Atnaujinkite slapukų nuostatas

Modern Survey Analysis: Using Python for Deeper Insights 2021 ed. [Kietas viršelis]

  • Formatas: Hardback, 347 pages, aukštis x plotis: 235x155 mm, weight: 729 g, 221 Illustrations, color; 5 Illustrations, black and white; XXVI, 347 p. 226 illus., 221 illus. in color., 1 Hardback
  • Išleidimo metai: 12-Sep-2022
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030762661
  • ISBN-13: 9783030762667
Kitos knygos pagal šią temą:
Modern Survey Analysis: Using Python for Deeper Insights 2021 ed.Didesnis paveikslėlis
  • Formatas: Hardback, 347 pages, aukštis x plotis: 235x155 mm, weight: 729 g, 221 Illustrations, color; 5 Illustrations, black and white; XXVI, 347 p. 226 illus., 221 illus. in color., 1 Hardback
  • Išleidimo metai: 12-Sep-2022
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030762661
  • ISBN-13: 9783030762667
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783030762667.html
Raktažodžiai:

Surveys are widely used as a primary way to gather data, and ultimately information, on attitudes, interests, and opinions (AIOs) of customers and constituents that are vital for private or public sector policy decisions. A compact volume that discusses and illustrates methods of analysis, this book is essential to those who evaluate survey data in either sector. It focuses on two overarching objectives:

    Demonstrating how to extract actionable, insightful, and useful information from their survey data
  1. Introducing Python and Pandas and showing readers how to use this software to analyze their survey data.
This book offers readers fundamentals of analyzing survey data from a tabular, visual, and statistical perspective. Readers will be able to use Python to create data visuals, to create and analyze tables, and to create and analyze correspondence maps of the tables. The included statistics is at an introductory level. A Jupyter notebook with data is provided.


