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Wish You Were Here! Strategies for Handling Missing Data

Wish You Were Here! Strategies for Handling Missing Data

Wish You Were Here! Strategies for Handling Missing Data. Agenda. Overview Types of Missing Data Strategies for Handling Missing Data Software Applications and Examples. Overview. Sources of Missing Data Item non-response Missing value for any given item Scale non-response

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720 views • 55 slides
Data Preprocessing

Data Preprocessing

Data Preprocessing. CSC 576: Data Science. Today. Data Preprocessing Handling Missing Values Handling Outliers Covariance and Correlation Normalization Binning, Discretization Sampling Aggregation. Data Preprocessing. Data Exploration phase results in finding data quality issues

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838 views • 44 slides
Introduction to Survey Data Analysis

Introduction to Survey Data Analysis

Introduction to Survey Data Analysis. Linda K. Owens, PhD Assistant Director for Sampling & Analysis Survey Research Laboratory University of Illinois at Chicago. Focus of the seminar. Data cleaning/missing data Sampling bias reduction. When analyzing survey data.

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596 views • 33 slides
Indexes, Scales, and Typologies

Indexes, Scales, and Typologies

Indexes, Scales, and Typologies. Edgar Degas: The Absinthe Drinker (detail), 1875-76. Indexes versus Scales Index A composite measure based upon the summed responses to dichotomous indicators .

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870 views • 30 slides
Chapter 16

Chapter 16

Chapter 16. Data Preparation and Description. Learning Objectives. Understand . . . importance of editing the collected raw data to detect errors and omissions how coding is used to assign number and other symbols to answers and to categorize responses

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348 views • 27 slides
Chapter 16

Chapter 16

Chapter 16. Data Preparation and Description. Learning Objectives. Understand . . . importance of editing the collected raw data to detect errors and omissions how coding is used to assign number and other symbols to answers and to categorize responses

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273 views • 27 slides
Chapter 16

Chapter 16

Chapter 16. Data Preparation and Description. Learning Objectives. Understand . . . importance of editing the collected raw data to detect errors and omissions how coding is used to assign number and other symbols to answers and to categorize responses

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273 views • 27 slides
Multiple Imputation with large proportions of missing data :how much is too much?

Multiple Imputation with large proportions of missing data :how much is too much?

Multiple Imputation with large proportions of missing data :how much is too much?. Jin is designed by Dr. Huber. Texas A&M HSC. Motivation. Motivations and Examples. Korean Female Colon Cancer. ?. ?. Is smoking protective ?. Not sure b/c Huge missing!!. background.

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398 views • 24 slides
Chapter 18

Chapter 18

Chapter 18. Data Preparation and Description. Learning Objectives. Understand the importance of editing the collected raw data to detect errors and omissions Understand how coding is used to assign number and other symbols to answers and to categorize responses

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253 views • 18 slides
Chapter 18

Chapter 18

Chapter 18. Data Preparation and Description. Learning Objectives. Understand the importance of editing the collected raw data to detect errors and omissions Understand how coding is used to assign number and other symbols to answers and to categorize responses

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277 views • 18 slides
Indexes and Scales ( ????? )

Indexes and Scales ( ????? )

Indexes and Scales ( ????? ). Social Research Methods 2117 & 6501 Fall, 2006 November 20, 2006. Recall: many social concepts are complex and multi-dimensional. So we often use multiple indicators to measure a concept. Composite measures ( ?????? ) of variables: indexes and scales

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450 views • 16 slides
Steps to Performing a Cluster Analysis

Steps to Performing a Cluster Analysis

Steps to Performing a Cluster Analysis. Rod Funk Chestnut Health Systems Bloomington, IL. Performing a Cluster Analysis. First step is deciding on what variables you want to cluster on Data can be continuous, counts or dichotomous

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226 views • 6 slides
Chapter 15

Chapter 15

Chapter 15. Data Preparation and Description. Learning Objectives. Understand . . . The importance of editing the collected raw data to detect errors and omissions. How coding is used to assign number and other symbols to answers and to categorize responses.

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144 views • 5 slides

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