Atjaunināt sīkdatņu piekrišanu

E-grāmata: Making Sense of Numbers: Quantitative Reasoning for Social Research

(Rutgers University, USA)
  • Formāts: PDF+DRM
  • Izdošanas datums: 23-Aug-2021
  • Izdevniecība: SAGE Publications Inc
  • Valoda: eng
  • ISBN-13: 9781544355627
  • Formāts - PDF+DRM
  • Cena: 82,09 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Formāts: PDF+DRM
  • Izdošanas datums: 23-Aug-2021
  • Izdevniecība: SAGE Publications Inc
  • Valoda: eng
  • ISBN-13: 9781544355627

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

"Making Sense of Numbers teaches students the skills they need to be both consumers and producers of quantitative research: able to read about, collect, calculate, and communicate numeric information for both everyday tasks and school or work assignments. The text teaches how to avoid making common errors of reasoning, calculation, or interpretation by introducing a systematic approach to working with numbers, showing students how to figure out what a particular number means. The text also demonstrates why it is important to apply a healthy dose of skepticism to the numbers we all encounter, so that we can understand how those numbers can (and cannot) be interpreted in their real-world context. Jane E. Miller uses annotated examples on a wide variety of topics to illustrate how to use new terms, concepts, and approaches to working with numbers. End-of-chapter engagement activities designed based on Miller's three decades of teaching experience can be used in class or as homework assignments, with some for students to do individually and others intended for group discussion. The book is ideally suited for a range of courses, including quantitative reasoning, research methods, basic statistics, data analysis, and communicating quantitative information"--

Making Sense of Numbers teaches students the skills they need to be both consumers and producers of quantitative research: able to read about, collect, calculate, and communicate numeric information for both everyday tasks and school or work assignments. The text teaches how to avoid making common errors of reasoning, calculation, or interpretation by introducing a systematic approach to working with numbers, showing students how to figure out what a particular number means. The text also demonstrates why it is important to apply a healthy dose of skepticism to the numbers we all encounter, so that we can understand how those numbers can (and cannot) be interpreted in their real-world context. Jane E. Miller uses annotated examples on a wide variety of topics to illustrate how to use new terms, concepts, and approaches to working with numbers. End-of-chapter engagement activities designed based on Miller’s three decades of teaching experience can be used in class or as homework assignments, with some for students to do individually and others intended for group discussion. The book is ideally suited for a range of courses, including quantitative reasoning, research methods, basic statistics, data analysis, and communicating quantitative information.

Recenzijas

This text invites students to develop an in-depth understanding of core concepts in research methods, clearly guides them through real-life examples, and offers tools needed for the development of strong analytical skills highly valued in the labor market. -- Maria Aysa-Lastra This an incredibly useful textbook, showing students how to interpret others quantitative research, think about quantitative research of their own, and communicate the findings of that research. I learned several great tips myself on writing effectively about quantitative research findings! -- Susan A. Dumais Making Sense of Numbers is an excellent companion for those learning to navigate the world of quantitative research. -- Marc Isaacson

