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E-grāmata: Lab Manual for Psychological Research and Statistical Analysis

  • Formāts: PDF+DRM
  • Izdošanas datums: 17-Jul-2019
  • Izdevniecība: SAGE Publications Inc
  • Valoda: eng
  • ISBN-13: 9781544363509
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  • Formāts: PDF+DRM
  • Izdošanas datums: 17-Jul-2019
  • Izdevniecība: SAGE Publications Inc
  • Valoda: eng
  • ISBN-13: 9781544363509

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Lab Manual for Psychological Research and Statistical Analysis serves as an additional resource for students and instructors in a research methods, statistics, or combined course where classroom and/or laboratory exercises are conducted. Packed with exercises, checklists, and how-to sections, this robust lab manual gives students hands-on guidance and practice for conducting and analyzing their own psychological research. Dawn M. McBride and J. Cooper Cutting provide students with additional opportunities for practice in a course with challenging material that requires practice and repetition for deeper understanding.
Introduction for Instructors ix
Chapter 1 Psychological Research: The Whys and Hows of the Scientific Method and Statistics
1(8)
1a The Purpose of Statistics
1(1)
1b Science in the Media
2(1)
1c Understanding Your Data
3(2)
1d Displaying Distributions
5(1)
1e Making and Interpreting Graphs
6(1)
1f Setting up Your Data in SPSS: Creating a Data File
7(1)
1g Displaying Distributions in SPSS
8(1)
Chapter 2 Developing a Research Question and Understanding Research Reports
9(16)
2a How to Read Empirical Journal Articles
9(3)
2b Reading Journal Articles---Mueller and Oppenheimer (2014)
12(1)
2c Reading Journal Articles---Roediger and Karpicke (2006)
13(2)
2d Reviewing the Literature
15(1)
2e Creating References
16(1)
2f APA Style
17(6)
2g APA-Style Manuscript Checklist
23(2)
Chapter 3 Ethical Guidelines for Psychological Research
25(7)
3a Ethics
25(2)
3b Ethics in a Published Study
27(1)
3c Academic Honesty Guidelines---What Is (and Isn't) Plagiarism
28(1)
3d Examples of Plagiarism
29(2)
3e Identifying and Avoiding Plagiarism
31(1)
Chapter 4 Probability and Sampling
32(5)
4a Distributions and Probability
32(2)
4b Basic Probability
34(1)
4c Subject Sampling
35(1)
4d Sampling
36(1)
Chapter 5 How Psychologists Use the Scientific Method: Data Collection Techniques and Research Designs
37(6)
5a Naturalistic Observation Group Activity
37(1)
5b Basics of Psychological Research
38(2)
5c Designing an Experiment Activity
40(1)
5d Research Design Exercise
41(1)
5e Design and Data Collection Exercise
42(1)
Chapter 6 Descriptive Statistics
43(7)
6a Central Tendency: Comparing Data Sets
43(1)
6b Understanding Central Tendency
44(1)
6c Central Tendency in SPSS
45(1)
6d Describing a Distribution (Calculations by Hand)
46(1)
6e More Describing Distributions
47(1)
6f Descriptive Statistics With Excel
48(1)
6g Measures of Variability in SPSS
49(1)
Chapter 7 Independent Variables and Validity in Research
50(8)
7a Identifying and Developing Hypotheses About Variables
50(2)
7b Independent and Dependent Variables
52(2)
7c Identifying Variables From Abstracts
54(1)
7d Identifying Variables From Empirical Articles
55(1)
7e Research Concepts: Designs, Validity, and Scales of Measurement
56(1)
7f Internal and External Validity
57(1)
Chapter 8 One-Factor Experiments
58(6)
8a Bias and Control Exercise
58(2)
8b Experimental Variables
60(1)
8c Experiments Exercise
61(2)
8d Experimental Designs
63(1)
Chapter 9 Hypothesis-Testing Logic
64(7)
9a Inferential Statistics Exercise
64(2)
9b Calculating z Scores Using SPSS
66(1)
9c The Normal Distribution
67(1)
9d Z Scores and the Normal Distribution
68(1)
9e Hypothesis Testing With Normal Populations
69(1)
9f Hypothesis Testing With z Tests
70(1)
Chapter 10 T Tests
71(14)
10a Hypothesis Testing With a Single Sample
71(1)
10b One-Sample t Test in SPSS
72(1)
10c One-Sample t Tests by Hand
73(1)
10d Related-Samples t Tests
74(2)
10e Related-Samples t Test in SPSS
76(1)
10f Independent Samples t Tests
77(1)
10g Hypothesis Testing---Multiple Tests
78(1)
10h More Hypothesis Tests With Multiple Tests
79(3)
10i T Tests Summary Worksheet
82(1)
10j Choose the Correct t Test
83(1)
10k Writing a Results Section From SPSS Output---t Tests
84(1)
Chapter 11 One-Way Analysis of Variance
85(6)
11a One-Way Between-Subjects Analysis of Variance (Hand Calculations)
85(1)
11b One-Way Between-Subjects Analysis of Variance in SPSS
86(1)
11c Writing a Results Section From SPSS Output---Analysis of Variance
87(2)
11d Inferential Statistics and Analyses
89(2)
Chapter 12 Correlation Tests and Simple Linear Regression
91(7)
12a Creating and Interpreting Scatterplots
91(2)
12b Understanding Correlations
93(1)
12c Correlations and Scatterplots in SPSS
94(1)
12d Computing Correlations by Hand
95(1)
