Interviewer Effects on a Network-Size Filter Question

Josten, Michael; Trappmann, Mark

doi:10.1515/jos-2016-0020

“…For this survey, in the case of the network size, the intraclass correlation (ICC) was 0.28, which means that 28 percent of the variance in the network size could be attributed to interviewers' characteristics. Similar studies conducted with CAPI mode have found similar or higher ICC Josten and Trappmann (2016). found an ICC of 0.30 in German panel survey "Labour Market and Social Security" (PASS) and Brüderl, Huyer-May and C. Schmiedeberg (2013) an ICC of up to 0.40 for the second wave of German Socioeconomic Panel.…”

supporting

confidence: 60%

Asymmetric Social Comparison and Life Satisfaction in Social Networks

Olivos

¹

,

Olivos-Jara

²

,

Browne

³

2020

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The aim of this study is to understand the effect of asymmetric social comparison on subjective well-being, and how it differs due to reciprocity. Our approach considers the social network of individuals as a local reference group. We tested competing hypotheses on negative and positive effects of comparison with worse-off (downward) and better-off (upward) targets with a representative sample of 1,596 Chileans over the age of 18.The findings support that life satisfaction is influenced by social comparison. By considering the social network as a reference group, the positive effect of downward comparison and the negative effect of upward comparison are confirmed. Upward comparison seems to be more substantial than downward comparison. Additionally, the positive effect of downward comparison decreases slightly with a reciprocal exchange of support between respondents and targets. The application of social network analysis opens a path to understanding the mechanisms underlying social comparison processes. Key wordsHappiness; reciprocity; social networks; asymmetric social comparison.3 This study has a threefold aim. First, to estimate the effect that comparison with relevant acquaintances, as identified by each subject, has on the own life satisfaction. Second, to understand how these comparisons work in relation to better or worse-off reference groups (upward and downward comparison, respectively). And third, to examine the influence that reciprocity with those relevant others has on this social comparison. By reciprocity, we understand the mutual exchange of support between the individual and their comparison targets, who are members of their social network. This threefold aim enables us to discuss the theoretical equivalence between the definitions of reference groups for social comparisons. Happiness research has used a wide variety of reference groups, such as classmates (Dijkstra, Kuyper, van der Werf, Buunk and van der Zee, 2008), colleagues (Gätcher, Nosenzo and Sefton, 2012; Gätcher and Thöni, 2010), and proximity of residence in the same city (Götz, Ehret, Jullien and Hall, 2006). However, research on the local dominance effect has shown the preeminence of closer targets in social comparison processes when multiple resources are available (Zell and Alicke, 2010). Despite this wide range of groups, social networks have not so far been considered. To our knowledge, this the first study considering social networks as the reference group of comparison. As we shall see later, the social network is the closest social surrounding of each individual and eventually, a more accessible, concrete, and suitable cognitive reference of comparison.Moreover, this study attempts to understand the cognitive basis of the social comparison process.Previous studies (Ateca-Amestoy et al., 2014;Senik, 2008;McBride, 2001) have been grounded in the analysis of the material life circumstances of a reference group, mainly income. Discussions on income reference groups are a growing field of research with a broad perspective. Senik...

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“…We suppose that the interviewer's job experience rises with increasing age what leads to a better understanding of recruiting respondents and thus to a lower nonresponse probability. However, we expect a non-linear relationship as Josten and Trappmann (2016) have found that the response quality decreases with interviewer age. Thus, we also include interviewer age squared.…”

Section: Interviewer Characteristicsmentioning

confidence: 94%

“…To analyse what factors contribute to unit nonresponse, a logistic random-intercept model is estimated (Josten and Trappmann 2016;Rabe-Hesketh and Skrondal 2008) as it is possible that the error terms are correlated across the observations interviewed by the same interviewer.…”

Section: Estimation Methodsmentioning

confidence: 99%

Unit nonresponse at the firm level: a cross-border analysis using the IAB-ReLOC data

Hecht

¹

,

Litzel

²

,

Schäffler

³

2019

J Labour Market Res

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The labour market effects of foreign direct investments (FDI) are a topic of constant interest. However, research progress is hindered as most datasets applied in research on this topic suffer from selectivity with respect to firm size. To overcome this deficiency a unique dataset that covers the total population of German firms with FDI in the Czech Republic and their Czech affiliates has been created: the IAB-ReLOC data. Based on this dataset, two points of high relevance are addressed in this paper. First, by presenting the generation process of this unique dataset the paper wants to provide guidance for similar cross-border data compilation projects and for researchers working with the data. Second, new insights on unit nonresponse in a firm-level survey are revealed. Based on multi-level logit models, the influence of firm and interviewer characteristics and of FDI features on survey participation is analysed. The main result is that apart from firm size and interviewer involvement, the response behaviour is related to the distance to the German-Czech border and to the strength of the cross-border relationship. What concerns the two latter characteristics, differences between German and Czech firms are identified.

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“…Similar studies conducted with CAPI mode have found similar or higher ICC. Josten and Trappmann (2016) found an ICC of 0.30 in German panel survey "Labour Market and Social Security" (PASS) and Brüderl, Huyer-May and C. Schmiedeberg 2013…”

Section: Independent Variablesmentioning

confidence: 99%

Asymmetric Social Comparison and Life Satisfaction in Social Networks

Olivos

¹

,

Olivos-Jara

²

,

Browne

³

2020

View full text Add to dashboard Cite

The aim of this study is to understand the effect of asymmetric social comparison on subjective well-being, and how it differs due to reciprocity. Our approach considers the social network of individuals as a local reference group. We tested competing hypotheses on negative and positive effects of comparison with worse-off (downward) and better-off (upward) targets with a representative sample of 1,596 Chileans over the age of 18. The findings support that life satisfaction is influenced by social comparison. By considering the social network as a reference group, the positive effect of downward comparison and the negative effect of upward comparison are confirmed. Upward comparison seems to be more substantial than downward comparison. Additionally, the positive effect of downward comparison decreases slightly with a reciprocal exchange of support between respondents and targets. The application of social network analysis opens a path to understanding the mechanisms underlying social comparison processes.

show abstract

Interviewer Effects on a Network-Size Filter Question

Cited by 25 publications

References 44 publications

Asymmetric Social Comparison and Life Satisfaction in Social Networks

Asymmetric Social Comparison and Life Satisfaction in Social Networks

Unit nonresponse at the firm level: a cross-border analysis using the IAB-ReLOC data

Asymmetric Social Comparison and Life Satisfaction in Social Networks

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