Underestimating busyness: Indications of nonresponse bias due to work-family conflict and time pressure

International research shows that both work-family conflict and time pressure are increasing in Western societies. With these increased pressures, precisely those people who are combining work and family obligations may be missing from survey estimates. This study investigates whether the measurement of work-family conflict (WFC) and time pressure is influenced by nonresponse bias. Using the Flemish “Work, Family and Time use in Flanders”-survey and the Basic Question survey for nonrespondents of this survey, we investigate whether nonrespondents score higher on subjective and objective indicators of WFC and time pressure than respondents. Results show that nonrespondents are indeed those sample units who experience significantly more WFC and time pressure, demonstrating that WFC and time pressure related nonresponse biases exist. Implications and suggestions to reduce bias in data collections are discussed.     

Can Weighting Compensate for Nonresponse Bias in a Dependent Variable? An Evaluation of Weighting Methods to Correct for Substantive Bias in a Mail Survey among Dutch Municipalities

Due to a lack of pertinent data, little is known about nonresponse in substantive, generally “dependent” variables and its consequences. However, in a study on policy performance of Dutch municipalities, we were fortunately able to gather performance data for respondents and nonrespondents from independent sources. Our study shows that very successful and very unsuccessful municipalities respond least, while intermediately successful municipalities respond most. In our opinion, this “curvilinear” relationship between performance and nonresponse can be interpreted as an interaction effect between interest in the topic of research and evaluation apprehension. As a consequence, the relationships between variables are also biased. To correct for this bias we made several weighting attempts. Weighting adjustment is based on the assumption of equal response probabilities. Our study, however, shows that this assumption is not met. Furthermore, our results indicate that weighting by one or two independent variables does not lead to better results, no more than weighting by policy performance alone. Interestingly, only weighting by a dependent and independent variable in combination leads to acceptable results, but only if weighting factors are not trimmed down too strongly and combination of weighting classes is limited.     

Should we trust survey data? Assessing response simplification and data fabrication

While many factors, such as unit- and item nonresponse, threaten data quality, we focus on data contamination that arises primarily from task simplification processes. We argue that such processes can occur at two levels. First, respondents themselves may engage in various response strategies that minimize their time and effort in completing the survey. Second, interviewers and other employees of the research institute might take various shortcuts to reduce their time and/or to fulfill the requirements of their contracts; in the simplest form this can be done via copy-and-paste procedures.This paper examines the cross-national quality of the reports from principals of schools participating in the 2009 PISA. We introduce two measures of data quality to document that extreme response simplification characterizes the behavior of substantial numbers of school principals in numerous countries. Additionally, we discovered strong evidence of data fabrication in several countries.     

Latent class analysis of response inconsistencies across modes of data collection

Latent class analysis (LCA) has been hailed as a promising technique for studying measurement errors in surveys, because the models produce estimates of the error rates associated with a given question. Still, the issue arises as to how accurate these error estimates are and under what circumstances they can be relied on. Skeptics argue that latent class models can understate the true error rates and at least one paper (Kreuter et al., 2008) demonstrates such underestimation empirically. We applied latent class models to data from two waves of the National Survey of Family Growth (NSFG), focusing on a pair of similar items about abortion that are administered under different modes of data collection. The first item is administered by computer-assisted personal interviewing (CAPI); the second, by audio computer-assisted self-interviewing (ACASI). Evidence shows that abortions are underreported in the NSFG and the conventional wisdom is that ACASI item yields fewer false negatives than the CAPI item. To evaluate these items, we made assumptions about the error rates within various subgroups of the population; these assumptions were needed to achieve an identifiable LCA model. Because there are external data available on the actual prevalence of abortion (by subgroup), we were able to form subgroups for which the identifying restrictions were likely to be (approximately) met and other subgroups for which the assumptions were likely to be violated. We also ran more complex models that took potential heterogeneity within subgroups into account. Most of the models yielded implausibly low error rates, supporting the argument that, under specific conditions, LCA models underestimate the error rates.     

The effect of sociodemographic (mis)match between interviewers and respondents on unit and item nonresponse in Belgium

Interviewer characteristics affect nonresponse and measurement errors in face-to-face surveys. Some studies have shown that mismatched sociodemographic characteristics – for example gender – affect people's behavior when interacting with an interviewer at the door and during the survey interview, resulting in more nonresponse. We investigate the effect of sociodemographic (mis)matching on nonresponse in two successive rounds of the European Social Survey in Belgium. As such, we replicate the analyses of the effect of (mis)matching gender and age on unit nonresponse on the one hand, and of gender, age and education level (mis)matching on item nonresponse on the other hand. Recurring effects of sociodemographic (mis)match are found for both unit and item nonresponse.     

