How Interviewer Characteristics Differ Household Surveys?: An Analysis in 2013 Turkey Demographic and Health Survey Melike Sarac (melikesarac@hacettepe.edu.tr) and A. Sinan Turkyilmaz (aturkyil@hacettepe.edu.tr) Hacettepe University, Institute of Population Studies, Ankara, Turkey Abstract Information on data quality indicators such as non-response rates and sampling errors have been presented in Turkey Demographic and Health Surveys (TDHS) since 1993 as household sample surveys. Apart from these quality indicators, tables on data quality indicators regarding with the household population, eligible and interviewed women, missing information, and birth and death information are presented on main reports of TDHSs. However, lack of information about interviewer characteristics which is known as a major error source in terms of survey quality still exists. This study mainly emphasizes on interviewer characteristics covered in a special data set, called “ Data Collection Staff Data Set” . Descriptive results which summarize recruitment process of TDHS-2013 and a multivariate analysis by the Poisson regression model that provides results on number of completed household interviews consist of main components of this study. An analysis on characteristics of interviewers who were employed in TDHS- 2013 fieldwork demonstrated significant effect on main component of the response rate: number of completed household interviews. As it is seen, number of completed interviews is assumed as an indicator that reflects the interviewer performance for this study in terms of response rate. Demographic characteristics of interviewers such as age and education have been interested in studies analyze interviewer effect on survey responses (Williams, 1964; Berk and Bernstein, 1988; Wilson and Olesen, 2002). Similarly, experience of interviewers has been discussed comprehensively within achieving higher response rates (Durbin and Stuart, 1951; Groves and Couper, 1998; Sala et. al., 2012). Our findings put forward that age, place of birth, experience and education of interviewers have an effect on number of completed household interviews under the control of other covariates. 1
Introduction and Objectives Main objective of a good survey design is to maximize survey quality under the survey constraints. This objective should be taken into account in all survey stages in order to maximize survey quality. Understanding the survey process and considering the total survey error within the sampling and non-sampling errors are crucial in order to evaluate survey quality that is determinable with accuracy, timeliness, accessibility, and completeness. Controlling non-sampling errors, which are not easy to estimate as much as sampling errors, is possible with accurate planning and careful survey design, interactive with knowledge, experiences and theories of many disciplines (Biemer and Lyberg, 2003). Therefore, error types which creates non-sampling error such as measurement error and coverage error should be tried to keep in a minimum level during the survey process. Interviewer who plays a critical role on survey estimates has an effect on respondents and responses directly whereas variance is smaller due to assistance provided by an interviewer. This effect can be considered within the non-sampling error considering the non-response behavior and social desirability. As Korbmacher (2014) mentioned in his study, interviewer has an impact on respondents’ willingness to participate. Initially, contact process with the sample unit and after that persuading respondent to survey participation and maintaining motivation of respondents during the interview are quite substantial issues in terms of gaining cooperation, having an interview with the respondent, and data quality. At the same time, interviewing approach that is provided by an interviewer affects data quality. Some researchers believe that questions to the respondent by interviewer should be asked very standardized manner whereas some researchers believe that the interviewing process is more interactive and flexible (Fowler and Mangione, 1990; Suchman and Jordan, 1990). It should be noted that, not only interviewing techniques but also demographic and other characteristics of interviewers have an influence on response behavior of respondents and survey estimates. This study mainly focuses on interviewer recruitment process of the TDHS-2013 based on the steps and interviewer s’ demographic and other characteristics within the context of completed household interviews which can be considered as a reflection of completion and response rates. In this regard, the main research question of the study is determined as: “Are there any impact of data collection staff characteristic s on 2
number of completed household interviews in TDHS- 2013?”. In this study, household interviews will be examined rather than women interviews due to the fact that the cooperation with respondent has been already taken in women interview thanks to the household interview. Data Source and Methodology In order to analyze the data collection staff characteristics and interview results, two different data sources were used. The first data set is TDHS-2013 Household Data Set, which is a nationally representative data on demographic and socio-economic characteristics of households in Turkey. And, secondly, a specially constructed data set, namely TDHS-2013 Data Collection Staff Data Set , constructed by aggregating ‘application’, ‘interview’, and ‘fieldwork preference’ forms filled by applicants involved in TDHS-2013 recruitment process. A Poisson regression model which is convenient for the number of completed household interviews, as a count variable, was used in order to identify and measure effect of interviewer characteristics on number of completed household interviews as well as estimable field characteristics. Average time of a household interview based on interviewer and number of days which spent for household interviews can be considered within the varying field characteristics depending on the interviewers. The dependent variable, total number of completed interviews for each interviewer, seems to be convenient for Poisson regression analysis considering the non-negative integers. The model for the analysis on total number of completed interviews is based on the Poisson distribution as the following: Pr(𝑍 = 𝑍|µ) = 𝑓 −µ µ 𝑧 , 𝑧 = 0,1,2, … 𝑧! where µ is the risk of a new occurrence of the interest during a spe cified time interval that can be assumed data collection process (in days) for this study. Additionally, descriptive results that focus on the recruitment process and household level completion and response rates will be presented based on the TDHS-2013 Household Data Set using the interview results . 3
𝐷 𝐼𝑝𝑣𝑡𝑓ℎ𝑝𝑚𝑒 𝑆𝑓𝑡𝑞𝑝𝑜𝑡𝑓 𝑆𝑏𝑢𝑓 = 𝐷 + 𝐼𝑄 + 𝑄 + 𝑆 + 𝐸𝑂𝐺 + 𝑄𝐷 𝐷 𝐼𝑝𝑣𝑡𝑓ℎ𝑝𝑚𝑒 𝐷𝑝𝑛𝑞𝑚𝑓𝑢𝑗𝑝𝑜 𝑆𝑏𝑢𝑓 = 𝐷 + 𝐼𝑄 + 𝐼𝐵 + 𝑄 + 𝑆 + 𝐸𝑊 + 𝐸𝐸 + 𝐸𝑂𝐺 + 𝑄𝐷 + 𝑃 where categories “completed” (C), “no household member/no competent member at home” (HP), “entire household absent for extended period of time” (HA), “postponed” (P), “refused” (R), “dwelling vacant or address not a dwelling” (DV), “dwelling destroyed” (DD), “dwelling not found” (DNF), “partially completed” (PC), and “other” (O). Firstly, recruitment process of the TDHS-2013 was reviewed and after that the impact of the interviewer characteristics such as age, place of birth, educational status, working status, interested in social science, survey experience, language abilities and field characteristics such as having an interview in metropolitan cities of Turkey, mean number of household members, and average time of a household interview were examined in Poisson regression model. Findings Recruitment steps and number of candidates who take part in each step are demonstrated in Table 1. As it is seen in Table 1 multi-stage recruitment process was conducted in TDHS-2013 recruitment process. As we have mentioned previously, interviewer specific completion / response rates, which is calculated with the total number of completed interviews, can be considered as one of the performance indicators of data collection staff. In total, interviews with 11749 out of 14489 selected households were completed. Completion rates should be interpreted carefully as they are affected by several factors such as operational difficulties, and logistic problems depending on field work regions. Therefore, for each team, tables associated with completion rates are presented along with their field work provinces (see Table 2). Furthermore, findings put forward that age, place of birth (5 regions of Turkey), educational status, survey experience, and average time of a household interview based on data collection staff have an impact on the incident rate of “completed household interviews” under the control of other variables by looking at the incidence ratios (Table 3). Interestingly, considering survey experience, staff who has never 4
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