12e Hypothesis Testing With Correlation Using SPSS
96(1)
12f Regression
97(1)
Chapter 13 Chi-Square Tests
98(5)
13a Chi-Square Crosstabs Tables
98(2)
13b Chi-Square Hand Calculations From Crosstabs Tables
100(1)
13c Chi-Square in SPSS---Type in the Data
101(1)
13d Chi-Square in SPSS From a Data File
102(1)
Chapter 14 Multifactor Experiments and Two-Way Analysis of Variance (Chapters 14 and 15)
103(8)
14a Factorial Designs
103(2)
14b Factorial Designs Article---Sproesser, Schupp, and Renner (2014)
105(1)
14c Factorial Designs Article---Farmer, McKay, and Tsakiris (2014)
106(1)
14d Describing Main Effects and Interactions
107(1)
14e Factorial Analysis of Variance
108(1)
14f Analysis of Variance Review
109(1)
14g Main Effects and Interactions in Factorial Analysis of Variance
110(1)
Chapter 15 One-Way Within-Subjects Analysis of Variance
111(3)
15a One-Way Within-Subjects Analysis of Variance
111(1)
15b One-Way Within-Subjects Analysis of Variance in SPSS
112(1)
15c One-Way Within-Subjects Analysis of Variance Review
113(1)
Chapter 16 Meet the Formulae and Practice Computation Problems
114(14)
16a Meet the Formula and Practice Problems: z Score Transformation
114(1)
16b Meet the Formula and Practice Problems: Single-Sample z Tests and t Tests
115(2)
16c Meet the Formula and Practice Problems: Comparing Independent Samples and Related Samples t Tests
117(2)
16d Meet the Formula and Practice Problems: One-Factor Between-Subjects Analysis of Variance
119(2)
16e Meet the Formula and Practice Problems: Two-Factor Analysis of Variance
121(3)
16f Meet the Formula and Practice Problems: One-Factor Within-Subjects Analysis of Variance
124(1)
16g Meet the Formula and Practice Problems: Correlation
125(2)
16h Meet the Formula and Practice Problems: Bivariate Regression
127(1)
Appendix A Data Sets and Activities
128(9)
A1 Data Analysis Exercise---von Hippel, Ronay, Baker, Kjelsaas, and Murphy (2016)
128(1)
A2 Data Analysis Exercise---Naime, Pandeirada, and Thompson (2008)
129(1)
A3 Data Analysis Project---Crammed vs. Distributed Study
130(1)
A4 Data Analysis Project---Teaching Techniques Study
131(1)
A5 Data Analysis Project---Distracted Driving Study
132(1)
A6 Data Analysis Project---Temperature and Air Quality Study
133(1)
A7 Data Analysis Project---Job Type and Satisfaction Study
134(1)
A8 Data Analysis Project---Attractive Face Recognition Study
135(1)
A9 Data Analysis Project---Discrimination in the Workplace Study
136(1)
Appendix B Overview and Selection of Statistical Tests
137(7)
B1 Finding the Appropriate Inferential Test
137(1)
B2 Finding the Appropriate Inferential Test From Research Designs
138(1)
B3 Finding the Appropriate Inferential Test From Research Questions
139(1)
B4 Identifying the Design and Finding the Appropriate Inferential Test From Abstracts
140(2)
B5 Identifying Variables and Determining the Inferential Test From Abstracts
142(2)
Appendix C Summary of Formulae
144(3)
References 147
Dawn M. McBride is professor of psychology at Illinois State University, where she has taught research methods since 1998. Her research interests include automatic forms of memory, false memory, prospective memory, task order choices, and forgetting. In addition to research methods, she teaches courses in introductory psychology, cognition and learning, and human memory; she also teaches a graduate course in experimental design. She is a recipient of the Illinois State University Teaching Initiative Award and the Illinois State University SPA/Psi Chi Jim Johnson Award for commitment to undergraduate mentorship, involvement, and achievement. Her nonacademic interests include spending time with her family, traveling, watching Philadelphia sports teams (it was a good year for Philly sports this year!), and reading British murder mysteries. She earned her PhD in cognitive psychology from the University of California, Irvine, and her BA from the University of California, Los Angeles.

J. Cooper Cutting (PhD, cognitive psychology, University of Illinois at Urbana-Champaign) is associate professor of psychology at Illinois State University. Dr. Cuttings research interests are in psycholinguistics, primarily, with a focus on the production of language. A central theme of his research is how different types of information interact during language use. He has examined this issue in the context of lexical access, within-sentence agreement processes, figurative language production, and pragmatics. He has taught courses in research methods, statistics, cognitive psychology, computer applications in psychology, human memory, psycholinguistics, and sensation and perception. He is also a recipient of the Illinois State University SPA/Psi Chi Jim Johnson Award for commitment to undergraduate mentorship, involvement, and achievement. His non-academic interests include gardening and reading science fiction and fantasy novels.