Heritability,family, school and academic achievement in adolescence

We demonstrate how genetically informed designs can be applied to administrative exam data to study academic achievement. ACE mixture latent class models have been used with Year 6 and 9 exam data for seven cohorts of Polish students which include 24,285 pairs of twins. Depending on a learning domain and classroom environment history, from 58% to 88% of variance in exam results is attributable to heritability, up to 34% to shared environment and from 8% to 15% depends on unique events in students’ lives. Moreover, between 54% and 66% of variance in students’ learning gains made between Years 6 and 9 is explained by heritability. The unique environment accounts for between 34% and 46% of that variance. However, we find no classroom effects on student progress made between Years 6 and 9. We situate this finding against the view that classroom peer groups and teachers matter for adolescent learning.     

A multivariate analysis of response structure: Race attitudes, 1972–1977

A particular kind of latent class model is used to characterize the unobservable variable measured by six discrete indicators of racial stance in 1972 and 1977. Methods recently introduced by Clogg and Goodman, 1982, Clogg and Goodman, 1983) for the simultaneous latent structure analysis of two multidimensional contingency tables are employed in across-year homogeneity tests on the latent class proportions. Trends in multivariate response patterns over the 5-year interval are then examined by cross-classifying the predicted latent variable with selected demographic characteristics of respondents in each year. The results indicate that (1) no significant changes in the distribution of the latent variable occurred over the 5-year time period, and (2) the status of certain demographic variables as predictors of racial stance fluctuated between the two survey years.     

The rescission of behaviors: Inconsistent responses in adolescent sexuality data

In longitudinal survey research, certain questions can be rescinded illogically. For instance, respondents who at Time 1 report having had sexual intercourse may at Time 2 report never having done so. This paper reports measurement techniques and analyses of these types of inconsistencies from an ongoing longitudinal adolescent sexuality project. Inconsistencies in intercourse, masturbation, and other sexual behaviors are reported and compared to rates from other studies and other less sensitive behaviors within the same study. Three conclusions are presented: (1) inconsistencies should be considered a natural part of any longitudinal survey process and should be incorporated into the response model; (2) inconsistency rates in these particular data support the contention that adolescent sexuality data of appropriate quality for analytical purposes can be obtained; and (3) inconsistency rates in fact contain substantive information concerning the processes under consideration.     

Are we keeping the people who used to stay? Changes in correlates of panel survey attrition over time

As survey response rates decline, correlates of survey participation may also be changing. Panel studies provide an opportunity to study a rich set of correlates of panel attrition over time. We look at changes in attrition rates in the American National Election Studies from 1964 to 2004, a repeated panel survey with a two-wave pre-post election design implemented over multiple decades. We examine changes in attrition rates by three groups of variables: sociodemographic and ecological characteristics of the respondent and household, party affiliation and political and social attitudes recorded at the first interview, and paradata about the first wave interview. We find relatively little overall change in the pre-post election panel attrition rates, but important changes in demographic correlates of panel attrition over time. We also examine contact and cooperation rates from 1988 to 2004.     

Investigating a typology of trust orientations towards national and European institutions: A person-centered approach

There is a growing recognition that trust attitudes towards national and European institutions are systematically related. Despite substantial research interest, the exact nature of this relationship remains unclear. We argue that a person-centered approach with its focus on identifying groups of individuals with similar attitudinal profiles provides a valuable complement to the currently standard variable-oriented approach in addressing this challenge. To illustrate, we examine the extent to which a prior typology of diffuse support for the European Union (EU) would emerge from a multilevel latent class analysis of the Eurobarometer data (2014). Our results provide support for the investigated typology by identifying four distinct trust profiles at the individual level that coalesce into separate latent clusters at the country level. Our findings underscore the value of a person-centered approach for the study of EU support.     

Understanding the resilience of mail-back survey methods: An analysis of 20 years of change in response rates to national park surveys

Coverage and response rate challenges facing telephone and internet surveys have encouraged scientists to reconsider mail data collection methods. Although response rates to telephone surveys have declined sharply in the last 20 years, it is unclear how response rates to mail have fared during this time. This study analyzes 179 mail-back surveys of visitors to US National Parks from 1988 to 2007, which used virtually the same administration procedures throughout the period. Results show that response rates, based on only those who initially agreed to return a questionnaire, have remained at a high level with a 76% average while the number of questions and pages steadily increased. Despite this rise in response burden, rates have declined only moderately from about 80% in the late 1980s to about 70% more recently. The roles of additional contacts and survey salience in maintaining high response rates are examined. Results suggest that mail-back surveys for obtaining information from quasi-general public populations remain an effective data collection procedure.     