List of Figures xxii
List of Tables xxvii
Preface xxix
Acknowledgments xxxiv
About the Author xxxvi
Part I: Introduction 1(36)
Chapter 1 Introduction to Making Sense of Numbers
2(11)
The Many Uses of Numbers
2(1)
Common Tasks Involving Numbers
3(1)
Plausibility of Numeric Values
4(1)
Challenges in Making Sense of Numbers
5(4)
A Cautionary Tale
8(1)
How We Learn to Make Sense of Numbers
9(4)
Chapter 2 Foundational Concepts for Quantitative Research
13(24)
Terminology for Quantitative Research
13(4)
The Research Circle
17(4)
Inductive Research
19(1)
Deductive Research
19(2)
Goals Of Quantitative Research
21(4)
Descriptive Research
21(1)
Exploratory Research
22(1)
Explanatory Research
22(1)
Evaluation Research
23(2)
The W's
25(1)
Report and Interpret Numbers
26(2)
Specify Direction and Magnitude
28(10)
Direction of Association
28(3)
Magnitude of an Association
31(6)
Part II: How Topic, Measurement, And Context Help Make Sense Of Numbers 37(76)
Chapter 3 Topic and Conceptualization
38(21)
Conceptualization
38(2)
Scope of a Definition
40(11)
How Change or Variation in a Definition Affects the Scope
45(5)
Anecdotes Are Not Definitions
50(1)
How Topic and Scope Affect Plausibility
51(3)
Concepts With Limits on Their Possible Values
52(1)
Positive and Negative Values
53(1)
Other Limits on Numeric Values
53(1)
How Topic and Perspective Affect Optimal Values
54(5)
How Topic Affects Optimal Value
54(1)
How Perspective Affects Optimal Value
55(4)
Chapter 4 Measurement
59(33)
Measurement
59(3)
Factors Affecting Operationalization
62(3)
Expected Audience and Use
62(1)
Ease of Measurement
63(1)
Ease of Comparison
64(1)
Measurement Issues for Secondary Data and Non-research Data Sources
65(1)
Levels of Measurement
65(4)
Categorical Variables
66(2)
Continuous Variables
68(1)
Units
69(4)
Unit of Observation
70(1)
System of Measurement
70(2)
Scale of Measurement
72(1)
Data Collection and Level of Measurement
73(8)
How Question Format Affects Measurement
75(6)
Closed-Ended Questions
75(1)
Open-Ended Questions
75(2)
Single-Response and Multiple-Response Questions
77(1)
Mutually Exclusive and Exhaustive Categories
78(1)
Missing Values
79(1)
Not Applicable
80(1)
How Measurement Affects Plausibility
81(1)
Level and Range of Possible Values
81(1)
Optimum Values
82(1)
Reliability and Validity of Numeric Measures
82(10)
Reliability
82(2)
Measurement Validity
84(9)
Face Validity
85(1)
Content Validity
86(1)
Criterion Validity
86(1)
Construct Validity
87(5)
Chapter 5 Context
92(21)
What Is Context?
92(1)
How Context Affects Plausibility
93(5)
Context as Limiting the Set of Cases Studied
96(1)
Other Dimensions of Context
97(1)
How Context Affects Measurement
98(1)
Population Versus Study Sample
98(2)
Representativeness
100(1)
Generalization
101(4)
Errors in Generalization
103(2)
Level of Analysis and Fallacy of Level
105(8)
Part III: Exhibits For Communicating Numeric Information 113(82)
Chapter 6 Working With Tables
114(35)
Criteria for Effective Tables
114(1)
Focused Tables
114(1)
Self-Contained Tables
115(1)
Anatomy of a Table
115(10)
Title
116(3)
Context
118(1)
Units
118(1)
Types of Measures or Statistics
118(1)
Row and Column Labels
119(1)
Indenting
120(1)
Column Headings
120(1)
Column Spanners
120(1)
Interior Cells
121(1)
Notes to Tables
121(1)
More Advanced Table Features
122(3)
Panels
122(3)
Organizing Data in Tables and Charts
125(12)
Level of Measurement
126(1)
Organizing Data in Tables to Accompany a Written Description
127(3)
Thematic Criteria
127(2)
Empirical Order
129(1)
Organizing Variables by Their Role in an Analysis
130(1)
Organizing Data in Tables Intended for Data Lookup
130(5)
Alphabetical Order
130(1)
Order of Items as Collected From Source
130(4)
Multiple Criteria for Organizing Tables
134(1)
Organizing Data in Three-Way Tables
135(2)
Reading Data From Tables
137(5)
Percentaging of Tables
137(13)
Direction of Percentage Calculations
138(2)
Percentages for Two-Category Variables
140(2)
Considerations for Creating Tables
142(7)
Portrait Versus Landscape Layout
142(1)
Alignment
143(1)
Digits and Decimal Places
143(6)
Chapter 7 Working With Charts and Visualizations
149(46)
Criteria for Effective Charts and Visualizations
150(2)
Focused Charts
150(1)
Self-Contained Charts
151(1)
Well-Organized Charts
151(1)
Visual Perception Principles
