Identifying Stabilising Effects on Survey Based Life Satisfaction Using Quasi-maximum Likelihood Estimation

Klement, Johannes

doi:10.1007/s10902-021-00376-3

Cited by 4 publications

(6 citation statements)

References 62 publications

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“…This is also suggested by the analysis of Bottan and Perez Truglia (2011). For a fuller explanation of these relationships, see Klement (2021).…”

Section: Methods Usedmentioning

confidence: 64%

“…As part of the analysis of the interviewer bias, I estimate the influence of a computer-assisted, personal interview on life satisfaction. For the model, which is the basis of the estimation, eight happiness correlates are used as independent variables, which are known to have a significant influence on the variation in life satisfaction (see Argyle, 1999;Wagner, 2010, 2013;Klement, 2021;Layard, 2006). Table 2 provides a short description of the variable content.…”

Section: Regression Variablesmentioning

confidence: 99%

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The Effect of Response Biases on the Variation in Life Satisfaction

Klement

2023

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Although life satisfaction is an empirically well-studied happiness concept, there are still open questions regarding the variation in life satisfaction. Using life satisfaction data from the German Socio-Economic Panel (SOEP), I examine the extent to which the variation in life satisfaction is influenced by biases arising from the design and implementation of the life satisfaction survey using a variety of methodological approaches, including dis- tributional analysis, descriptive statistics, Markov transition probabilities, and logistic regression. As a result, I show that the presence of an interviewer during the survey can lead to significant positive and non-adapting changes in life satisfaction. The eleven-point integer scale on which life satisfaction is measured has an even more decisive influence. I show that people use the midpoints of the scale to evaluate their lives, balancing the quality of their lives between good and bad. This has implications for the variation in general. It seems implausible that, on a scale of 0 to 10, average life satisfaction scores of more than 8 can be achieved in the long term. Ultimately thus the scale also effects adaptive processes. I furthermore show that the variation in dissatisfaction and satisfaction follow different patterns. In order to gain a more nuanced understanding of the causes, it may be useful for future research to analyse periods of satisfaction and dissatisfaction with life separately.

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“…This is also suggested by the analysis of Bottan and Perez Truglia (2011). For a fuller explanation of these relationships, see Klement (2021).…”

Section: Methods Usedmentioning

confidence: 64%

Section: Regression Variablesmentioning

confidence: 99%

The Effect of Response Biases on the Variation in Life Satisfaction

Klement

2023

Preprint

Self Cite

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“…As part of the research on social desirability bias, I estimate the influence of a computer-assisted, personal interview on life satisfaction. For the model, which is the basis of the estimation, eight happiness correlates are used as independent variables, which are known to have a significant influence on the variation of life satisfaction (see Argyle, 1999;Wagner, 2010, 2013;Klement, 2021;Layard, 2006). Table 2 provides a short description of the variable content.…”

Section: Regression Variablesmentioning

confidence: 99%

Social Desirability and Central Tendency Bias in the Survey and Variation of Life Satisfaction

Klement

2023

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Although life satisfaction is an empirically well-studied happiness concept, there are still open questions regarding the variation in life satisfaction and the influence of variables not included in data sets such as the German Socio-Economic Panel (SOEP). In this article, I use data from the SOEP to examine the extent to which the variation in life satisfaction is influenced by biases resulting from the design of the life satisfaction survey. Using a variety of methodological approaches, including distributional analysis, descriptive statistics, Markov transition probabilities and logistic regression, I examine the extent to which possible biases due to central tendency or social desirability affect the variation in life satisfaction. This leads me to the following conclusions: 1. People who are interviewed in the presence of an interviewer are more satisfied with their lives than people who are interviewed in writing without an interviewer. This suggests that people express themselves more positively about their well-being in the presence of others. 2. On the 11-point life satisfaction scale, defined as a whole number between 0 and 10, respondents prefer to choose points 5 and 8, which are the mid-points of the scale, and they choose them more often than other points. This is important for adaptation research: it suggests that people prefer to report life satisfaction that is balanced between good and bad, leaving room for improvement even in satisfied states. Social desirability and central tendency bias have a stronger influence on the variation of life satisfaction than many of the happiness correlates examined. In terms of growth in life satisfaction, point 8 seems to be the maximum on average. A higher value is implausible based on the results of the study.

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“…These variables are happiness correlates. Please refer to (see Argyle, 1999;Wagner, 2010, 2013;Klement, 2021;Layard, 2006) for further information. For a brief description of the variables, please see Table 2.…”

Section: Regression Variablesmentioning

confidence: 99%

The Impact of Response Biases on the Variation in Life Satisfaction

Klement

2023

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Despite the abundance of empirical research on life satisfaction, disparities remain regard- ing its variation. Using data from the German Socio-Economic Panel (SOEP), this study analyses to what extent biases in the design and implementation of life satisfaction sur- veys influenced the variation in life satisfaction. The study employs various methodologi- cal approaches, including distributional analysis, descriptive statistics, Markov transition probabilities and logistic regression. Consequently, this study demonstrates the significant positive and non-adaptive changes in life satisfaction that can arise when an interviewer is present during a survey. Moreover, the method of measuring life satisfaction on an eleven- point integer scale has a substantial impact. The midpoints of the scale are frequently used by individuals to assess their lives, which results in a balance between positive and negative evaluations. These findings have broader implications for the overall level of variation. It appears unlikely that an average life satisfaction score of more than 8 can be maintained over the long term on a scale of 0 to 10. It should be noted that the scale also impacts adaptive processes. Additionally, the patterns of dissatisfaction and satisfaction variation differ. For a more nuanced understanding of the causes, future research may benefit from analyzing periods of satisfaction and dissatisfaction with life separately.

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Identifying Stabilising Effects on Survey Based Life Satisfaction Using Quasi-maximum Likelihood Estimation

Cited by 4 publications

References 62 publications

The Effect of Response Biases on the Variation in Life Satisfaction

The Effect of Response Biases on the Variation in Life Satisfaction

Social Desirability and Central Tendency Bias in the Survey and Variation of Life Satisfaction

The Impact of Response Biases on the Variation in Life Satisfaction

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