36 Questions
What are missing values in data?
Codes used to indicate lack of response
What should be considered before applying a missing-data procedure?
Whether an underlying “true” value exists and whether it is unknown
What is the relationship between missing values and coarsened data?
Missing values are part of the broader concept of coarsened data
What are latent variables?
Unobservable quantities imperfectly measured by tests or questionnaires
What should be the goal of a statistical procedure?
To make valid and efficient inferences about a population of interest
What are the basic criteria for evaluating statistical procedures?
Small bias and variance, accurate standard errors and confidence intervals, and honesty in reporting measures of uncertainty
Why is it important to make assumptions about the processes causing missing data explicit?
To investigate the sensitivity of results to alternative assumptions
What are the two types of nonresponse in surveys?
Unit nonresponse and item nonresponse
What are common types of missing data in longitudinal studies?
Wave nonresponse and attrition
What are the three classes of overall missing-data patterns?
Univariate, monotone, and arbitrary patterns
What is the distribution of missingness?
A mathematical device to describe the rates and patterns of missing values and to capture possible relationships between missingness and the values of missing items
What is the relationship between missing values and deceased participants in longitudinal studies?
It is sometimes reasonable to consider characteristics of deceased participants as missing values for computational purposes
What are missing values in data?
Codes used to indicate lack of response
What should be considered before applying a missing-data procedure?
Whether an underlying “true” value exists and whether it is unknown
What is the relationship between missing values and coarsened data?
Missing values are part of the broader concept of coarsened data
What are latent variables?
Unobservable quantities imperfectly measured by tests or questionnaires
What should be the goal of a statistical procedure?
To make valid and efficient inferences about a population of interest
What are the basic criteria for evaluating statistical procedures?
Small bias and variance, accurate standard errors and confidence intervals, and honesty in reporting measures of uncertainty
Why is it important to make assumptions about the processes causing missing data explicit?
To investigate the sensitivity of results to alternative assumptions
What are the two types of nonresponse in surveys?
Unit nonresponse and item nonresponse
What are common types of missing data in longitudinal studies?
Wave nonresponse and attrition
What are the three classes of overall missing-data patterns?
Univariate, monotone, and arbitrary patterns
What is the distribution of missingness?
A mathematical device to describe the rates and patterns of missing values and to capture possible relationships between missingness and the values of missing items
What is the relationship between missing values and deceased participants in longitudinal studies?
It is sometimes reasonable to consider characteristics of deceased participants as missing values for computational purposes
What are missing values in data?
Codes used to indicate lack of response
What should be considered before applying a missing-data procedure?
Whether an underlying 'true' value exists and whether it is unknown
What are latent variables?
Unobservable quantities imperfectly measured by tests or questionnaires
What should be the goal of a statistical procedure?
To make valid and efficient inferences about a population of interest
What are basic criteria for evaluating statistical procedures?
Small bias and variance, accurate standard errors and confidence intervals, and honesty in reporting measures of uncertainty
What is unit nonresponse?
Where the entire data collection procedure fails
What is item nonresponse?
Where partial data are available
What are wave nonresponse and attrition?
Common types of missing data in longitudinal studies
What are the important classes of overall missing-data patterns?
Univariate, monotone, and arbitrary patterns
What is the distribution of missingness?
A mathematical device to describe the rates and patterns of missing values and to capture possible relationships between missingness and the values of missing items
What is coarsened data?
Grouped, aggregated, rounded, censored, or truncated numbers resulting in partial loss of information
What is an example of missing values in longitudinal studies?
Characteristics of deceased participants as missing values for computational purposes
Study Notes
Understanding Missing Values in Data Analysis
- Missing values in data refer to codes used to indicate lack of response, such as “Don’t know” or “Refused”.
- Before applying a missing-data procedure, it is important to consider whether an underlying “true” value exists and whether it is unknown.
- In longitudinal studies, it is sometimes reasonable to consider characteristics of deceased participants as missing values for computational purposes.
- Missing values are part of the broader concept of coarsened data, which includes grouped, aggregated, rounded, censored, or truncated numbers resulting in partial loss of information.
- Latent variables, which are unobservable quantities imperfectly measured by tests or questionnaires, are also related to missing data.
- The goal of a statistical procedure should be to make valid and efficient inferences about a population of interest, not to estimate or recover missing observations.
- Basic criteria for evaluating statistical procedures include small bias and variance, accurate standard errors and confidence intervals, and honesty in reporting measures of uncertainty.
- It is important to make assumptions about the processes causing missing data explicit and investigate the sensitivity of results to alternative assumptions.
- Unit nonresponse, where the entire data collection procedure fails, and item nonresponse, where partial data are available, are two types of nonresponse in surveys.
- In longitudinal studies, wave nonresponse and attrition are common types of missing data, and procedures that use all available data for each participant are recommended.
- There are several important classes of overall missing-data patterns, including univariate, monotone, and arbitrary patterns.
- The distribution of missingness is best regarded as a mathematical device to describe the rates and patterns of missing values and to capture possible relationships between missingness and the values of missing items.
Test your knowledge on missing values in data analysis with our quiz! Learn about the different types of missing data, their impact on statistical procedures, and how to evaluate statistical methods for handling missing values. This quiz covers topics such as coarsened data, latent variables, nonresponse in surveys and longitudinal studies, and missing-data patterns. Sharpen your skills and improve your understanding of missing values in data analysis with this quiz.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