1 Introduction to Modern Survey Analytics
1(34)
1.1 Information and Survey Data
3(1)
1.2 Demystifying Surveys
4(12)
1.2.1 Survey Objectives
5(2)
1.2.2 Target Audience and Sample Size
7(2)
1.2.2.1 Key Parameters to Estimate
9(1)
1.2.2.2 Sample Design to Use
9(1)
1.2.2.3 Population Size
10(1)
1.2.2.4 Alpha
10(1)
1.2.2.5 Margin of Error
10(1)
1.2.2.6 Additional Information
10(2)
1.2.3 Screener and Questionnaire Design
12(2)
1.2.4 Fielding the Study
14(1)
1.2.5 Data Analysis
14(2)
1.2.6 Report Writing and Presentation
16(1)
1.3 Sample Representativeness
16(6)
1.3.1 Digression on Indicator Variables
20(1)
1.3.2 Calculating the Population Parameters
21(1)
1.4 Estimating Population Parameters
22(3)
1.5 Case Studies
25(5)
1.5.1 Consumer Study: Yogurt Consumption
25(2)
1.5.2 Public Sector Study: VA Benefits Survey
27(1)
1.5.3 Public Opinion Study: Toronto Casino Opinion Survey
28(2)
1.5.4 Public Opinion Study: San Francisco Airport Customer Satisfaction Survey
30(1)
1.6 Why Use Python for Survey Data Analysis?
30(2)
1.7 Why Use Jupyter for Survey Data Analysis?
32(3)
2 First Step: Working with Survey Data
35(48)
2.1 Best Practices: First Steps to Analysis
36(7)
2.1.1 Installing and Importing Python Packages
36(3)
2.1.2 Organizing Routinely Used Packages, Functions, and Formats
39(2)
2.1.3 Defining Data Paths and File Names
41(1)
2.1.4 Defining Your Functions and Formatting Statements
42(1)
2.1.5 Documenting Your Data with a Dictionary
42(1)
2.2 Importing Your Data with Pandas
43(5)
2.3 Handling Missing Values
48(4)
2.3.1 Identifying Missing Values
49(1)
2.3.2 Reporting Missing Values
49(1)
2.3.3 Reasons for Missing Values
50(1)
2.3.4 Dealing with Missing Values
51(1)
2.3.4.1 Use the fillna() Method
51(1)
2.3.4.2 Use the Interpolation) Method
51(1)
2.3.4.3 An Even More Sophisticated Method
52(1)
2.4 Handling Special Types of Survey Data
52(4)
2.4.1 CATA Questions
52(1)
2.4.1.1 Multiple Responses
53(1)
2.4.1.2 Multiple Responses by ID
53(1)
2.4.1.3 Multiple Responses Delimited
54(1)
2.4.1.4 Indicator Variable
54(1)
2.4.1.5 Frequencies
54(1)
2.4.2 Categorical Questions
54(2)
2.5 Creating New Variables, Binning, and Rescaling
56(11)
2.5.1 Creating Summary Variables
58(4)
2.5.2 Rescaling
62(2)
2.5.3 Other Forms of Preprocessing
64(3)
2.6 Knowing the Structure of the Data Using Simple Statistics
67(3)
2.6.1 Descriptive Statistics and DataFrame Checks
68(1)
2.6.2 Obtaining Value Counts
69(1)
2.6.3 Styling Your DataFrame Display
69(1)
2.7 Weight Calculations
70(10)
2.7.1 Complex Weight Calculation: Raking
73(2)
2.7.2 Types of Weights
75(5)
2.8 Querying Data
80(3)
3 Shallow Survey Analysis
83(30)
3.1 Frequency Summaries
84(2)
3.1.1 Ordinal-Based Summaries
85(1)
3.1.2 Nominal-Based Summaries
86(1)
3.2 Basic Descriptive Statistics
86(3)
3.3 Cross-Tabulations
89(5)
3.4 Data Visualization
94(17)
3.4.1 Visuals Best Practice
95(1)
3.4.2 Data Visualization Background
95(3)
3.4.3 Pie Charts
98(1)
3.4.4 Bar Charts
99(2)
3.4.5 Other Charts and Graphs
101(4)
3.4.5.1 Histograms and Boxplots for Distributions
105(1)
3.4.5.2 Mosaic Charts
105(4)
3.4.5.3 Heatmaps
109(2)
3.5 Weighted Summaries: Crosstabs and Descriptive Statistics
111(2)
4 Beginning Deep Survey Analysis
113(64)
4.1 Hypothesis Testing
114(8)
4.1.1 Hypothesis Testing Background
115(3)
4.1.2 Examples of Hypotheses
118(1)
4.1.3 A Formal Framework for Statistical Tests
118(1)
4.1.4 A Less Formal Framework for Statistical Tests
119(1)
4.1.5 Types of Tests to Use
120(2)
4.2 Quantitative Data: Tests of Means
122(20)
4.2.1 Test of One Mean
122(4)
4.2.2 Test of Two Means for Two Populations
126(1)
4.2.2.1 Standard Errors: Independent Populations
126(3)
4.2.2.2 Standard Errors: Dependent Populations
129(2)
4.2.3 Test of More Than Two Means
131(11)
4.3 Categorical Data: Tests of Proportions
142(11)
4.3.1 Single Proportions
143(1)
4.3.2 Comparing Proportions: Two Independent Populations
144(2)
4.3.3 Comparing Proportions: Paired Populations
146(1)
4.3.4 Comparing Multiple Proportions
147(6)
4.4 Advanced Tabulations
153(5)
4.5 Advanced Visualization
158(19)
4.5.1 Extended Visualizations
159(3)
4.5.2 Geographic Maps
162(3)
4.5.3 Dynamic Graphs
165(1)
Appendix
166(11)
5 Advanced Deep Survey Analysis: The Regression Family
177(32)
5.1 The Regression Family and Link Functions
178(1)
5.2 The Identity Link: Introduction to OLS Regression
179(8)
5.2.1 OLS Regression Background
180(1)
5.2.2 The Classical Assumptions
180(1)
