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Trivellato, Ugo

doi:10.1023/a:1004657006031

Cited by 39 publications

(9 citation statements)

References 12 publications

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“…Repeated cross-sectional and panel studies are often conducted to gain insights into important behavioral and psychological variables and trajectories, as in the COVID-19 pandemic (e.g., Betsch, 2020; Luchetti et al, 2020; Zettler et al, 2021). Although it is well-known that RCS studies and panel studies have different features, strengths, and limitations (e.g., Juodis, 2018; Lebo & Weber, 2015; Menard, 2007; Trivellato, 1999), such as the possibility of keeping sample characteristics constant across measurement occasions or concerning potential effects of participants in responding to similar items repeatedly, it is often (implicitly) assumed that both types of studies, in principle, allow for drawing similar general conclusions about variables in a population and how they change in the population over time. Accordingly, some previous studies have applied both RCS and panel studies to address a research topic (e.g., Butterworth et al, 2020; Sommet et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Both RCS and panel study designs have certain features, strengths, and limitations (e.g., Lebo & Weber, 2015; Menard, 2007; Trivellato, 1999) that potentially hamper a straightforward comparison of observed responses and their trajectories. For instance, most studies rely on voluntary participation, so participants might have different intrinsic or extrinsic motivations for self-selecting into an RCS or panel study, especially because the latter require repeated participation.…”

Section: Open Science Statementmentioning

confidence: 99%

“…However, unobserved selection, cohort, societal, and/or time effects among the responders can threaten the comparability of RCS samples and assessments. Moreover, RCS studies do not allow for investigating trajectories or their drivers on an individual level, which is possible in panel studies (e.g., Cook & Campbell, 1979; Menard, 2007; Trivellato, 1999).…”

Section: Open Science Statementmentioning

confidence: 99%

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Comparing responses in repeated cross-sectional and panel studies: Results across eight weeks during the first COVID-19 lockdown in Denmark.

Zettler

Lilleholt

Böhm

et al. 2021

Psychological Assessment

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It is often important to study people's thoughts, feelings, and behaviors over time. To this end, researchers have relied on repeated cross-sectional (RCS) studies, in which different people from the same population participate on different measurement occasions. Also, researchers have relied on panel studies, in which the same group of people participate on different measurement occasions. However, few studies have directly tested whether participants' responses in RCS studies were similar to those found in panel studies. To address this gap, we compared the responses to 33 items, 28 of which were further grouped into four aggregates (Affections, Worries, States, Health concerns), over 8 weeks during the first COVID-19 lockdown in Denmark in a RCS study (overall N = 5,993, per measurement occasion 616 ≤ n ≤ 964) with the responses in a largely equivalent panel study (993 ≤ n ≤ 2,546 across measurement occasions). The study participants were randomly drawn from the same quota-representative participant pool and responded to the same items on the same measurement occasions. Results indicated a few differences between the study samples on the first measurement occasion (i.e., selection effects between studies). Further, we found statistical support for different trajectories in 21 aggregates/items. However, visual inspection of the trajectories suggested subtle differences between the studies at large. The results thus raise awareness that the trajectories of people's thoughts, feelings, and behaviors can differ between survey methods, especially when only a few measurement occasions are considered. Nevertheless, such differences might not be substantial over time. Public Significance StatementAnalyses comparing responses to 37 variables over 8 weeks during the first COVID-19 lockdown in Denmark in a repeated cross-sectional (RCS) study with the responses to the same variables in a panel study showed different trajectories in 21 variables. However, in most cases, the differences were subtle over the 8-week study period. The results suggest being more cautious in generalizing trajectories of variables based on one study design and only a few measurement occasions.

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Section: Discussionmentioning

confidence: 99%

Section: Open Science Statementmentioning

confidence: 99%

Section: Open Science Statementmentioning

confidence: 99%

See 1 more Smart Citation

Comparing responses in repeated cross-sectional and panel studies: Results across eight weeks during the first COVID-19 lockdown in Denmark.

Zettler

Lilleholt

Böhm

et al. 2021

Psychological Assessment

View full text Add to dashboard Cite

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“…Typically, the units leaving the sample are replaced by others, with observable characteristics (the same used in the survey design) quite similar to those of the replaced units. A more complex solution is to set up panel rotations that periodically discard a part of the panel and make place for new units, that can also realign the sample to the changes in the reference population occurred since the creation of the panel (Trivellato 1999). Rotating panels also spread the response burden and therefore indirectly reduce the attrition caused by it (Ardilly and Lavallée 2007).…”

Section: Panel Attrition: An Overviewmentioning

confidence: 99%

Using Administrative Data to Evaluate Sampling Bias in a Business Panel Survey

D’Aurizio

Papadia

2019

Journal of Official Statistics

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We examine two sources of bias for the Bank of Italy’s panel business survey of Industrial and Services Firms: 1) the bias caused by panel attrition; and 2) the bias created by delays in the distributional data on the reference population, needed for computing the survey weights. As for the first source of bias, the estimates strongly dependent on big firms’ values are less affected by panel attrition than those representing firms’ average behavior, independent of their sizes. Positive economic results make it easier to enroll new firms in the survey, in order to replace firms dropping out because of bad economic performances. However, the economic results of new entrances become more aligned to those of the population, once they enter the sample. A very different result emerges for the second source of bias, since, when the population size is highly variable, the information delays produce a bias for the estimates influenced by the contribution of great firms, but the effect is negligible for the estimates not dependent on firm size.

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“…The time dimension can be easily determined by the purpose of the survey which means the survey can be designed with the minimum length that meet the requirement of the interest demand, but the length is also seriously depended on the costs of collection (Trivellato, U., 1999).…”

Section: Time Dimensionmentioning

confidence: 99%

The Effects of Time Dimension and Interview Period Research on Sample Loss in SIPP

Xiong

2018

Proceedings of the 2nd International Forum on Management, Education and Information Technology Application (IFMEITA 2017)

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The Survey of Income and Program Participation (SIPP), is widely used in many fields of the most effective methods to analyze the data. From the history of the development of SIPP, the panel was redesigned in 1996, in order to improve the quality of information, improves the sample size and the length of the two. A change usually affect the sample loss. This paper deeply analyses the reasons of loss, to find ways to reduce sample loss, improve the survey quality possible.

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Cited by 39 publications

References 12 publications

Comparing responses in repeated cross-sectional and panel studies: Results across eight weeks during the first COVID-19 lockdown in Denmark.

Comparing responses in repeated cross-sectional and panel studies: Results across eight weeks during the first COVID-19 lockdown in Denmark.

Using Administrative Data to Evaluate Sampling Bias in a Business Panel Survey

The Effects of Time Dimension and Interview Period Research on Sample Loss in SIPP

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