Weighting Missing Data Coding and Data Preparation Wrap-up Preview of Next Time. Data Management
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1 Data Management Department of Political Science and Government Aarhus University November 24, 2014
2 Data Management Weighting Handling missing data Categorizing missing data types Imputation Summary measures Scale construction Combining question branches Coding and editing Open-ended questions Marking problematic data Data preparation Codebook creation File formats Archiving, access, and rights
3 1 Weighting 2 Missing Data 3 Coding and Data Preparation 4 Wrap-up 5 Preview of Next Time
4 1 Weighting 2 Missing Data 3 Coding and Data Preparation 4 Wrap-up 5 Preview of Next Time
5 Goal of Survey Research The goal of survey research is to estimate population-level quantities (e.g., means, proportions, totals) Samples estimate those quantities with uncertainty (sampling error) Sample estimates are unbiased if they match population quantities
6 Realities of Survey Research Sample may not match population for a variety of reasons: Due to constraints on design Due to sampling frame coverage Due to intentional over/under-sampling Due to nonresponse Due to sampling error
7 Realities of Survey Research Sample may not match population for a variety of reasons: Due to constraints on design Due to sampling frame coverage Due to intentional over/under-sampling Due to nonresponse Due to sampling error Weights can be used to correct a sample
8 Realities of Survey Research Sample may not match population for a variety of reasons: Due to constraints on design Due to sampling frame coverage Due to intentional over/under-sampling Due to nonresponse Due to sampling error Weights can be used to correct a sample Weighting is never perfect Limited to work with observed variables Rarely have good knowledge of coverage, nonresponse, or sampling error Weighting can increase sampling variance
9 Three Kinds of Weights Design Weights Nonresponse Weights Post-Stratification Weights
10 Design Weights Address design-related unequal probability of selection into a sample Applied to complex survey designs: Disproportionate allocation stratified sampling Oversampling of subpopulations Cluster sampling Combinations thereof
11 Design Weights: Simple Random Sampling Imagine sampling frame of 100,000 units Sample size will be 1,000 What is the probability that a unit in the sampling frame is included in the sample?
12 Design Weights: Simple Random Sampling Imagine sampling frame of 100,000 units Sample size will be 1,000 What is the probability that a unit in the sampling frame is included in the sample? p = ,000 =.01
13 Design Weights: Simple Random Sampling Imagine sampling frame of 100,000 units Sample size will be 1,000 What is the probability that a unit in the sampling frame is included in the sample? p = ,000 =.01 Design weight for all units is w = 1/p = 100 SRS is self-weighting
14 Design Weights: Stratified Sample Imagine sampling frame of 100,000 units 90,000 Danes & 10,000 Immigrants Sample size will be 1,000 (proportionate allocation) 900 Danes & 100 Immigrants What is the probability that a unit in the sampling frame is included in the sample?
15 Design Weights: Stratified Sample Imagine sampling frame of 100,000 units 90,000 Danes & 10,000 Immigrants Sample size will be 1,000 (proportionate allocation) 900 Danes & 100 Immigrants What is the probability that a unit in the sampling frame is included in the sample? p Danish = ,000 =.01 p Imm = ,000 =.01
16 Design Weights: Stratified Sample Imagine sampling frame of 100,000 units 90,000 Danes & 10,000 Immigrants Sample size will be 1,000 (proportionate allocation) 900 Danes & 100 Immigrants What is the probability that a unit in the sampling frame is included in the sample? p Danish = ,000 =.01 p Imm = ,000 =.01 Design weight for all units is w = 1/p = 100 Proportionate allocation is self-weighting
17 Design Weights: Stratified Sample Imagine sampling frame of 100,000 units 90,000 Danes & 10,000 Immigrants Sample size will be 1,000 (disproportionate allocation) 500 Danes & 500 Immigrants What is the probability that a unit in the sampling frame is included in the sample?