152(4)
Perceptual Tasks
152(1)
Cognitive Load
152(4)
Anatomy of a Chart or Visualization
156(5)
Title
156(2)
Axis Titles and Axis Labels
158(1)
Legend
158(1)
Data Labels
158(1)
Notes to Charts
159(1)
Other Features
159(1)
Reading Data From a Chart or Visualization
159(2)
Charts and Visualizations for Specific Tasks
161(21)
Presenting One Number
161(4)
Text Visualization for One Large Number
161(1)
Pie and Donut Charts
162(1)
Icon Arrays
163(2)
Parts of a Whole
165(3)
Pie Charts
165(2)
Stacked Bar Charts
167(1)
Comparing Two or More Numbers
168(4)
Categorical Independent Variables
168(2)
Continuous Independent Variables
170(2)
More Complex Patterns
172(8)
Clustered Bar Charts
172(1)
Multiple Stacked Bar Charts
173(1)
Diverging Stacked Bars
174(2)
Dumbbell Dot Plots
176(1)
Multiple-Line Chart
177(1)
Slopegraphs
177(3)
Maps
180(1)
Heat Maps
180(2)
Design Issues
182(2)
Use of Color
182(1)
Linear and Logarithmic Axis Scales
183(1)
Common Errors in Chart Creation
184(12)
Incorrect Chart for Level of Measurement
184(1)
Incorrect Axis Scales
184(1)
Inconsistent Axis Scales
185(2)
Incorrect Spacing of Values on an Axis
187(2)
Other Design Issues
189(7)
Pie With Too Many Categories
189(1)
Use of 3-D and Angled or Tilted Perspective
189(6)
Part IV: Making Sense Of Numbers From Mathematical And Statistical Methods 195(110)
Chapter 8 Comparison Values, Contrast Sizes, and Standards
196(33)
Reference Groups and Comparison Values
196(5)
Criteria for Identifying Comparison Values
197(4)
Comparisons Across Groups
197(3)
Temporal Comparisons
200(1)
Standards, Thresholds, and Target Values
201(11)
Standards
201(1)
Thresholds and Cutoffs
202(4)
Benchmarks, Targets, and Objectives
206(1)
Historical Records
207(1)
Cyclical Patterns
208(3)
Criteria for Choosing External Comparison Values or Standards
211(1)
Contrast Sizes for Quantitative Variables
212(4)
The "Goldilocks" Problem
212(2)
When a One-Unit Contrast Is Too Big
213(1)
When a One-Unit Contrast Is Too Small
214(1)
Criteria for Identifying Contrast Sizes
214(2)
Theoretical Criteria for Contrast Size
214(1)
Empirical Criteria for Contrast Size
215(1)
Considerations for Comparability
216(13)
Measurement
216(2)
Units
216(1)
Categories
217(1)
Context
218(3)
Comparison Across lime
218(1)
Comparison Across Location
219(1)
Comparison Across Units, Place, and Time
219(2)
Comparability of Standards
221(8)
Chapter 9 Numbers, Comparisons, and Calculations
229(31)
Numeric Measures of Level
230(5)
Value or Level
230(1)
Incidence and Prevalence
231(1)
Rating
232(1)
Ratios
233(1)
Rates
233(1)
Risk
234(1)
Plausibility Criteria for Measures of Level
235(1)
Measures of Position in a Ranked List
235(5)
Rank
235(5)
Percentile
240(1)
Plausibility Criteria for Measures of Position
240(2)
Mathematical Calculations
242(8)
Subtraction
242(1)
Division
243(2)
Relative Risk
244(1)
Percentage or Proportion of a Whole
245(1)
Percentage Difference
246(1)
Relationship between Ratio and Percentage Difference
247(1)
Percentage Change
247(3)
Percentage of Versus Percentile Versus Percentage Difference or Change
248(2)
Plausibility Criteria for Results of Calculations
250(1)
How Level of Measurement Affects Valid Types of Comparison
250(6)
Qualitative Variables
250(3)
Quantitative Variables
253(3)
Choosing Types of Comparisons
256(4)
Influence of Topic and Field
257(3)
Chapter 10 Distributions and Associations
260(45)
Distributions of Single Variables
261(13)
Measures of Central Tendency
261(2)
Mean
261(1)
Median
262(1)
Mode
262(1)
Measures of Variability
263(6)
Frequency Distribution
263(2)
Minimum and Maximum
265(1)
Range
265(1)
Variance
266(2)
Standard Deviation
268(1)
Shape
269(2)
Position in a Distribution: Standardized Score or Z-Score
271(3)
Plausibility Criteria for Univariate Statistics
274(3)
Tables and Charts for Presenting Distributions
277(10)
Portraying Distribution of Categorical Variables
277(6)
Nominal Variables
279(1)
Ordinal Variables
280(3)
Portraying Distribution of Continuous Variables
283(4)
Associations Between Two or More Variables
287(8)
Correlation
289(1)
Cross-Tabulation
290(3)
Difference in Means or ANOVA
293(2)
Three-Way Associations
295(2)
Two-Way Difference in Means or Two-Way ANOVA
295(1)
Three-Way Cross-Tabulations
296(1)
Plausibility Criteria for Bivariate and Three-Way Statistics
297(2)