New options for national population surveys: The implications of internet and smartphone coverage

Challenges to survey data collection have increased the costs of social research via face-to-face surveys so much that it may become extremely difficult for social scientists to continue using these methods. A key drawback to less expensive Internet-based alternatives is the threat of biased results from coverage errors in survey data. The rise of Internet-enabled smartphones presents an opportunity to re-examine the issue of Internet coverage for surveys and its implications for coverage bias. Two questions (on Internet access and smartphone ownership) were added to the National Survey of Family Growth (NSFG), a U.S. national probability survey of women and men age 15–44, using a continuous sample design. We examine 16 quarters (4 years) of data, from September 2012 to August 2016.Overall, we estimate that 82.9% of the target NSFG population has Internet access, and 81.6% has a smartphone. Combined, this means that about 90.7% of U.S. residents age 15–44 have Internet access, via either traditional devices or a smartphone. We find some evidence of compensatory coverage when looking at key race/ethnicity and age subgroups. For instance, while Black teens (15–18) have the lowest estimated rate of Internet access (81.9%) and the lowest rate of smartphone usage (72.6%), an estimated 88.0% of this subgroup has some form of Internet access.We also examine the socio-demographic correlates of Internet and smartphone coverage, separately and combined, as indicators of technology access in this population. In addition, we look at the effect of differential coverage on key estimates produced by the NSFG, related to fertility, family formation, and sexual activity. While this does not address nonresponse or measurement biases that may differ for alternative modes, our paper has implications for possible coverage biases that may arise when switching to a Web-based mode of data collection, either for follow-up surveys or to replace the main face-to-face data collection.     

Familial relations and labor market outcomes: the Palestinian refugees in Lebanon

We analyze intra-family support among Palestinians living in Lebanon, using detailed household survey data from the refugee camps and Palestinian communities in Lebanon and latent class analysis technique. The study uncovers five latent classes of familial exchange. Two different theoretical models—the solidarity and reciprocal exchange—are used to examine the link between patterns of assistance and labor market conditions. Contrary to the solidarity account, we find no evidence of high familial assistance among the unemployed, or those excluded from the labor market, and this is true regardless of type of exchange. However, the camp population is more likely to engage in exchange relations than their non-camp counterparts. The married, loners, educated, employed, and those with stronger ties to relatives are more likely to be money exchangers. The findings are similar for care receivers, but age and gender figure prominently here. Some policy implications of the findings are discussed.     

Heterogeneity in the rise and decline of city-level homicide rates, 1976-2005: A latent trajectory analysis

This study applies latent trajectory methods to the analysis of temporal changes in homicide rates among large US cities across recent decades. Specifically, annual homicide rates for 157 large US cities are analyzed for the 30 years from 1976 to 2005. We address the fundamental questions: Did all of cities experience similar levels and patterns of rise and decline in homicide rates over these three decades? Or is there hidden or unobserved heterogeneity with respect to these temporal patterns, thus leading to the identification of more homogeneous groupings of the cities? And if latent homogeneous groupings surface, is membership due to specific structural characteristics found within those cities? Evidence is found for the existence of four latent homicide rate trajectories. After identifying and classifying the cities into these four groups, multivariate statistical techniques are used to determine which social and economic characteristics are significant predictors of these distinct homicide trends. Criminal justice measures are also included as controls. It is found that larger cities located in the South with higher levels of resource deprivation/concentrated poverty, higher income inequality, higher percentages of the adult male population that are divorced, higher unemployment rates, higher percentages of youth, higher percentages of the population who are Hispanic and higher numbers of police per capita are more likely to be in a higher than a lower homicide trajectory group. Higher percentages of the population enrolled in colleges and universities and locations in states with higher incarceration rates are characteristics of cities associated with membership in a lower homicide trajectory group.     

A Sort of Homecoming: Incarceration and the housing security of urban men

While individuals returning from prison face many barriers to successful re-entry, among the most serious are the challenges they face in securing housing. Housing has long been recognized as a prerequisite for stable employment, access to social services, and other aspects of individual and family functioning. The formerly incarcerated face several administrative and de facto restrictions on their housing options; however, little is known about the unique instabilities that they face. We use a longitudinal survey of urban families to examine housing insecurity among nearly 3000 urban men, including over 1000 with incarceration histories. We find that men recently incarcerated face greater housing insecurity, including both serious hardships such as homelessness, and precursors to homelessness such as residential turnover and relying on others for housing expenses. Their increased risk is tied both to diminished annual earnings and other factors, including, potentially, evictions from public housing supported by Federal “one-strike” policies.     