18 Design Weights: Stratified Sample Imagine sampling frame of 100,000 units 90,000 Danes & 10,000 Immigrants Sample size will be 1,000 (disproportionate allocation) 500 Danes & 500 Immigrants What is the probability that a unit in the sampling frame is included in the sample? p Danish = ,000 =.0056 p Imm = ,000 =.05
19 Design Weights: Stratified Sample Imagine sampling frame of 100,000 units 90,000 Danes & 10,000 Immigrants Sample size will be 1,000 (disproportionate allocation) 500 Danes & 500 Immigrants What is the probability that a unit in the sampling frame is included in the sample? p Danish = ,000 =.0056 p Imm = ,000 =.05 Design weights differ across units: w Danish = 1/p Danish = w Imm = 1/p Imm = 20 Disproportionate allocation is not self-weighting
20 Design Weights: Cluster Sample Imagine sampling frame of 1000 units in 5 clusters of varying sizes Sample size will be 10 each from 3 clusters What is the probability that a unit in the sampling frame is included in the sample? p = n clusters /N clusters 1/n cluster = 3 5 1/n cluster
21 Design Weights: Cluster Sample Imagine sampling frame of 1000 units in 5 clusters of varying sizes Sample size will be 10 each from 3 clusters What is the probability that a unit in the sampling frame is included in the sample? p = n clusters /N clusters 1/n cluster = 3 5 1/n cluster
22 Design Weights: Cluster Sample Imagine sampling frame of 1000 units in 5 clusters of varying sizes Sample size will be 10 each from 3 clusters What is the probability that a unit in the sampling frame is included in the sample? p = n clusters /N clusters 1/n cluster = 3 5 1/n cluster Design weights differ across units: Clusters are equally likely to be sampled Probability of selection within cluster varies with cluster size Cluster sampling is rarely self-weighting
23 Nonresponse Weights Correct for nonresponse Require knowledge of nonrespondents on variables that have been measured for respondents Requires data are missing at random Two common methods Weighting classes Propensity score subclassification
24 Nonresponse Weights: Example Imagine immigrants end up being less likely to respond 1 RR Danish = 1.0 RR Imm = This refers to a lower RR in this particular survey sample, not in general.
25 Nonresponse Weights: Example Imagine immigrants end up being less likely to respond 1 RR Danish = 1.0 RR Imm = 0.8 Using weighting classes: w rr,danish = 1/1 = 1 w rr,imm = 1/0.8 = 1.25 Can generalize to multiple variables and strata 1 This refers to a lower RR in this particular survey sample, not in general.
26 Post-Stratification Correct for nonresponse, coverage errors, and sampling errors
27 Post-Stratification Correct for nonresponse, coverage errors, and sampling errors Reweight sample data to match population distributions Divide sample and population into strata Weight units in each stratum so that the weighted sample stratum contains the same proportion of units as the population stratum does
28 Post-Stratification Correct for nonresponse, coverage errors, and sampling errors Reweight sample data to match population distributions Divide sample and population into strata Weight units in each stratum so that the weighted sample stratum contains the same proportion of units as the population stratum does There are numerous other related techniques
29 Post-Stratification: Example Imagine our sample ends up skewed on immigration status and gender relative to the population Group Pop. Sample Rep. Weight Danish, Female.45.5 Danish, Male.45.4 Immigrant, Female Immigrant, Male PS weight is just w ps = N l /n l
30 Post-Stratification: Example Imagine our sample ends up skewed on immigration status and gender relative to the population Group Pop. Sample Rep. Weight Danish, Female.45.5 Over Danish, Male.45.4 Under Immigrant, Female Over Immigrant, Male Under PS weight is just w ps = N l /n l
31 Post-Stratification: Example Imagine our sample ends up skewed on immigration status and gender relative to the population Group Pop. Sample Rep. Weight Danish, Female.45.5 Over Danish, Male.45.4 Under Immigrant, Female Over Immigrant, Male Under PS weight is just w ps = N l /n l
32 Post-Stratification: Example Imagine our sample ends up skewed on immigration status and gender relative to the population Group Pop. Sample Rep. Weight Danish, Female.45.5 Over Danish, Male.45.4 Under Immigrant, Female Over Immigrant, Male Under PS weight is just w ps = N l /n l
