Statistical estimates bias for data with missing values: Nonresponse study: انحياز التقديرات الإحصائية للبيانات التي تحوي قيماً مفقودة: دراسة حالة عدم الاستجابة

Statistical surveys are usually conducted to obtain data describing a problem in a studied society, and many surveys experience a rise in nonresponse rates, as the rate of nonresponse may affect the bias of the nonresponse in survey estimates. Recent empirical results show instances of nonresponse rate correlation with nonresponse bias, we attempt to translate statistical experiences of nonresponse bias in newly published studies and research into causal models that lead to assumptions about when a lack of response causes bias in estimates. Research studies of the estimates of nonresponse bias show that this bias often exists. The logical question is: what is the advantage of surveys if they suffer from high rates of nonresponse, since post-survey adjustments for nonresponse require additional variables, the answer depends on the nature of the design and the quality of the additional variables.

The impact of nonresponse in different survey stages on the precision of prevalence estimates for multi-stage survey studies

Objective While it is known that nonresponse might produce biased results and impair the precision of results in survey research studies, the pattern of the impact on the precision of estimates due to the nonresponse in different survey stages is historically overlooked. Having this type of information is essential when creating recruitment plans. This study proposes to examine and compare the effect of nonresponse in different stages on the precision of prevalence estimates in multi-stage survey studies. Based on data from a state level survey, a simulation approach was used to generate datasets with different nonresponse rates in three stages. The margin of error was then compared between the datasets with nonresponse at three different survey stages for 12 outcomes. Results At the same nonresponse rate, the mean margin of error was greater for the data with nonresponse at higher stages. Additionally, as the nonresponse rate increased, precision was more inflated within the data with higher stage nonresponse. This suggests that the effort used to recruit the primary sampling units is more crucial to improve the precision of estimates in multi-stage survey studies.

The Impact of Nonresponse in Different Survey Phases on the Precision of Prevalence Estimates

Objective Survey research is widely used in social studies. Whereas it has been widely known that nonresponse might produce biased results and impair the precision, the pattern of the impact on the precision of the estimate due to the non-response in the different survey stages is historically overlooked, though such information is essential to guide the recruitment plan. This study proposed to examine and compare the effect of first and second level nonresponse on the precision of prevalence estimates in the multi-stage survey studies. Based on the benchmark dataset from a state level survey, we used simulation approach to create datasets with different first and second level nonresponse rates and then compare the margin of error (an indicator for the precision) for the 12 outcomes between datasets with first vs. second level nonresponse. Results At the same nonresponse rate, the mean margin of error was greater for the data with first level nonresponse, compared to the data with second level nonresponse. As the nonresponse rate increased, the loss of precision was more inflated with the data with first level nonresponse, suggesting that the effort for recruiting primary sampling units is more crucial to improve the estimate precision in survey studies.

No-respuesta al ítem en una encuesta de hogares en Colombia

Item nonresponse occurs when sample units do not provide information on a particular variable, problem that may affect the representativeness of the sample and the reliability of the estimates. Efforts to reduce the item nonresponse rate do not necessarily improve the quality of the information. Besides the nonresponse rate, representativeness indicators can be used to measure the quality of the collected data. This paper analyzes the wage nonresponse mechanism of a household survey in Colombia and evaluates the quality of the wage information in two different periods of time (2008:4 and 2017:4). The results show a low but increasing nonresponse rate whose behavior does not seem to be associated with the set of observables considered. This result is of value since the selection of the adequate imputation method relies on the assumptions on the missing data mechanism.

Misreporting Among Reluctant Respondents

Many surveys aim to achieve high response rates to keep bias due to nonresponse low. However, research has shown that the relationship between the nonresponse rate and nonresponse bias is small. In fact, high response rates may lead to measurement error, if respondents with low response propensities provide survey responses of low quality. In this paper, we explore the relationship between response propensity and measurement error, specifically, motivated misreporting, the tendency to give inaccurate answers to speed through an interview. Using data from four surveys conducted in several countries and modes, we analyze whether motivated misreporting is worse among those respondents who were the least likely to respond to the survey. Contrary to the prediction of our theoretical model, we find only limited evidence that reluctant respondents are more likely to misreport.

Ethnicity and Occupational Differences in the Transforming Homogenous Culture: The Case of Lithuania

This article presents empiric research on respondents’ self-identification of ethnicity in such culturally homogenous country as Lithuania and comparisons among three occupational sectors: education, healthcare and business. The results reveal the difficulty when attempting to self-identity ethnicity resulting in high nonresponse rate. Furthermore, results indicate effects of occupational background of the respondent and influence of EU membership on the perceived ethnicity. The contextualisation of results within social and historical context of the country is outlined, as well as methodological implications.

Don’t Know or Won’t Say? Exploring How Colorblind Norms Shape Item Nonresponse in Social Surveys

Colorblind norms play an important role in shaping how people discuss race. There is reason to believe that these norms also affect the ways respondents interact with social surveys. Specifically, some respondents may be using nonresponse as a tactic to not discuss race in social surveys. If this is the case, very different demographics of respondents would be most prone to nonresponse, and the phenomenon should also vary on the basis of the interviewer’s race. The author conducted bivariate and multivariate analysis of the Chicago Area Study to examine whether colorblindness may be promoting “don’t know” responses and item refusals. The author finds that nonresponse to a perceived race of interviewer item follows a distinct pattern consistent with previous research on colorblind norms. For example, white respondents have nearly five times the rate of nonresponse compared with blacks and Latinos. Bolstering the colorblindness theory, an interracial interview context nearly triples the nonresponse rate compared with same-race interviews. Findings of this research have important implications for both survey researchers using social surveys to examine race and racial attitudes and race scholars who seek to understand the prevalence of colorblind norms across society.