chapter 3 Problem Set
References

CHAPTER 4 - Sampling Methods
Nonprobability Sampling
Convenience Sampling
Purposive Sampling
Snowball Sampling
Quota Sampling
Probability Sampling
Simple Random Sampling
Systematic Random Sampling
Stratified Sampling
Cluster Sampling
Response Rates
Summary
Terms
Chapter 4 Problem Set
References

CHAPTER 5 - Sampling Distributions and Central Limit Theorem
Sampling Distributions
Sample Means
Central Limit Theorem
Other Statistics
Summary
Terms
Chapter 5 Problem Set
References

CHAPTER 6 - Estimating and Testing
Estimation of Parameters
Point Estimation
Interval Estimation
Confidence Intervals for Means
Testing of Hypotheses
Research Hypotheses
Null Hypotheses
Statistical Null and Research Hypotheses
Decisions and Types of Errors in Testing Hypotheses
Significance Statements
Calculation of p- Values (z- Test)
Alpha Levels
Evaluation of the Hypothesis Test
Regions of Rejection and Non- Rejection—One- sided v. Two- sided Tests
Non- significant Results
Sample Size and Practical Significance of Test Results
Effect Sizes
Calculating Type- II Errors
Selection of b- levels
Relationship between Type- I and Type- II Errors
Power of a Statistical Test
Calculation of Optimal Sample Sizes
Summary
Terms
Chapter 6 Problem Set
References

CHAPTER 7 - Specific Significance Tests for Differences
Tests for Mean Differences
Inde pen dent Samples t- Test
Dependent (Paired) Samples t- Test
Analysis of Variance (ANOVA)
Example: Teacher Confirmation and Student Satisfaction
Tests for Frequency Differences (Chi- Square Tests)
One Measurement, One- Dimensional Chi- Square Test
Two Measurements, One Dimensional Chi- Square Test
One Measurement, Two- Dimensional Chi- Square Test
Summary
Terms
Chapter 7 Problem Set
References

CHAPTER 8 - Specific Significance Tests for Dependencies
Regression Analysis
Regression Coefficients and the Least- Squares Criterion
Significance Test for Regression Coefficients
Confidence Interval for Regression Coefficients
Assumptions and Issues
Perceived Reader Support and Blogger Personal Growth
Covariance
Covariance and Regression
Correlation Coefficient r (Pearson Correlation Coefficient)
Perceived Reader Support and Blogger Personal Growth
Significance Test and Confidence Interval for Correlation Coefficients
Correlation and Regression
Coefficient of Determination
Assumptions and Issues
Fisher’s Z- Transformation: Tests for Comparing Correlations
Sampling Distributions for Correlations Unequal Zero
Averaging and Comparing Correlations Using Fisher’s Z- Scores
Testing the Null Hypothesis 0 ( 0 0) Using Fisher’s Z- Scores
Testing the Null Hypothesis 1 2 Using Fisher’s Z- Scores
Correlation and Causality
Definition of Causality
Summary
Terms
Chapter 8 Problem Set
References

CHAPTER 9 - Epilogue
A Critical Perspective on Statistics by Timothy R. Levine, Michigan State University
On Critics and Criticism
Four Statistical Rants
Rant Number One—Statistics, Substantive Focus, and Theory
Rant Number Two—Focus on Data Analysis over Data Creation
Rant Number Three—Over- Reliance on p .05
Rant Number Four—When Complexity is Not a Virtue
Summary
Terms
References

APPENDIX A - Tables
Normal Distribution
t-Distribution
F-Distribution
Chi- square Distribution
Fisher’s Z-Values

APPENDIX B - Problem Set Answers
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8

APPENDIX C - Course Schedules—Recommendations
Quarter- System (10 weeks schedule)
Semester- System (14 weeks schedule)

APPENDIX D - Using R and R- Commander for Statistical Analyses
Running R
Introduction: Program Instructions for R- Commander (Rcmdr)
Problem 1: Entering and Retrieving Data
Problem 2: Descriptive Statistics
Problem 3: Finding Standard Scores
Problem 4: One Sample t- Test
Problem 5: Two Independent Samples t- Test
Problem 6: Two Dependent Samples t- Test
Problem 7: K L Chi- Square Test
Problem 8: Pearson Correlation R
Problem 9: Partial Correlation
Problem 10: One- Way Analysis of Variance
Problem 11: Factorial ANOVA
Problem 12: Multiple Regression
Final note
References

