abstract: This dissertation examines a planned missing data design in the context of mediational analysis. The study considered a scenario in which the high cost of an expensive mediator limited sample size, but in which less expensive mediators could be gathered on a larger sample size. Simulated multivariate normal data were generated from a latent variable mediation model with three observed indicator variables, M1, M2, and M3. Planned missingness was implemented on M1 under the missing completely at random mechanism. Five analysis methods were employed: latent variable mediation model with all three mediators as indicators of a latent construct (Method 1), auxiliary variable model with M1 as the mediator and M2 and M3 as auxiliary variables (Method 2), auxiliary variable model with M1 as the mediator and M2 as a single auxiliary variable (Method 3), maximum likelihood estimation including all available data but incorporating only mediator M1 (Method 4), and listwise deletion (Method 5). The main outcome of interest was empirical power to detect the mediated effect. The main effects of mediation effect size, sample size, and missing data rate performed as expected with power increasing for increasing mediation effect sizes, increasing sample sizes, and decreasing missing data rates. Consistent with expectations, power was the greatest for analysis methods that included all three mediators, and power decreased with analysis methods that included less information. Across all design cells relative to the complete data condition, Method 1 with 20% missingness on M1 produced only 2.06% loss in power for the mediated effect; with 50% missingness, 6.02% loss; and 80% missingess, only 11.86% loss. Method 2 exhibited 20.72% power loss at 80% missingness, even though the total amount of data utilized was the same as Method 1. Methods 3 – 5 exhibited greater power loss. Compared to an average power loss of 11.55% across all levels of missingness for Method 1, average power losses for Methods 3, 4, and 5 were 23.87%, 29.35%, and 32.40%, respectively. In conclusion, planned missingness in a multiple mediator design may permit higher quality characterization of the mediator construct at feasible cost. / Dissertation/Thesis / Doctoral Dissertation Psychology 2015

Denna kvalitativa studie “Varför engagera sig?” av Josefine Ekenstein och Erica Mogel undersöker vad som driver individen att engagera sig ideellt i organisationen Missing People och vad den engagerade får i utbyte. Utifrån två frågeställningar samlades det empiriska materialet in genom semistrukturerade intervjuer för att sedan analyseras mot de teoretiska ramverken. De teorier som används är Gagné & Decis Self-Determination theory, Maslows behovshierarki samt Bourdieus oegennyttiga handlingar. Studien kommer fram till att det finns vissa framträdande inre och yttre faktorer som främst motiverar intervjupersonerna till engagemang inom Missing People. I slutsatsen presenteras även hur intervjupersonerna mår bra av att hjälpa andra och hur det egna välmåendet kan vara en form av belöning. / Populärvetenskaplig sammanfattning Denna studie undersöker varför individer väljer att arbeta ideellt i organisationen Missing People trots att arbetet inte medför någon ekonomisk kompensation. Vi undersöker även om individen upplever att den får ut någon form belöning av sitt engagemang. Vi har genom att intervjua personer som är engagerade inom Missing People kommit fram till att den främsta anledningen till att intervjupersonerna engagerat sig i organisationen är för att de mår bra av att hjälpa andra. / Abstract This qualitative study, "Why get involved?” by Josefine Ekenstein and Erica Mogel examines what motivates the individual to engage in the voluntary organization Missing People and examine what the individual gets in reward. Based on two research questions we gathered our empirical material through semi-structured interviews and were then analyzed through the theoretical frameworks. The theories used are Gagné & Decis Self-determination theory, Maslow’s hierarchy of needs theory and Bourdieu’s theory of disinterested actions. The study concludes that there are both internal and external factors that primarily motivate the respondents for their involvement in Missing People. It also concludes that the respondents felt good to help others which could be seen as a form of reward.

