Does panel conditioning affect data quality in ego-centered social network questions?

Silber, Henning; Schröder, Jette; Struminskaya, Bella; Stocké, Volker; Bošnjak, Michael

doi:10.1016/j.socnet.2018.08.003

Cited by 16 publications

(14 citation statements)

References 42 publications

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“…Other methodological issues also affect the probability that a t1 alter's absence at t2 is accurate: the instrument--how exhaustive the set of name-eliciting questions are, how many names it accepts, and how probing the interview is (Brewer 2000;Fischer and Bayham, 2019); the inclusion of what may have be fleeting ties (Desmond 2012;Small, 2017;Torres 2019); 2 selective attrition in the respondent sample; and possible panel conditioning (Silber et al, 2019). But the process respondents use to, in effect, "sample" the alters in their networks seems fundamental to the process and implies that real culling of alters-and thus the real level of "churn"--may be significantly less common than existing results imply.…”

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confidence: 99%

Who is dropped and why? Methodological and substantive accounts for network loss

Fischer

Offer

2020

Social Networks

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High rates of egocentric network turnover are frequently observed but not well explained. About 1,000 respondents to the UCNets survey named an average of 10 names in each of two waves a year apart. Consistent with prior studies, respondents in wave 2 failed to relist about half of the names they provided in wave 1. Asked why, respondents explained that they had forgotten the alter for about 40 percent of the missing names. Other common answers, such as no "occasion... to be in touch," also suggest that the true rate of alters being dropped is probably under 20 percent. Multilevel logit models identified the predictors of alters being dropped (neither relisted nor forgotten) versus retained. Immediate kin were likeliest to be retained and roommates, coworkers, and acquaintances to be dropped. Alters who provided companionship, confiding, advice, and emergency help were especially likely to be retained, as were those to whom respondents felt close. Little about the respondents themselves affected drop rates: having moved recently, having a close friend die, or having had an important relationship break up. Results are consistent with the argument that a tie's degree of constraint (notably being close family) and its balance of rewards determine the likelihood of it being dropped or demoted. Who is Dropped and Why? Methodological and Substantive Accounts for Network Loss Panel studies of egocentric networks commonly report a paradox: Respondents report much turnover among their alters-typically, about half of those named are not renamed-and yet respondents' repopulated networks display notable constancy in traits such as size, composition, supportiveness, and structure (see review below). Our purpose here is to expand our understanding of this phenomena by focusing on the first part of this process: whom respondents drop. We use two waves of UCNets, a survey that administered many name-eliciting questions to about 1,000 adult respondents. Methodologically, we found that (a) many apparently dropped alters were not really dropped, but were simply forgotten; (b) many alters whom respondents failed to re-list nonetheless remained "dormant" and available to the respondents; and (c) respondents positively dropped or demoted alters for several distinct reasons. Substantively, we found that (a) alters who were really dropped-neither re-listed nor simply forgotten-tended to be: extended kin, coworkers, or acquaintances; alters who had not been named as providing key support; and alters who were considered "difficult" (unless they were immediate kin). (b) Respondents who dropped many of their alters were particularly likely to have moved outside the region between waves. These findings suggest, methodologically, that the "true" rate of dropped alters in an adult population is nearer to 20 percent than the conventional 50 percent (depending on how the analyst classifies dropped alters who seemed to have been moved toward the periphery of ego's network). Theoretically, the findings suggest that the likelihood of being dropped ...

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confidence: 99%

Who is dropped and why? Methodological and substantive accounts for network loss

Fischer

Offer

2020

Social Networks

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“…If, at the sample level, there is a randomly distributed but consistent decline in the number of alters nominated between waves, this would suggest that the respondents are engaging in motivated underreporting. If, on the other hand, only those with very large networks underreport alter nominations in subsequent waves, this would suggest anchoring is a more plausible explanation, akin to the regression to the mean explanation for such results suggested by Silber et al (2019).…”