Rene Weber
René Weber is an Associate Professor in the Department of Communication at the University of California, Santa Barbara. He holds a Ph.D. (Dr.rer.nat.) in Psychology (Berlin University of Technology, Germany) and an M.D. (Dr.rer.medic.) in Cognitive Neuroscience (RWTH University Aachen, Germany). He also holds Bachelor and Master’s Degrees in Communication and Business Administration with an emphasis on econometrics and statistics. In his academic research he focuses on mass communication/audience behaviors and effects as a product and determinant of complex cognitions. He develops and applies both traditional social scientific and neuroscientific methodology (e.g., fMRI) to test media related theories. His research has been published in major communication and neuroscience journals, in numerous book chapters, and in three books.
Ryan Fuller
Ryan Fuller is a Doctoral Candidate in the Department of Communication at the University of California, Santa Barbara, and Research Associate at the Carsey- Wolf Center. He holds a Bachelor Degree in Communication (UC Davis) and a Master Degree in Business Administration (San Francisco State University). His research interests include organizational communication, conflict and negotiation, labor unions, media industries, and communication research methods.

Every day we are inundated with information in our professional as well as personal lives…

We simply cannot attend to everything, and need help in separating out and evaluating claims. Understanding statistical methods provides us with an efficient way to explore, analyze and interpret data, as well as evaluate the strength of evidence presented in support of or opposition to claims.

Statistical Methods for Communication Researchers and Professionals helps the reader develop the statistical competency necessary to become good researchers and good “statistical citizens” in the information age.

Statistical Methods for Communication Researchers and Professionals:

• Provides an overview of statistical methods in the context of communication research and practice.
• Prepares the reader for careers such as research scientists, professional communicators, or anyone that is responsible for describing, evaluating, and interpreting data they confront daily in their professions.
• Draws on a range of recently published examples in different arenas in communication.
• Addresses three primary goals of statistical methods:
• The design of samples, including those based on probability and non- probability procedures.
• Describing, exploring, and summarizing data in samples or non- samples.
• Making predictions and generalizations from a sample to the population we are interested in, or testing assumptions in populations with data in samples.

CHAPTER 1 - Introduction to Statistical Methods and Measurement
Levels of Measurement
Nominal
Ordinal
Interval
Ratio
Recommendations
Summary
Terms
Chapter 1 Problem Set
References

CHAPTER 2 - Descriptive Statistics
Tabular and Graphical Descriptive Methods
Frequencies: Nominal- Level Measurement
Frequencies: Interval/Ratio- Level Measurement
Numerical Descriptive Methods
Measures of Central Tendency
Recommendations
Measures of Variation
Measures of Position
Summary
Terms
Chapter 2 Problem Set
References

CHAPTER 3 - Probability and Probability Distributions
Probability
Calculation Rules for Probabilities
Probability Distributions
Discrete Variables
Continuous Variables
Finding Probabilities in a Normal Distribution
Summary
Terms
Chapter 3 Problem Set
References

CHAPTER 4 - Sampling Methods
Nonprobability Sampling
Convenience Sampling
Purposive Sampling
Snowball Sampling
Quota Sampling
Probability Sampling
Simple Random Sampling
Systematic Random Sampling
Stratified Sampling
Cluster Sampling
Response Rates
Summary
Terms
Chapter 4 Problem Set
References

CHAPTER 5 - Sampling Distributions and Central Limit Theorem
Sampling Distributions
Sample Means
Central Limit Theorem
Other Statistics
Summary
Terms
Chapter 5 Problem Set
References

CHAPTER 6 - Estimating and Testing
Estimation of Parameters
Point Estimation
Interval Estimation
Confidence Intervals for Means
Testing of Hypotheses
Research Hypotheses
Null Hypotheses
Statistical Null and Research Hypotheses
Decisions and Types of Errors in Testing Hypotheses
Significance Statements
Calculation of p- Values (z- Test)
Alpha Levels
Evaluation of the Hypothesis Test
Regions of Rejection and Non- Rejection—One- sided v. Two- sided Tests
Non- significant Results
Sample Size and Practical Significance of Test Results
Effect Sizes
Calculating Type- II Errors
Selection of b- levels
Relationship between Type- I and Type- II Errors
Power of a Statistical Test
Calculation of Optimal Sample Sizes
Summary
Terms
Chapter 6 Problem Set
References