The role of administrative data in the big data revolution in social science research

The term big data is currently a buzzword in social science, however its precise meaning is ambiguous. In this paper we focus on administrative data which is a distinctive form of big data. Exciting new opportunities for social science research will be afforded by new administrative data resources, but these are currently under appreciated by the research community. The central aim of this paper is to discuss the challenges associated with administrative data. We emphasise that it is critical for researchers to carefully consider how administrative data has been produced. We conclude that administrative datasets have the potential to contribute to the development of high-quality and impactful social science research, and should not be overlooked in the emerging field of big data.     

《大学》行政伦理思想及其现代价值

《大学》的行政伦理思想具体体现在“三纲、八目”之中。其核心是强调统治者“修身”在“治国、平天下”过程中的重要性。撇开其历史与阶级的局限性，这一思想对于我们现阶段在行政伦理建设中如何加强为政者的道德修养是有益的。《大学》的“知止”观对我们的党风廉政建设也有着重要的启示和借鉴意义。     

Risk factors and outcomes of chronic sexual harassment during the transition to college: Examination of a two-part growth mixture model

A two-part latent growth mixture model was implemented in order to examine heterogeneity in the growth of sexual harassment (SH) victimization in college and university students, and the extent to which SH class membership explains substance use and mental health outcomes for certain groups of students. Demographic risk factors, mental health, and substance use were examined as they related to chronically experienced SH victimization. Incoming freshmen students (N = 2855; 58% female; 54% White) completed a survey at five time points. In addition to self-reporting gender, race, and sexual orientation, students completed measures of sexual harassment, anxiety, depression, binge drinking, and marijuana use. Overall, self-reported SH declined upon college entry, although levels rebounded by the third year of college. Results also supported a two-class solution (Infrequent and Chronic) for SH victimization. Being female, White, and a sexual minority were linked to being classified into the Chronic (relative to the Infrequent) SH class. In turn, Chronic SH class membership predicted greater anxiety, depression, and substance use, supporting a mediational model.     

Measuring the level of social support using latent class analysis

Different instruments have been used to measure social support in epidemiological studies of which the most widely used is the Medical Outcomes Study Social Support Scale (SSS-MOS). However, these studies lack measures of the level of social support on health risks. We used latent class analysis (LCA) to distinguish subgroups with different levels of perceived social support and tested the consistency of these subgroups by their associations with the prevalence of Common Mental Disorders (CMD). This is a cross-sectional study of 1013 mothers living in the city of Salvador, Brazil in which psychosocial data were collected through home visits using the SSS-MOS and the Self Reporting Questionnaire-20. For each dimension of social support analysed here, we selected models with two classes using LCA. Multivariate logistic regression models were used to estimate the association between participants’ perceived social support and the prevalence of CMD to verify the consistency of the groups defined by LCA. There was a clear difference in the reporting of perceived social support between those classified as high or low using LCA. The probability of perceiving several types of social support was lower in the subgroup classified as low level of social support (13.7–59.8%), and it was much higher in the group classified as high level of social support (84.3–98%). A greater prevalence of CMD was found among mothers with lower levels of social support. LCA seems to be a useful tool to improve measurement of perceived social support by separation into two levels in which the lower level is associated with an increased prevalence of CMD.     

Structural stability,quantitative change: A latent class analysis approach towards gender role attitudes in Germany

Since the 1960s, social science surveys have aimed to assess respondents' attitudes towards gender roles. In this paper, a model-based clustering approach towards gender role attitudes is proposed in contrast to commonly used dimensional methods. Working from a role theory perspective, we expect different profiles in the population when it comes to role expectations. Using data from the German General Social Survey in 1991 and 2012, we identify attitude patterns via multi-group latent class analysis, taking differences between Eastern and Western Germany into account. Five latent classes representing different combinations of role expectations towards couples and parents and varying levels of intensity are distinguished. Attitude change is assessed by comparing latent class prevalence over time, while statistically testing for measurement equivalence. The analysis reveals a regionally variable decrease in traditional role models: Eastern Germany exhibits a faster pace of ‘de-traditionalization’ and less potential for role conflicts regarding working mothers.