5.2.3 Example of Application
181(1)
5.2.4 Steps for Estimating an OLS Regression
182(4)
5.2.5 Predicting with the OLS Model
186(1)
5.3 The Logit Link: Introduction to Logistic Regression
187(13)
5.3.1 Logistic Regression Background
189(3)
5.3.2 Example of Application
192(2)
5.3.3 Steps for Estimating a Logistic Regression
194(6)
5.3.4 Predicting with the Logistic Regression Model
200(1)
5.4 The Poisson Link: Introduction to Poisson Regression
200(9)
5.4.1 Poisson Regression Background
200(1)
5.4.2 Example of Application
201(1)
5.4.3 Steps for Estimating a Poisson Regression
201(1)
5.4.4 Predicting with the Poisson Regression Model
202(1)
Appendix
203(6)
6 Sample of Specialized Survey Analyses
209(28)
6.1 Conjoint Analysis
210(7)
6.1.1 Case Study
210(1)
6.1.2 Analysis Steps
210(1)
6.1.3 Creating the Design Matrix
211(1)
6.1.4 Fielding the Conjoint Study
212(2)
6.1.5 Estimating a Conjoint Model
214(1)
6.1.6 Attribute Importance Analysis
215(2)
6.2 Net Promoter Score
217(7)
6.3 Correspondence Analysis
224(4)
6.4 Text Analysis
228(9)
7 Complex Surveys
237(14)
7.1 Complex Sample Survey Estimation Effects
239(1)
7.2 Sample Size Calculation
240(1)
7.3 Parameter Estimation
241(3)
7.4 Tabulation
244(2)
7.4.1 Tabulation
245(1)
7.4.2 CrossTabulation
245(1)
7.5 Hypothesis Testing
246(5)
7.5.1 One-Sample Test: Hypothesized Mean
247(1)
7.5.2 Two-Sample Test: Independence Case
248(1)
7.5.3 Two-Sample Test: Paired Case
248(3)
8 Bayesian Survey Analysis: Introduction
251(52)
8.1 Frequentist vs Bayesian Statistical Approaches
253(6)
8.2 Digression on Bayes' Rule
259(6)
8.2.1 Bayes' Rule Derivation
259(2)
8.2.2 Bayes' Rule Reexpressions
261(1)
8.2.3 The Prior Distribution
262(1)
8.2.4 The Likelihood Function
263(1)
8.2.5 The Marginal Probability Function
263(1)
8.2.6 The Posterior Distribution
264(1)
8.2.7 Hyperparameters of the Distributions
264(1)
8.3 Computational Method: MCMC
265(4)
8.3.1 Digression on Markov Chain Monte Carlo Simulation
265(4)
8.3.2 Sampling from a Markov Chain Monte Carlo Simulation
269(1)
8.4 Python Package pyMC3: Overview
269(1)
8.5 Case Study
270(3)
8.5.1 Basic Data Analysis
272(1)
8.6 Benchmark OLS Regression Estimation
273(1)
8.7 Using pyMC3
274(15)
8.7.1 pyMC3 Bayesian Regression Setup
274(6)
8.7.2 Bayesian Estimation Results
280(1)
8.7.2.1 The MAP Estimate
280(2)
8.7.2.2 The Visualization Output
282(7)
8.8 Extensions to Other Analyses
289(11)
8.8.1 Sample Mean Analysis
290(1)
8.8.2 Sample Proportion Analysis
290(1)
8.8.3 Contingency Table Analysis
291(4)
8.8.4 Logit Model for Contingency Table
295(2)
8.8.5 Poisson Model for Count Data
297(3)
8.9 Appendix
300(3)
8.9.1 Beta Distribution
300(1)
8.9.2 Half-Normal Distribution
300(1)
8.9.3 Bernoulli Distribution
301(2)
9 Bayesian Survey Analysis: Multilevel Extension
303(34)
9.1 Multilevel Modeling: An introduction
304(4)
9.1.1 Omitted Variable Bias
305(2)
9.1.2 Simple Handling of Data Structure
307(1)
9.1.3 Nested Market Structures
307(1)
9.2 Multilevel Modeling: Some Observations
308(4)
9.2.1 Aggregation and Disaggregation Issues
309(1)
9.2.2 Two Fallacies
310(1)
9.2.3 Terminology
311(1)
9.2.4 Ubiquity of Hierarchical Structures
311(1)
9.3 Data Visualization of Multilevel Data
312(6)
9.3.1 Basic Data Visualization and Regression Analysis
313(5)
9.4 Case Study Modeling
318(5)
9.4.1 Pooled Regression Model
318(1)
9.4.2 Unpooled (Dummy Variable) Regression Model
319(2)
9.4.3 Multilevel Regression Model
321(2)
9.5 Multilevel Modeling Using pyMC3: Introduction
323(5)
9.5.1 Multilevel Model Notation
324(1)
9.5.2 Multilevel Model Formulation
324(1)
9.5.3 Example Multilevel Estimation Set-up
325(3)
9.5.4 Example Multilevel Estimation Analyses
328(1)
9.6 Multilevel Modeling with Level Explanatory Variables
328(1)
9.7 Extensions of Multilevel Models
328(9)
9.7.1 Logistic Regression Model
330(2)
9.7.2 Possion Model
332(1)
9.7.3 Panel Data
332(1)
Appendix
333(4)
References 337(6)
Index 343
Walter R. Paczkowski, PhD, has worked at AT&T, AT&T Bell Labs, and AT&T Labs. He founded Data Analytics Corp., a statistical consulting company, in 2001. Dr. Paczkowski is also a part-time lecturer of economics at Rutgers University. He is the author of Business Analytics: Data Science for Business Problems (2022), Deep Data Analytics for New Product Development (2020), Pricing Analytics: Models and Advanced Quantitative Techniques for Product Pricing (2018), and Market Data Analysis Using JMP (2016).