33 Post-Stratification: Example Imagine our sample ends up skewed on immigration status and gender relative to the population Group Pop. Sample Rep. Weight Danish, Female.45.5 Over Danish, Male.45.4 Under Immigrant, Female Over Immigrant, Male Under PS weight is just w ps = N l /n l
34 Post-Stratification: Example Imagine our sample ends up skewed on immigration status and gender relative to the population Group Pop. Sample Rep. Weight Danish, Female.45.5 Over Danish, Male.45.4 Under Immigrant, Female Over Immigrant, Male Under PS weight is just w ps = N l /n l
35 Post-Stratification Should only be done after correcting for sampling design Strata must be large (n > 15) Need accurate population-level stratum sizes Only useful if stratifying variables are related to key constructs of interest
36 Post-Stratification Should only be done after correcting for sampling design Strata must be large (n > 15) Need accurate population-level stratum sizes Only useful if stratifying variables are related to key constructs of interest This is the basis for inference in non-probability samples Probability samples make design-based inferences
37 Questions about weighting?
38 1 Weighting 2 Missing Data 3 Coding and Data Preparation 4 Wrap-up 5 Preview of Next Time
39 Sources of Missing Data Unit or item nonresponse Attrition or break-off Data loss
40 Effects of Missing Data Sampling variance and effective sample size
41 Effects of Missing Data Sampling variance and effective sample size Scale construction and multi-variate analysis
42 Effects of Missing Data Sampling variance and effective sample size Scale construction and multi-variate analysis Bias in estimates
43 Imputation Definition
44 Imputation Definition: Systematic replacement of missing values
45 Imputation Definition: Systematic replacement of missing values Why? Casewise deletion creates loss of information Preserve sampling variances (i.e., no loss of precision)
46 Imputation Definition: Systematic replacement of missing values Why? Casewise deletion creates loss of information Preserve sampling variances (i.e., no loss of precision) Considerations Why are data missing? How do we impute? What are the consequences of imputation?
47 Missing Data Assumptions Missing Completely At Random (MCAR) Missing At Random (MAR) Missing Not At Random (MNAR)
48 Imputation Methods Single Imputation
49 Imputation Methods Single Imputation Mean imputation Top/bottom category imputation Random imputation Hot deck imputation Regression imputation
50 Imputation Methods Single Imputation Mean imputation Top/bottom category imputation Random imputation Hot deck imputation Regression imputation Multiple Imputation
51 Imputation Methods Single Imputation Mean imputation Top/bottom category imputation Random imputation Hot deck imputation Regression imputation Multiple Imputation Single imputation multiple times, combining results across data sets Can apply numerous imputation methods Accounts for uncertainty due to missingness
52 1 Weighting 2 Missing Data 3 Coding and Data Preparation 4 Wrap-up 5 Preview of Next Time
53 Coding What is coding? Categorizing responses Assigning numeric values to categories
54 Coding What is coding? Categorizing responses Assigning numeric values to categories When in the data collection process do we code? In the field After data collection
55 Coding What is coding? Categorizing responses Assigning numeric values to categories When in the data collection process do we code? In the field After data collection How do we code? Create set of exhaustive, mutually exclusive categories Assign responses to categories Add new categories, as needed
56 Practice Coding 1 Code the Gordon Brown responses as: Correct Incorrect Don t know 2 Code the MIP responses into issue categories
57 Data Editing Leftover of manual data recording Software handles most data editing now Online survey tools (e.g., Qualtrics) CATI systems May still have problematic data points that need to be marked or changed If still in field, may clarify answers with respondents
58 Anonymizing Data Why do data need to be anonymous?
59 Anonymizing Data Why do data need to be anonymous? Guarantees of anonymity Sensitive data
60 Anonymizing Data Why do data need to be anonymous? Guarantees of anonymity Sensitive data When are data non-anonymous?
