Detroit Metro Area Communities Study (DMACS) Wave 3, Michigan, 2018 (ICPSR 37687)

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Sample View help for Sample

The target population for this study was the adult household population of the City of Detroit. The sample had two components:

• The 714 respondents to the first wave of the DMACS survey: The initial sample in this case was a simple address-based sample. In 2016 a simple random sample of addresses was drawn from a list of all household addresses in the City. A total of 3100 addresses were selected from FIPS 26/22000, City of Detroit. The sample file contained information including the tract number, block and block group, and whether the address was a single or multiple family structure. The sample provider also matched each address to the likely name and phone number of a resident when such information was available. Approximately 90% of the sample lines came with a possible name match and 68% with a possible telephone number match. All individuals who responded to the Wave 1 survey were invited to participate in the Wave 3 survey.
• A new stratified two-stage cluster sample of household addresses: For the first stage of sampling, U.S. block groups were selected with probability proportionate to estimated size within two strata. The first stratum included block groups where 70% or more of the population is Hispanic/Latino according to ACS estimates; eight block groups were selected within this stratum. The second stratum included all other block groups within the City of Detroit; 41 block groups were selected within this stratum. The second stage of sampling consisted of selecting household addresses from a list of all households in the block group. A total of 3,852 addresses were selected at this stage.

Time Method View help for Time Method

Universe View help for Universe

Adult household population of the City of Detroit.

Unit(s) of Observation View help for Unit(s) of Observation

Data Type(s) View help for Data Type(s)

Mode of Data Collection View help for Mode of Data Collection

Response Rates View help for Response Rates

The response rate to the Wave 3 survey was 32.2%, calculated using AAPOR Response Rate 3.

Presence of Common Scales View help for Presence of Common Scales

Several Likert-type scales were used.

Original Release Date View help for Original Release Date

2020-07-07

Weight View help for Weight

Statistical weighting to control for the impacts of the sample design and non-response was performed in three stages:

• Design weight: A design weight of 1 was assigned to existing panel members, who were drawn from a simple random sample of the City. For newly-sampled households, respondents were assigned a design weight that was equal to the inverse of the selection probability (the number of households in the city as estimated by the Census, divided by the number of sampled households in the block group). This was then divided by a constant to adjust the scale of the weights to a mean of 1.
• Non-response weight: Non-response weights were calculated separately for the returning panelists and the new Wave 3 sample members. For the returning panelists, steps in generating this weight included:
• Factor analysis on 13 block-group variables from the 2011-2015 American Community Survey to reduce the number of potential predictors;
• Multiple imputation by chained equations to impute 25 datasets with complete W1 data for all respondents;
• Examination of the bivariate relationships between Wave 3 response and potential predictors, including ACS data, Wave 1 responses, and paradata from Waves 1 and 3;
• Running a response propensity model on all 25 imputed datsets. This model was an unweighted logit model using limited set of predictors (those where p <.1 in the bivariate relationship to W3 response).
• Smoothing the weights generated by creating quintile groupings of the inverse of the predicted probability of response

For the new Wave 3 sample members, the process was very similar; though the potential predictors available were fewer, and without prior data, the multiple imputation phase was not necessary. In addition,

• Factor analysis was conducted on 15 block-group variables from the 2012-2016 American Community Survey rather than the 13 from the earlier wave of the ACS that were attached to Wave 1;
• The selected response propensity model was a weighted logit model that included ACS factor scores as predictors and random effects for block group. This model was selected as the preferred model because (a) it includes the design weights for selecting the new wave 3 sample, (b) the random effects for block groups (our primary sampling units) were significant, and (c) it produced the least amount of variance in predicted probabilities of the potential models tested.
• Post-stratification weight: after multiplying the design weight by the non-response weight, an additional post-stratification weight was developed to calibrate the demographic distribution of respondents to the target population of the City of Detroit. We first used multiple imputation to create ten datasets that were complete for all respondents for the variables used in raking. In order to preserve the correlations between these variables and other survey outcomes, a larger set of variables was imputed, including income, length of residence at current address and length of residence in Detroit, number of places R has lived in last five years, home ownership, whether R ever experienced homelessness, marital status, internet access at home, neighborhood satisfaction, views on community assets (Q6a-k), views on priorities to improve public health (Q7a-q), fear of crime, support from social networks, whether Rs neighborhood has name, primary source of health care, insurance status, affiliation with community associations, ability to pay for current care or health emergency, attendance of religious services, and political ideology. The predictors for these imputation models included ACS factor scores (see step 3a) and other wave 3 variables for which there were no missing data. This weight was developed with an iterative proportional fitting (raking) procedure (using the "ipfraking" package in Stata 15) and includes adjustments for age, gender, race, Hispanic ethnicity, and education to match the American Community Survey (ACS) 2012-2016 estimates for the population 18 and older in the City of Detroit. Weights were trimmed to a maximum value of 4.