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E-grāmata: Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences

  • Formāts: PDF+DRM
  • Izdošanas datums: 21-Feb-2017
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119290216
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  • Formāts: PDF+DRM
  • Izdošanas datums: 21-Feb-2017
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119290216
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Guides readers through the quantitative data analysis process including contextualizing data within a research situation, connecting data to the appropriate statistical tests, and drawing valid conclusions

Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences presents a clear and accessible introduction to the basics of quantitative data analysis and focuses on how to use statistical tests as a key tool for analyzing research data. The book presents the entire data analysis process as a cyclical, multiphase process and addresses the processes of exploratory analysis, decision-making for performing parametric or nonparametric analysis, and practical significance determination. In addition, the author details how data analysis is used to reveal the underlying patterns and relationships between the variables and connects those trends to the datas contextual situation.

Filling the gap in quantitative data analysis literature, this book teaches the methods and thought processes behind data analysis, rather than how to perform the study itself or how to perform individual statistical tests. With a clear and conversational style, readers are provided with a better understanding of the overall structure and methodology behind performing a data analysis as well as the needed techniques to make informed, meaningful decisions during data analysis. The book features numerous data analysis examples in order to emphasize the decision and thought processes that are best followed, and self-contained sections throughout separate the statistical data analysis from the detailed discussion of the concepts allowing readers to reference a specific section of the book for immediate solutions to problems and/or applications. Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences also features coverage of the following:

The overall methodology and research mind-set for how to approach quantitative data analysis and how to use statistics tests as part of research data analysis

A comprehensive understanding of the data, its connection to a research situation, and the most appropriate statistical tests for the data

Numerous data analysis problems and worked-out examples to illustrate the decision and thought processes that reveal underlying patterns and trends

Detailed examples of the main concepts to aid readers in gaining the needed skills to perform a full analysis of research problems

A conversational tone to effectively introduce readers to the basics of how to perform data analysis as well as make meaningful decisions during data analysis

Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences is an ideal textbook for upper-undergraduate and graduate-level research method courses in the behavioral and social sciences, statistics, and engineering. This book is also an appropriate reference for practitioners who require a review of quantitative research methods.

Michael J. Albers, Ph.D., is Professor in the Department of English at East Carolina University. His research interests include information design with a focus on answering real-world questions, the presentation of complex information, and humaninformation interaction. Dr. Albers received his Ph.D. in Technical Communication and Rhetoric from Texas Tech University.
Preface ix
About the Companion Website xiii
1 Introduction
1(12)
Basis of How All Quantitative Statistical Based Research
1(2)
Data Analysis, Not Statistical Analysis
3(5)
Quantitative Versus Qualitative Research
8(1)
What the Book Covers and What It Does Not Cover
9(1)
Book Structure
10(1)
References
11(2)
Part I Data Analysis Approaches
13(92)
2 Statistics Terminology
15(34)
Statistically Testing a Hypothesis
15(4)
Statistical Significance and p-Value
19(7)
Confidence Intervals
26(1)
Effect Size
27(4)
Statistical Power of a Test
31(3)
Practical Significance Versus Statistical Significance
34(1)
Statistical Independence
34(2)
Degrees of Freedom
36(1)
Measures of Central Tendency
37(4)
Percentile and Percentile Rank
41(1)
Central Limit Theorem
42(2)
Law of Large Numbers
44(4)
References
48(1)
3 Analysis Issues and Potential Pitfalls
49(14)
Effects of Variables
49(4)
Outliers in the Dataset
53(1)
Relationships Between Variables
53(7)
A Single Contradictory Example Does Not Invalidate a Statistical Relationship
60(2)
References
62(1)
4 Graphically Representing Data
63(24)
Data Distributions
63(1)
Bell Curves
64(4)
Skewed Curves
68(3)
Bimodal Distributions
71(4)
Poisson Distributions
75(2)
Binomial Distribution
77(2)
Histograms
79(1)
Scatter Plots
80(1)
Box Plots
81(1)
Ranges of Values and Error Bars
82(3)
References
85(2)
5 Statistical Tests
87(18)
Inter-Rater Reliability
87(5)
Regression Models
92(1)
Parametric Tests
93(2)
Nonparametric Tests
95(1)
One-Tailed or Two-Tailed Tests
96(3)
Tests Must Make Sense
99(4)
References
103(2)
Part II Data Analysis Examples
105(92)
6 Overview of Data Analysis Process
107(6)
Know How to Analyze It Before Starting the Study
107(1)
Perform an Exploratory Data Analysis
108(1)
Perform the Statistical Analysis
109(1)
Analyze the Results and Draw Conclusions
110(1)
Writing Up the Study
111(1)
References
112(1)
7 Analysis of a Study on Reading and Lighting Levels
113(22)
Lighting and Reading Comprehension
113(1)
Know How the Data Will Be Analyzed Before Starting the Study
113(2)
Perform an Exploratory Data Analysis
115(7)
Perform an Inferential Statistical Analysis
122(10)
Exercises
132(3)
8 Analysis of Usability of an E-Commerce Site
135(24)
Usability of an E-Commerce Site
135(1)
Study Overview
135(1)
Know How You Will Analyze the Data Before Starting the Study
136(2)
Perform an Exploratory Data Analysis
138(9)
Perform an Inferential Statistical Analysis
147(4)
Follow-Up Tests
151(2)
Performing Follow-Up Tests
153(4)
Exercises
157(1)
Reference
158(1)
9 Analysis of Essay Grading
159(18)
Analysis of Essay Grading
159(1)
Exploratory Data Analysis
160(5)
Inferential Statistical Data Analysis
165(8)
Exercises
173(2)
Reference
175(2)
10 Specific Analysis Examples
177(12)
Handling Outliers in the Data
177(5)
Floor/Ceiling Effects
182(1)
Order Effects
183(1)
Data from Stratified Sampling
184(1)
Missing Data
184(2)
Noisy Data
186(1)
Transform the Data
187(1)
References
188(1)
11 Other Types of Data Analysis
189(8)
Time-Series Experiment
189(3)
Analysis for Data Clusters
192(1)
Low-Probability Events
193(1)
Metadata Analysis
193(2)
Reference
195(2)
A Research Terminology
197(18)
Independent, Dependent, and Controlled Variables
197(2)
Between Subjects and Within Subjects
199(1)
Validity and Reliability
200(1)
Variable Types
201(1)
Type of Data
201(2)
Independent Measures and Repeated Measures
203(2)
Variation in Data Collection
205(7)
Probability---What 30% Chance Means
212(2)
References
214(1)
Index 215
Michael J. Albers, Ph.D., is Professor in the Department of English at East Carolina University. His research interests include information design with a focus on answering real-world questions, the presentation of complex information, and human???information interaction. Dr. Albers received his Ph.D. in Technical Communication and Rhetoric from Texas Tech University.