61 Anonymizing Data Why do data need to be anonymous? Guarantees of anonymity Sensitive data When are data non-anonymous? Identifying information Statistical identifiability
62 Anonymizing Data Why do data need to be anonymous? Guarantees of anonymity Sensitive data When are data non-anonymous? Identifying information Statistical identifiability How do we anonymize?
63 Anonymizing Data Why do data need to be anonymous? Guarantees of anonymity Sensitive data When are data non-anonymous? Identifying information Statistical identifiability How do we anonymize? Restrict data access Remove identifying variables
64 Data Storage, Archiving, and Sharing In what formats can we store survey data?
65 Data Storage, Archiving, and Sharing In what formats can we store survey data? Paper Punchcards Digitally
66 Data Storage, Archiving, and Sharing In what formats can we store survey data? Paper Punchcards Digitally Considerations in digital formats Open versus proprietary Human-readable versus machine-readable File sizes Study-level metadata Question-level metadata
67 Study-level Metadata Title Creator/Author Sponsor Description Date of publication Dates of data collection Population, sampling frame, etc. Sampling design Sample size Recruitment details Mode Rights
68 Question-level Metadata Response codes Response labels Variable labels Variable names Missing data categories Variable types Mode
69 Question-level Metadata II Details of randomization or question order Exclusion criteria Source of data (if not from R) Frequencies or summary statistics Interviewer instructions Constraints on responses
70 Example Codebook 2 Question B 10 DK Which party did you vote for in that election? (Denmark) Variable name and label: prtvtcdk Party voted for in last national election, Denmark Values and categories 01 Socialdemokraterne - the Danish social democtrats 02 Det Radikale Venstre - Danish Social-Liberal Party 03 Det Konservative Folkeparti - Conservative 04 SF Socialistisk Folkeparti - Socialist People s Party 05 Dansk Folkeparti - Danish peoples party 06 Kristendemokraterne - Christian democtrats 07 Venstre, Danmarks Liberale Parti - Venstre 08 Liberal Alliance - Liberal Alliance 09 Enhedslisten - Unity List - The Red-Green Alliance 10 Andet - other 66 Not applicable 77 Refusal 88 Don t know 99 No answer Filter: If code 1 at B9 2 From:
71 File Formats Go to the course website Open data files under Week 11 All contain the same data What do you notice about the different files?
72 Metadata Standards Most survey data are stored in proprietary formats using codebooks constructed in arbitrary formats This makes it hard to work with survey data There are common standards for metadata Dublin Core (DC) Data Documentation Initiative (DDI)
73 For Your Project Discuss appropriate file format for data storage/sharing Discuss how data can be used after collection (i.e., rights) Discuss codebook creation When do you create a codebook What goes in your codebook Where do you record study-level metadata
74 Questions about handling survey data?
75 1 Weighting 2 Missing Data 3 Coding and Data Preparation 4 Wrap-up 5 Preview of Next Time
76 Total Survey Error Design-Related Errors Coverage Error Sampling Error Nonresponse Error Adjustment Error
77 Total Survey Error Design-Related Errors Coverage Error Sampling Error Nonresponse Error Adjustment Error Measurement Errors Construct Validity Measurement Error and Response Biases Processing Error
78 Total Survey Error Design-Related Errors Coverage Error Sampling Error Nonresponse Error Adjustment Error Measurement Errors Construct Validity Measurement Error and Response Biases Processing Error Our goal: Minimize total error (thus maximizing data quality), within the constraints of time, cost, and other resources
79 1 Weighting 2 Missing Data 3 Coding and Data Preparation 4 Wrap-up 5 Preview of Next Time
80 Agenda for next two classes Presentations Prepare questions to get help with me if you want to meet (after Dec. 4)
81
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