CHAPTER 7 - Specific Significance Tests for Differences
Tests for Mean Differences
Inde pen dent Samples t- Test
Dependent (Paired) Samples t- Test
Analysis of Variance (ANOVA)
Example: Teacher Confirmation and Student Satisfaction
Tests for Frequency Differences (Chi- Square Tests)
One Measurement, One- Dimensional Chi- Square Test
Two Measurements, One Dimensional Chi- Square Test
One Measurement, Two- Dimensional Chi- Square Test
Summary
Terms
Chapter 7 Problem Set
References

CHAPTER 8 - Specific Significance Tests for Dependencies
Regression Analysis
Regression Coefficients and the Least- Squares Criterion
Significance Test for Regression Coefficients
Confidence Interval for Regression Coefficients
Assumptions and Issues
Perceived Reader Support and Blogger Personal Growth
Covariance
Covariance and Regression
Correlation Coefficient r (Pearson Correlation Coefficient)
Perceived Reader Support and Blogger Personal Growth
Significance Test and Confidence Interval for Correlation Coefficients
Correlation and Regression
Coefficient of Determination
Assumptions and Issues
Fisher’s Z- Transformation: Tests for Comparing Correlations
Sampling Distributions for Correlations Unequal Zero
Averaging and Comparing Correlations Using Fisher’s Z- Scores
Testing the Null Hypothesis 0 ( 0 0) Using Fisher’s Z- Scores
Testing the Null Hypothesis 1 2 Using Fisher’s Z- Scores
Correlation and Causality
Definition of Causality
Summary
Terms
Chapter 8 Problem Set
References

CHAPTER 9 - Epilogue
A Critical Perspective on Statistics by Timothy R. Levine, Michigan State University
On Critics and Criticism
Four Statistical Rants
Rant Number One—Statistics, Substantive Focus, and Theory
Rant Number Two—Focus on Data Analysis over Data Creation
Rant Number Three—Over- Reliance on p .05
Rant Number Four—When Complexity is Not a Virtue
Summary
Terms
References

APPENDIX A - Tables
Normal Distribution
t-Distribution
F-Distribution
Chi- square Distribution
Fisher’s Z-Values

APPENDIX B - Problem Set Answers
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8

APPENDIX C - Course Schedules—Recommendations
Quarter- System (10 weeks schedule)
Semester- System (14 weeks schedule)

APPENDIX D - Using R and R- Commander for Statistical Analyses
Running R
Introduction: Program Instructions for R- Commander (Rcmdr)
Problem 1: Entering and Retrieving Data
Problem 2: Descriptive Statistics
Problem 3: Finding Standard Scores
Problem 4: One Sample t- Test
Problem 5: Two Independent Samples t- Test
Problem 6: Two Dependent Samples t- Test
Problem 7: K L Chi- Square Test
Problem 8: Pearson Correlation R
Problem 9: Partial Correlation
Problem 10: One- Way Analysis of Variance
Problem 11: Factorial ANOVA
Problem 12: Multiple Regression
Final note
References

Rene Weber
René Weber is an Associate Professor in the Department of Communication at the University of California, Santa Barbara. He holds a Ph.D. (Dr.rer.nat.) in Psychology (Berlin University of Technology, Germany) and an M.D. (Dr.rer.medic.) in Cognitive Neuroscience (RWTH University Aachen, Germany). He also holds Bachelor and Master’s Degrees in Communication and Business Administration with an emphasis on econometrics and statistics. In his academic research he focuses on mass communication/audience behaviors and effects as a product and determinant of complex cognitions. He develops and applies both traditional social scientific and neuroscientific methodology (e.g., fMRI) to test media related theories. His research has been published in major communication and neuroscience journals, in numerous book chapters, and in three books.
Ryan Fuller
Ryan Fuller is a Doctoral Candidate in the Department of Communication at the University of California, Santa Barbara, and Research Associate at the Carsey- Wolf Center. He holds a Bachelor Degree in Communication (UC Davis) and a Master Degree in Business Administration (San Francisco State University). His research interests include organizational communication, conflict and negotiation, labor unions, media industries, and communication research methods.