Section: Research Question 3 To What Extent To Do We Observe Systemamentioning

confidence: 98%

“…Instrument bias refers to the way in which the nature of the data collection task can suggest or nudge individuals into withholding important data or including unnecessarily or extraneous details. Panel conditioning refers to changes in responses at a subsequent stage or wave that occur because of the experience in a previous stage or wave of data collection (Silber et al, 2019;Warren & Halpern-Manners, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…In an observational study, it is difficult to assess whether individuals have clearly established boundaries and whether those nominated truly belong in the network given the specific prompts. Doing this would require the sort of experimental design of Silber et al (2019) who withheld the name generator from one wave for half of respondents. In that case, they found little evidence of panel conditioning, but this was using a considerably smaller network akin to the name generator found in the General Social Survey.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the stability of egocentric networks over time using the digital participant-aided sociogram tool Network Canvas

Hogan

Janulis

Phillips

et al. 2019

Net Sci

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This paper examines the stability of egocentric networks as reported over time using a novel touchscreen-based participant-aided sociogram. Past work has noted the instability of nominated network alters, with a large proportion leaving and reappearing between interview observations. To explain this instability of networks over time, researchers often look to structural embeddedness, namely the notion that alters are connected to other alters within egocentric networks. Recent research has also asked whether the interview situation itself may play a role in conditioning respondents to what might be the appropriate size and shape of a social network, and thereby which alters ought to be nominated or not. We report on change in these networks across three waves and assess whether this change appears to be the result of natural churn in the network or whether changes might be the result of factors in the interview itself, particularly anchoring and motivated underreporting. Our results indicate little change in average network size across waves, particularly for indirect tie nominations. Slight, significant changes were noted between waves one and two particularly among those with the largest networks. Almost no significant differences were observed between waves two and three, either in terms of network size, composition, or density. Data come from three waves of a Chicago-based panel study of young men who have sex with men.

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“…Within the context of repeated interviewing, motivated misreporting among experienced respondents (with an advanced knowledge of the questionnaire structure) has been documented for unemployment (Bailar, 1975;, party membership , everyday hygiene product use (Nancarrow & Cartwright, 2007), and functional limitations (Mathiowetz & Lair, 1994). However, other studies analyzing reports of household purchases and social contacts did not find any evidence of an increase in motivated misreporting among experienced survey respondents Eckman & Bach, 2021;Silber et al, 2019).…”

Section: Survey Satisficingmentioning

confidence: 99%

Panel Conditioning in a German Probability-Based Longitudinal Study: A Comparison of Respondents with Different Levels of Survey Experience

Kraemer¹,

Silber²,

Struminskaya³

et al. 2022

Preprint

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Learning effects due to repeated interviewing, which are referred to as panel conditioning, are a major threat to response quality in later waves of a panel study. Up to date, research has not provided a clear picture regarding the circumstances, mechanisms, and dimensions of potential panel conditioning effects. Especially the effects of conditioning frequency, that is, different levels of experience within a panel, on response quality are underexplored. Against this background, we investigated the effects of panel conditioning by using data from the GESIS Panel, a German mixed-mode probability-based panel study. Using two refreshment samples, we compared three panel cohorts with differing levels of experience with respect to several response quality indicators related to the mechanisms of reflection, satisficing, and social desirability. Overall, we find evidence for both negative (i.e., disadvantageous for response quality) as well as positive (i.e., advantageous for response quality) panel conditioning. Highly experienced respondents were more likely to satisfice by selecting mid-point responses or by speeding through the questionnaire. They also had a higher probability of refusing to answer sensitive questions than less experienced panel members. However, more experienced respondents were also more likely to optimize the response processes by needing less time compared to panelists with lower experience levels (when controlling for speeding). In contrast, we did not find significant differences with respect to the number of “don’t know” responses, non-differentiation, the selection of first response categories, and the number of non-triggered filter questions. Of the observed differences, speeding showed the highest magnitude with an average increase of 5.9 percentage points for highly experienced panel members compared to low experienced panelists.

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Does panel conditioning affect data quality in ego-centered social network questions?

Cited by 16 publications

References 42 publications

Who is dropped and why? Methodological and substantive accounts for network loss

Who is dropped and why? Methodological and substantive accounts for network loss

Assessing the stability of egocentric networks over time using the digital participant-aided sociogram tool Network Canvas

Panel Conditioning in a German Probability-Based Longitudinal Study: A Comparison of Respondents with Different Levels of Survey Experience

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