Understanding political science statistics: observations and expectations in political analysis
"Any student can learn the basic concepts of statistics if they are introduced as solutions to particular problems, and not formulas with a life of their own. In this text, students are introduced to a problem, asked to consider, conceptually, how one would address that problem, and then led th...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York and London
Routledge
2015
|
| Ausgabe: | First published |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Any student can learn the basic concepts of statistics if they are introduced as solutions to particular problems, and not formulas with a life of their own. In this text, students are introduced to a problem, asked to consider, conceptually, how one would address that problem, and then led through the derivation of the appropriate statistical formula. This applied method of teaching statistics through political science examples allows students to see the research method as problem solving. They learn the math, but only after they learn the concepts and methodological considerations that give the math context. The concepts throughout are presented through the lens of "observations and expectations," applied to myriad statistical techniques, both descriptive and inferential, as well as more generalized concepts of research methodology itself, such as hypothesis testing. Galderisi highlights that with each advance in technical sophistication, each statistical procedure is built on a small set of basic concepts, such as the reasons for standardization, the effects of outliers, or the concept of proportional reduction of error, to show that they are cumulative. More important than just memorizing a series of formula, this text emphasizes the underlying logic of statistical analysis for greater understanding. Further, the applications and examples drawn from political science (including law) allow students to better see how they can apply these concepts and techniques in their own research and in future coursework"-- |
| Beschreibung: | xxiii, 339 Seiten Diagramme |
| ISBN: | 9780415890052 |
Internformat
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| 520 | |a "Any student can learn the basic concepts of statistics if they are introduced as solutions to particular problems, and not formulas with a life of their own. In this text, students are introduced to a problem, asked to consider, conceptually, how one would address that problem, and then led through the derivation of the appropriate statistical formula. This applied method of teaching statistics through political science examples allows students to see the research method as problem solving. They learn the math, but only after they learn the concepts and methodological considerations that give the math context. The concepts throughout are presented through the lens of "observations and expectations," applied to myriad statistical techniques, both descriptive and inferential, as well as more generalized concepts of research methodology itself, such as hypothesis testing. Galderisi highlights that with each advance in technical sophistication, each statistical procedure is built on a small set of basic concepts, such as the reasons for standardization, the effects of outliers, or the concept of proportional reduction of error, to show that they are cumulative. More important than just memorizing a series of formula, this text emphasizes the underlying logic of statistical analysis for greater understanding. Further, the applications and examples drawn from political science (including law) allow students to better see how they can apply these concepts and techniques in their own research and in future coursework"-- | ||
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Datensatz im Suchindex
| _version_ | 1804152812080201728 |
|---|---|
| adam_text | Contents
Tables, Figures, and Sidebars
Xiii
Preface
xix
Political Science, the Scientific Method, and
Statistical Analysis: An Overview
1
The Language of Science
2
Units of Analysis, Case, or Pact
2
Properties, Concepts, and Variables
3
Laws and Hypotheses
4
Theories
5
The Structure of Hypotheses
7
FaLifiability
7
Not Immediately Verifiable
9
The Beauty of Hypotheses for Reseatch
10
Increasing the Number and Types of Tests
10
Broadening Our Frame ofRiference or Context
11
Uncovering Theoretical Relevance
11
The Logic of Causation
—
A Review
12
Test
12
Theory or Theory Sketch
12
Tnangulization
14
Alternate Explanations
14
Potential Measurement Problems
15
Potential Design Problems
15
Key Terms
17
Questions and Exercises
17
VII
CONTENTS
2
How Do We Measure and Observe?
20
Statistical Measurement
—
An Introduction
21
Reliability of Measurement
23
Internal Validity of Measurement
24
Reliability versus Internal Invalidity
25
Precision in Measurement
26
Levels of Measurement
—
Mathematical Assumptions
27
Nominal Data
27
Ordinal Data
28
Interval Data
29
Levels of Measurement
—
Conceptual Assumptions
30
Frequency Distributions as Measurement and Observation
Ύ)
Absolute Frequency
33
Relative Frequencies: Percentages and Proportions
34
The Importance of Standardization
35
Cumulative Frequency
37
From Nominal to Interval Data
38
Graphs as Visualizations of Out Observations
39
Bar Chart
39
Pie Chan
40
Frequency Polygon and Line Charts
40
Time Series Charts
42
One More Example
46
Measuring Properties and the Importance of Categorization
46
Key Terms
48
Questions and Exercises
48
3
Central Tendency as Summary Observation
54
Measures of Central Tendency
55
Mode
55
Understanding Statistics as Games of Chance
57
Median
5
Я
Mean
60
Formulas as Shorthand Devices
65
FormuL· for the Mean Derived from a Frequency Distribution
67
A Note on Medians and Means
69
Appendix:
2012
Presidential Vote
69
Key Terms
71
Questions and Exercises
71
4
Dispersion/Variation/Goodness of Fit as Summary
Observation
77
Measures of Diversity for Nominal Data
78
Variation Ratio
79
Index of
Qualitative
Variation
81
VIII
C O N T F N T S
Measures of Diversity for Ordinal and Interval Data
89
Ihe Range and Interquartile Range
90
Deviation Scores
91
Mean Absolute Deviation
93
Variance (Mean Squared Deviation)
94.