Comparisons by Level of Measurement, Revisited
299(6)
Part V: Assessing The Quality Of Numeric Estimates 305(120)
Chapter 11 Bias
306(41)
What Is Bias?
307(1)
Time Structure of Study Designs
308(5)
Cross-Sectional Studies
308(1)
Longitudinal Studies
309(4)
Repeated Cross-Sectional Studies
309(1)
Prospective Studies
310(1)
Retrospective Studies
311(2)
Sampling Methods
313(12)
Probability Sampling Methods
314(6)
Simple Random Sampling
315(1)
Stratified Random Sampling
316(1)
Disproportionate Sampling
317(1)
Cluster Sampling
318(1)
Multistage Random Sampling
319(1)
Non-probability Sampling Methods
320(4)
Case-Control
321(1)
Convenience Sampling
321(2)
Quota Sampling
323(1)
Selective Observation
324(1)
Study Nonresponse
325(3)
Baseline Nonresponse
325(2)
Attrition From Longitudinal Studies
327(1)
Item Nonresponse
328(5)
Question Wording
329(1)
Respondents Who Lack Knowledge
330(1)
Missing Values vs. Not Applicable
331(2)
Measurement Bias
333(4)
Social Desirability Bias
333(1)
Biased Wording
334(1)
Recall Bias
335(1)
Poorly Worded Questions
336(1)
Respondents Who Lack Knowledge
336(1)
Data Sources
337(10)
Primary and Secondary Data
337(2)
Data Collected for Research Purposes
339(2)
Questionnaires
339(1)
Surveillance
339(1)
Other Sources of Research Data
340(1)
Data Collected for Non-research Purposes
341(7)
Administrative Data
341(1)
"Big Data"
341(6)
Chapter 12 Causality
347(40)
Causality Defined
348(2)
Why Does Causality Matter?
348(1)
Association Does not Equal Causation
349(1)
Criteria for Assessing Causality
350(11)
Empirical Association
350(2)
Time Order
352(3)
Study Design and Time Order
355(1)
Non-spuriousness
356(2)
Mechanism
358(1)
Dose-Response
359(1)
Summary of Assessing Causality
360(1)
Experimental Studies
361(9)
Randomization Into Treatment and Control Groups
362(3)
Pre/Post Measurement
365(2)
Blinding
367(2)
Colloquial vs. Research Meaning of "Experiment"
369(1)
Observational Studies
370(6)
Variation in the Independent Variable
371(1)
Time Order
372(2)
Mechanism
374(1)
Other Threats to Internal Validity
375(1)
Research Strategies for Assessing Confounding
376(4)
Randomize to Remove Confounders
376(1)
Measure and Take Into Account Potential Confounders
377(3)
Random Sampling vs. Random Assignment
380(2)
Implications of Causality for Quantitative Research
382(5)
Chapter 13 Uncertainty of Numeric Estimates
387(38)
What Is Statistical Uncertainty?
388(1)
Inferential Statistics
389(3)
Measures of Uncertainty
392(7)
Standard Error
392(1)
Margin of Error and Confidence Level
393(3)
Confidence Intervals
396(3)
Criteria for Making Sense of Measures of Uncertainty
399(1)
Uncertainty vs. Bias
399(3)
Basics of Hypothesis Testing
402(6)
Step 1: Write a Hypothesis
403(1)
Step 2: Obtain a p-Value
403(1)
Step 3: Specify the Significance Level
404(1)
Step 4: Assess Statistical Significance Using the p-Value
404(1)
Use of Confidence Intervals for Hypothesis Testing
405(2)
Criteria for Making Sense of Hypothesis-Testing Results
407(1)
Drawbacks of Traditional Hypothesis Testing
408(2)
Interpreting Inferential Statistics for Bivariate and Three-Way Procedures
410(16)
Bivariate Statistical Results
411(5)
Correlation
411(2)
Cross-Tabulation
413(1)
Difference in Means or ANOVA
414(2)
Three-Way Associations
416(11)
Three-Way Cross-Tabulations
416(2)
Two-Way ANOVA
418(2)
Multiple Regression
420(5)
Part VI: Pulling It All Together 425(78)
Chapter 14 Communicating Quantitative Research
426(43)
Tools for Presenting Quantitative Research
427(5)
What Is the Objective?
428(1)
How Many Numbers?
429(1)
General Shape or Precise Values?
429(2)
How Much Time?
431(1)
Who Is the Audience?
431(1)
Expository Writing Techniques
432(5)
Using Paragraphs to Organize Ideas
432(1)
Setting the Context
433(1)
Using Topic Sentences
434(1)
Using Evidentiary Sentences
434(1)
Using Transition Sentences
435(2)
Writing About Numbers in Particular
437(3)
Specifying Units
437(1)
Reporting and Interpreting Numbers
437(1)
Expressing Direction and Magnitude
437(1)
Specifying the Comparison Value
438(1)
Using Vocabulary and Calculations to Express Shape and Size
438(2)
Conveying the Type of Measure or Calculation
440(10)
Measures of Level
440(3)
Specific Level or Value
440(2)
Percentage or Proportion of a Whole
442(1)
Ratio Between Two Concepts
442(1)
Measures of Rank
