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Empfohlen: 4 weeks of study, 1-2 hours/week...

Englisch

Untertitel: Englisch

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Ca. 9 Stunden zum Abschließen

Empfohlen: 4 weeks of study, 1-2 hours/week...

Englisch

Untertitel: Englisch

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1
3 Stunden zum Abschließen

General Steps in Weighting

Weights are used to expand a sample to a population. To accomplish this, the weights may correct for coverage errors in the sampling frame, adjust for nonresponse, and reduce variances of estimators by incorporating covariates. The series of steps needed to do this are covered in Module 1.

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7 Videos (Gesamt 48 min), 7 Lektüren, 7 Quiz
7 Videos
Quantities to Estimate8m
Goals of Estimation6m
Statistical Interpretation of Estimates10m
Coverage Problems5m
Improving Precision3m
Effects of Weighting on SEs2m
7 Lektüren
Class notes + additional reading10m
Class notes10m
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
7 praktische Übungen
Introductory quiz on weights6m
Quantities4m
Goals6m
Interpretation6m
Coverage4m
Improving precision6m
Effects on SEs6m
Woche
2
2 Stunden zum Abschließen

Specific Steps

Specific steps in weighting include computing base weights, adjusting if there are cases whose eligibility we are unsure of, adjusting for nonresponse, and using covariates to calibrate the sample to external population controls. We flesh out the general steps with specific details here.

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6 Videos (Gesamt 44 min), 6 Lektüren, 5 Quiz
6 Videos
Base Weights8m
Nonresponse Adjustments7m
Response Propensities4m
Tree algorithms10m
Calibration5m
6 Lektüren
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
5 praktische Übungen
Overview6m
Base weights6m
Nonresponse4m
Trees4m
Calibration6m
Woche
3
2 Stunden zum Abschließen

Implementing the Steps

Software is critical to implementing the steps, but the R system is an excellent source of free routines. This module covers several R packages, including sampling, survey, and PracTools that will select samples and compute weights.

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6 Videos (Gesamt 64 min), 5 Lektüren, 4 Quiz
6 Videos
Base Weights10m
More on Base Weights13m
Nonresponse Adjustments13m
Examples of Calibration7m
Software for Poststratification14m
5 Lektüren
Class Notes10m
Class Notes + Software10m
Class Notes10m
Class Notes + Software for propensity classes10m
Class Notes + Software for calibration10m
4 praktische Übungen
Software4m
Quiz on base weights8m
Quiz on nonresponse adjustments6m
Quiz on calibration and poststratification8m
Woche
4
2 Stunden zum Abschließen

Imputing for Missing Items

In most surveys there will be items for which respondents do not provide information, even though the respondent completed enough of the data collection instrument to be considered "complete". If only the cases with all items present are retained when fitting a model, quite a few cases may be excluded from the analysis. Imputing for the missing items avoids dropping the missing cases. We cover methods of doing the imputing and of reflecting the effects of imputations on standard errors in this module.

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6 Videos (Gesamt 46 min), 5 Lektüren, 5 Quiz
6 Videos
Means and hotdeck7m
Regression Imputation6m
Effect on Variances9m
mice R package4m
mice example10m
5 Lektüren
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes + mice R package10m
5 praktische Übungen
Reasons for imputing6m
Means and hot deck4m
Regression imputation8m
Effects on variances8m
Imputation software12m
13 Minuten zum Abschließen

Summary of Course 5

We briefly summarize the methods of weighting and imputation that were covered in Course 5.

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1 Video (Gesamt 3 min), 1 Lektüre
1 Video
1 Lektüre
Class Notes10m
3.8
21 BewertungenChevron Right

Top reviews from Dealing With Missing Data

von MMJun 5th 2017

This course quite help to get as much reliable data as possible for any survey.

Dozent

Avatar

Richard Valliant, Ph.D.

Research Professor
Joint Program in Survey Methodology

Über University of Maryland, College Park

The University of Maryland is the state's flagship university and one of the nation's preeminent public research universities. A global leader in research, entrepreneurship and innovation, the university is home to more than 37,000 students, 9,000 faculty and staff, and 250 academic programs. Its faculty includes three Nobel laureates, three Pulitzer Prize winners, 47 members of the national academies and scores of Fulbright scholars. The institution has a $1.8 billion operating budget, secures $500 million annually in external research funding and recently completed a $1 billion fundraising campaign. ...

Über die Spezialisierung Survey Data Collection and Analytics

This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data. In the final Capstone Project, you’ll apply the skills learned throughout the specialization by analyzing and comparing multiple data sources. Faculty for this specialisation comes from the Michigan Program in Survey Methodology and the Joint Program in Survey Methodology, a collaboration between the University of Maryland, the University of Michigan, and the data collection firm Westat, founded by the National Science Foundation and the Interagency Consortium of Statistical Policy in the U.S. to educate the next generation of survey researchers, survey statisticians, and survey methodologists. In addition to this specialization we offer short courses, a summer school, certificates, master degrees as well as PhD programs....
Survey Data Collection and Analytics

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