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I studien deltog 235 personer, 231 av dessa deltog i jämförelsen mellan könen. Syftet var att undersöka om Fear of Missing Out [FoMO] spelar roll i hur nöjd man är med livet (Satisfaction with Life Scale) [SWLS] både i gruppen som helhet samt uppdelat mellan kön. Utöver detta ämnade studien undersöka om det fanns något samband mellan FoMO och ålder, både i gruppen som helhet och uppdelat mellan kön. Vidare var syftet också att se om det fanns några könsskillnader när det kommer till FoMO samt SWLS. Studien kunde visa på ett negativt samband mellan självskattad SWLS och självskattad FoMO för gruppen män. För gruppen kvinnor fanns det inget samband mellan självskattad SWLS och självskattad FoMO. För gruppen som helhet fanns det också ett negativt samband mellan självskattad SWLS och självskattad FoMO. Man bör dock uppmärksamma att det är gruppen män som gör att det blir ett negativt samband mellan FoMO och SWLS vad gäller gruppen som helhet. Studien har också kunnat visa på att det finns ett negativt samband mellan FoMO och ålder både i gruppen som helhet samt för kvinnor och män. Ju äldre deltagarna var, desto lägre självskattad FoMO. Slutligen kunde studien inte visa på några könsskillnader i självskattad FoMO, inte heller i SWLS. / In this study a total of 235 people participated, whereof 231 of these participated in comparisons between genders. The purpose of this study was to see if Fear of Missing Out [FoMO] plays a role in how satisfied you are with your life (Satisfaction with Life Scale) [SWLS], amongst all participants but also compared between gender. In addition, this study wanted to see if there were any connection between FoMO and age among all participants as well as between genders. Furthermore, the study wanted to see if there were any differences in FoMO and SWLS between genders. The results showed that there was a significant negative correlation between self-reported SWLS and self-reported FoMO for men. But there was no correlation between self-reported SWLS and self-reported FoMO for women. Though there were a negative correlation between self-reported SWLS and self-reported FoMO for all participants, this indicates that the significant correlation in the whole group was indeed caused by the male participants since there were no significant correlations for women. This study could also show a significant negative correlation between FoMO and age for all participants as well as for men and women separately. Older participants reported lower FoMO. Finally, this study did not find any differences in gender when it comes to self-reported FoMO, nor self-reported SWLS.

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Missing data is one of the challenges we are facing today in modeling valid statistical models. It reduces the representativeness of the data samples. Hence, population estimates, and model parameters estimated from such data are likely to be biased. However, the missing data problem is an area under study, and alternative better statistical procedures have been presented to mitigate its shortcomings. In this paper, we review causes of missing data, and various methods of handling missing data. Our main focus is evaluating various multiple imputation (MI) methods from the multiple imputation of chained equation (MICE) package in the statistical software R. We assess how these MI methods perform with different percentages of missing data. A multiple regression model was fit on the imputed data sets and the complete data set. Statistical comparisons of the regression coefficients are made between the models using the imputed data and the complete data.

The longitudinal feature of measurements and counting process of responses motivate the regression models for longitudinal count data (LCD) to take into account the phenomenons such as within-subject association and overdispersion. One common problem in longitudinal studies is the missing data problem, which adds additional difficulties into the analysis. The missingness can be handled with missing data techniques. However, the amount of missingness in the data and the missingness mechanism that the data have affect the performance of missing data techniques. In this thesis, among the regression models for LCD, the Log-Log-Gamma marginalized multilevel model (Log-Log-Gamma MMM) and the random-intercept model are focused on. The performance of the models is compared via a simulation study under three missing data mechanisms (missing completely at random, missing at random conditional on observed data, and missing not random), two types of missingness percentage (10% and 20%), and four missing data techniques (complete case analysis, subject, occasion and conditional mean imputation). The simulation study shows that while the mean absolute error and mean square error values of Log-Log-Gamma MMM are larger in amount compared to the random-intercept model, both regression models yield parallel results. The simulation study results justify that the amount of missingness in the data and that the missingness mechanism that the data have, strictly influence the performance of missing data techniques under both regression models. Furthermore, while generally occasion mean imputation displays the worst performance, conditional mean imputation shows a superior performance over occasion and subject mean imputation and gives parallel results with complete case analysis.

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This thesis provides an introduction to methods for handling missing data. A thorough review of earlier methods and the development of the field of missing data is provided. The thesis present the methods suggested in today’s literature, multiple imputation and maximum likelihood estimation. A simulation study is performed to see if there are circumstances in small samples when any of the two methods are to be preferred. To show the importance of handling missing data, multiple imputation and maximum likelihood are compared to listwise deletion. The results from the simulation study does not show any crucial differences between multiple imputation and maximum likelihood when it comes to point estimates. Some differences are seen in the estimation of the confidence intervals, talking in favour of multiple imputation. The difference is decreasing with an increasing sample size and more studies are needed to draw definite conclusions. Further, the results shows that listwise deletion lead to biased estimations under a missing at random mechanism. The methods are also applied to a real dataset, the Swedish enrollment registry, to show how the methods work in a practical application.

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