443(1)
Results of Calculations
443(7)
Subtraction
446(1)
Division
447(2)
Percentage Difference or Change
449(1)
Writing About Distributions
450(4)
Nominal Variables
451(1)
Quantitative Variables
452(2)
Ordinal Variables
453(1)
Continuous Variables
453(1)
Writing About Associations
454(4)
Cross-Sectional Comparisons
454(1)
Nominal Independent Variables
454(1)
Ordinal Independent Variables
455(1)
Trends
455(2)
Comparison Against a Benchmark
457(1)
Writing About Complex Patterns
458(5)
Generalization
459(2)
Example
461(1)
Exception
461(2)
Content and Structure of Research Formats
463(6)
Research Audiences
463(1)
Lay Audiences
463(1)
Applied Audiences
464(5)
Chapter 15 The Role of Research Methods in Making Sense of Numbers
469(34)
The W's Revisited
470(1)
Practical Importance
470(7)
What Is Practical Importance?
470(1)
Practical Significance vs. Statistical Significance
471(9)
What Questions Can Statistical Significance Answer?
471(1)
What Questions Can't Statistical Significance Answer?
472(5)
Importance of a Numeric Finding: The Big Picture
477(3)
How Study Design, Measurement, and Sample Size Affect "Importance"
480(11)
Practical Importance
480(4)
Statistical Significance
484(1)
Internal Validity
485(2)
External Validity
487(1)
Relationships Among Dimensions of "Importance"
488(2)
Garbage In, Garbage Out
490(1)
Making Sense of Numbers in Quantitative Research Tasks
491(13)
Reading About and Assessing Others' Use of Numeric Information
496(1)
Collecting Numeric Data
497(2)
Conducting Mathematical and Elementary Statistical Analysis
499(1)
Communicating Quantitative Information
500(3)
Appendixes 503(41)
Appendix A: Why and How to Create New Variables
504(18)
Why New Variables Might Be Needed
504(3)
Transformations of Numbers
507(4)
Changing Scale
507(1)
Rounded Value
508(1)
Truncated Value
509(1)
Logarithmic Transformations
510(1)
Indexes and Scales
511(3)
New Continuous Variables
514(1)
Count Variables
514(1)
Other Types of Composite Measures
515(1)
New Categorical Variables
515(7)
Categorical Variable From a Continuous Variable
515(1)
Simplified Categorical Variable From a Detailed Categorical Variable
515(1)
New Categorical Variable From Two Categorical Variables
516(1)
Indicator Variables
517(1)
Indicator Variables From a Multiple-Response Item
517(5)
Appendix B: Sampling Weights
522(5)
The Purpose of Sampling Weights
522(1)
Sampling Weights for Disproportionate Sampling
523(2)
Communicating Use of Sampling Weights
525(2)
Appendix C: Brief Technical Background on Inferential Statistics
527(17)
Standard Error and Sample Size
528(1)
Margin of Error
528(1)
Confidence Interval
529(1)
Criteria for Making Sense of Measures of Uncertainty
529(2)
Hypothesis Testing
531(7)
Step 1: Specify Null and Alternative Hypotheses
531(2)
Important Aside About the "Null" Value
533(1)
Step 2: Calculate a Test Statistic
533(1)
Step 3: Obtain a p-Value
534(1)
Step 4: Assess Statistical Significance Using the p-Value
535(1)
Step 5: Interpret the Inferential Test Result
536(2)
Errors in Hypothesis Testing
538(2)
Plausibility Criteria for Inferential Test Statistics
540(4)
References 544(10)
Index 554
Jane E. Miller is a Professor at the Edward J. Bloustein School of Planning and Public Policy at Rutgers University, where she is Lead Instructor for the undergraduate Research Methods course and instructor for the undergraduate Honors Research Program. She also teaches graduate courses on data visualization and quantitative research. She was previously Faculty Director of Project L/EARN an intensive social science research training program for undergraduates from historically under-represented groups.

Dr. Miller has written two other books: The Chicago Guide to Writing about Numbers and The Chicago Guide to Writing about Multivariate Analysis (University of Chicago Press) both in their second editions, and also available in Chinese translation (Xinhua Publishing). She has also authored a series of articles in teaching and research journals on how to communicate about quantitative research. Dr. Millers research interests include relationships between poverty, child health, health insurance, and access to health care. She earned her bachelors degree in Economics from Williams College and her M.A. and PhD in Demography from the University of Pennsylvania.