Standard Deviation
95
A Summary Example with Aggregated Data
97
Key Terms
98
Questions and Exercises
99
5
Standardized Scores and Normal Distributions:
Tbe Concept of Relative Observation
102
How Well Off Are We?
103
Standardization and Z-Scores
106
A Policy Example
109
Furthering Our Understanding of ¿-Scores
110
Relative PUcement: Why a Student Should Never Ask
That Grades Be Curved
113
A Cautionary Tale
119
Key Terms
119
Questions and Exercises
120
6
Ал
Intuitive Introduction to Inference and
Hypothesis Testing
122
Inferential Statistics
123
The Law of Large Numbers
125
The Sampling Distribution of Means and the Central Limit Theorem
127
An Example: The Gender Gap in Wages
132
Hypothesis Testing
136
Considerations in Sampling
137
ŕ-Tests
and Statistical Hypothesis Testing
140
Statistical Hypothesis Testing and One-Tailed Tests
142
Key Terms
143
Questions and Exercises
1.43
7
Hypothesis Testing and the Concept of Association:
Observations and Expectations about the Difference
between Means
146
Comparison of Two Means
148
The Marriage Gap and Feelings toward Parties
154
Special Comment on Significance Tests
157
Appendix: Comparison oflwo Variables, Same or
Matched Groups
158
Key Terms
159
Questions and Exercises
159
IX
CONTENTS
8
Inferential Statistics for Proportions
164
Dichotomous Proportions
165
Chi-Squâre
(χ2)
171
Appendix: Visualizing Dichotomous Proportions as Means
181
Key Terms
182
Questions and Exercises
182
9
Measuring Association for Nominal and Ordinal Data
185
Contingency Tables
185
The On-Square Statistic
189
An Important Note
196
Proportional Reduction of Error and Lambda
196
Different Statistics, Different Assumptions, Different Calculations
201
Symmetrical versus Asymmetrical Measures
203
Interpretation of the Values of Measures of Association
204
No and Perfect Association
204
Statistical Independence
204
Accord
205
Strict Perfect Association
205
Weak Perfect Association
205
Implicit Perfect Association
206
Sensitivity to Marginal Distributions
206
All Roads Lead To...
207
Key Terms
211
Questions and Exercises
212
10
Research Design and the Use of Control Variables
217
Classic Experimental Design
218
Introducing a Third VariabLe
223
The Logic of Control: Tabular Data
225
A Third Variable Used to Test for an Alternate Explanation
225
Statistical Technique
—
Working through the Logic
227
Using a Third Variable to Test for an Explanatory Link
231
Using a Third Variable to Test for Specification/Interaction Effects
235
Ал
Example with Means
—
Simpson s Paradox
238
Key Terms
240
Questions and Exercises
240
11
Different by How Much? Linear Regression
247
You Already Know This: Reading a Linear Regression Equation
248
Which Line Fits Best? Minimizing the Sum of the Squared
Deviation Scores
252
Regression and PRE/PRV Measures (^Square)
256
Going Full Circle: Slopes, Pearsons r, and Back to
Ã-Square
262
:
Ο Ν Γ
F N T
5
is the Slope Really Different from the Mean? inference with Regression
263
Linear Is Not Always the Best Fit
265
Linear Regression Considerations
266
Key Terms
268
Questions and Exercises
268
12
Retracing Our Methodological Steps: Hypotheses, Multiple
Regression, and the Effects of Ihird Variables
272
Multiple Regression and Spurious Relationships
273
Dummy Variables
277
Multiple Regression and Explanatory Effects
278
Standardized Slopes
281
Multiple Regression and Additive Effects
281
Inference with Multiple Regression
282
Multiple Regression and Specification Effects
283
Key Terms
285
Questions and Exercises
286
Concluding Remarks
290
Glossary
291
Sample Solutions Guide
299
Index
329
XI
|
| any_adam_object | 1 |
| author | Galderisi, Peter F. 1951- |
| author_GND | (DE-588)170701166 |
| author_facet | Galderisi, Peter F. 1951- |
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| dewey-full | 320.015195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 320 - Political science (Politics and government) |
| dewey-raw | 320.015195 |
| dewey-search | 320.015195 |
| dewey-sort | 3320.015195 |
| dewey-tens | 320 - Political science (Politics and government) |
| discipline | Politologie |
| edition | First published |
| format | Book |
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| id | DE-604.BV042259980 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:16:39Z |
| institution | BVB |
| isbn | 9780415890052 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027697775 |
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| physical | xxiii, 339 Seiten Diagramme |
