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# Statistical Methods for Communication Researchers and Professionals

**Author(s):** * Rene Weber, Ryan Fuller *

** Edition: ** 1

** Copyright: ** 2013

** Pages: ** 320

**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.

- Includes access to an actively-maintained website with additional information. Features include datasets, additional examples and problems, errata, links to other statistics sources, course materials, and more!

**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 **

Downloading and Installing R

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.

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