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| spelling | Galderisi, Peter F. 1951- Verfasser (DE-588)170701166 aut Understanding political science statistics observations and expectations in political analysis Peter Galderisi First published New York and London Routledge 2015 xxiii, 339 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier "Any student can learn the basic concepts of statistics if they are introduced as solutions to particular problems, and not formulas with a life of their own. In this text, students are introduced to a problem, asked to consider, conceptually, how one would address that problem, and then led through the derivation of the appropriate statistical formula. This applied method of teaching statistics through political science examples allows students to see the research method as problem solving. They learn the math, but only after they learn the concepts and methodological considerations that give the math context. The concepts throughout are presented through the lens of "observations and expectations," applied to myriad statistical techniques, both descriptive and inferential, as well as more generalized concepts of research methodology itself, such as hypothesis testing. Galderisi highlights that with each advance in technical sophistication, each statistical procedure is built on a small set of basic concepts, such as the reasons for standardization, the effects of outliers, or the concept of proportional reduction of error, to show that they are cumulative. More important than just memorizing a series of formula, this text emphasizes the underlying logic of statistical analysis for greater understanding. Further, the applications and examples drawn from political science (including law) allow students to better see how they can apply these concepts and techniques in their own research and in future coursework"-- Political statistics Political science / Methodology POLITICAL SCIENCE / General bisacsh Politische Wissenschaft Politikwissenschaftliches Studium (DE-588)4205357-2 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Politische Wissenschaft (DE-588)4076229-4 gnd rswk-swf Anwendung (DE-588)4196864-5 gnd rswk-swf Politikwissenschaftliches Studium (DE-588)4205357-2 s Statistik (DE-588)4056995-0 s DE-604 Politische Wissenschaft (DE-588)4076229-4 s Anwendung (DE-588)4196864-5 s Erscheint auch als Online-Ausgabe 978-0-203-83003-1 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027697775&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Galderisi, Peter F. 1951- Understanding political science statistics observations and expectations in political analysis Political statistics Political science / Methodology POLITICAL SCIENCE / General bisacsh Politische Wissenschaft Politikwissenschaftliches Studium (DE-588)4205357-2 gnd Statistik (DE-588)4056995-0 gnd Politische Wissenschaft (DE-588)4076229-4 gnd Anwendung (DE-588)4196864-5 gnd |
| subject_GND | (DE-588)4205357-2 (DE-588)4056995-0 (DE-588)4076229-4 (DE-588)4196864-5 |
| title | Understanding political science statistics observations and expectations in political analysis |
| title_auth | Understanding political science statistics observations and expectations in political analysis |
| title_exact_search | Understanding political science statistics observations and expectations in political analysis |
| title_full | Understanding political science statistics observations and expectations in political analysis Peter Galderisi |
| title_fullStr | Understanding political science statistics observations and expectations in political analysis Peter Galderisi |
| title_full_unstemmed | Understanding political science statistics observations and expectations in political analysis Peter Galderisi |
| title_short | Understanding political science statistics |
| title_sort | understanding political science statistics observations and expectations in political analysis |
| title_sub | observations and expectations in political analysis |
| topic | Political statistics Political science / Methodology POLITICAL SCIENCE / General bisacsh Politische Wissenschaft Politikwissenschaftliches Studium (DE-588)4205357-2 gnd Statistik (DE-588)4056995-0 gnd Politische Wissenschaft (DE-588)4076229-4 gnd Anwendung (DE-588)4196864-5 gnd |
| topic_facet | Political statistics Political science / Methodology POLITICAL SCIENCE / General Politische Wissenschaft Politikwissenschaftliches Studium Statistik Anwendung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027697775&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT galderisipeterf understandingpoliticalsciencestatisticsobservationsandexpectationsinpoliticalanalysis |