Statistical matching is a technique which can be applied when one wants to investigate the joint relationship between two variables that are observed in different datasets, using one or more...Show moreStatistical matching is a technique which can be applied when one wants to investigate the joint relationship between two variables that are observed in different datasets, using one or more variables that overlap in both datasets. This joint relationship cannot be estimated without relying on assumptions or additional data. Classically, statistical matching is based on the Conditional Independence Assumption (CIA) which asserts the non-overlapping variables to be independent given the overlapping variable. This assumption is inflexible, untestable and often does not hold. The current project proposes to use an approach based on the Instrumental Variable Assumption (IVA). An instrumental variable is a variable that, given the value of some mediating variable, has no effect on some outcome variable. In the context of statistical matching this gives rise to three scenarios: the mediating variable overlaps, the outcome variable overlaps, or the instrumental variable overlaps. The IVA approach is more flexible than the CIA approach. This is because the IVA approach does not make any assumptions on which variable is the overlapping variable, whereas the CIA always conditions on the overlapping variable. The aims of the current study were twofold: 1) how does the IVA approach perform when the assumption is violated to various degrees and 2) how does the IVA approach compare to the CIA approach. To answer these questions, a simulation study was performed. For each scenario, joint probabilities of the non-overlapping variables were estimated under both the IVA and the CIA in populations which violate the IVA to various degrees. Measures for the bias, accuracy and precision were estimated and compared. The results indicate that the IVA approach is moderately robust against slight violations of the assumption. When the IVA is not violated, estimations are unbiased and for all matching scenarios the method outperforms the CIA. When the IVA is violated it is advisable to rely on the CIA, since results of the current simulation study suggest the CIA to be more robust against violations in general.Show less
Multivariate binary data with multiple binary response variables arise in many areas of research, including biology, psychology, medicine, dentistry, and other empirical sciences. In such data, the...Show moreMultivariate binary data with multiple binary response variables arise in many areas of research, including biology, psychology, medicine, dentistry, and other empirical sciences. In such data, the effect of a predictor on the response variable and the effect of a predictor on the association structure between the response variables is of interest. Multinomial Restricted Unfolding (MRU) is a probabilistic multidimensional unfolding model that can be used to analyse multicategory response variables in the presence of predictors. In this thesis, we investigated an extension of the MRU model to analyse multivariate binary data focusing on how diagnoses of depressive and anxiety disorders are influenced by personality traits and how the association between two disorders is affected by these personality traits. We compared the results using usual and squared Euclidean distances for the main effects and associations MRU models. We have demonstrated that MRU models using squared and usual Euclidean distances can be used to analyse multivariate binary data, representing well the changes in log odds and the changes in log odds ratio. Our results indicated that the MRU models using squared Euclidean distances are more straightforward and easier to be interpreted than those using usual Euclidean distance. However, despite the more complicated interpretation, the model using the usual Euclidean distance is more flexible, which might lead to a better model fit. Regarding the change in log odds of having GAD, the main effects model results indicated a constant change in slope between different pairs of categories that represent GAD. For the associations model, the parallel lines indicate that the change in the slope was constant within a pair of categories that represents GAD but not the same for different pairs of categories. When usual Euclidean distances were used, the change in slope was not constant for both main effects and associations models. Regarding the interpretation rules to express the change in the log odds ratio, the main effects model showed that the association structure does not dependent on the value of the predictor variable. However, for the associations model, the log odds ratio is dependent on the value of the predictor variable, in which a constant change in slope is shown. When using usual Euclidean distances to explore the association structure between two response variables, the change in slope was not constant for both models.Show less
As the availability of data becomes more widespread and computational technology develops, the need to model several outcome variables at once increases. To do this for multiple dichotomous outcome...Show moreAs the availability of data becomes more widespread and computational technology develops, the need to model several outcome variables at once increases. To do this for multiple dichotomous outcome variables, De Rooij and Groenen (2021) proposed the MELODIC family for simultaneous binary logistic regression in a reduces space. As an added feature, 4 different forms of regularization on the singular values were proposed to let the algorithm itself select the true dimensionality of the data set. In this paper a simulation study was performed to provide empirical evidence for the functionality of the regularization features on data sets with differing numbers of subjects, predictor variables, and outcome variables. The results show that the hard-thresholding regularization consistently estimates the correct dimensionality with regularization on the logarithm of the singular values slightly outperforming the regularization on the unaltered singular values.Show less
Objective: In this study I investigated the p-curves of 250 social psychology papers to see if they showed evidence of p-hacking. I also divided the papers into categories based on types of...Show moreObjective: In this study I investigated the p-curves of 250 social psychology papers to see if they showed evidence of p-hacking. I also divided the papers into categories based on types of justifications for exclusions (strongly justified, grey area, poorly justified) to describe how pcurves of these categories differ from each other. Methods: From a larger sample of 1000 random social psychology papers, 250 were randomly selected and coded by a larger group of coders. The papers were coded for main finding, main result, statistics sentence and the number and justification of exclusions. The statistics sentences were then pasted into a software that graphed the p-curves and analysed them for skewness and the presence of evidential value. Results: Two studies were mistakenly added in the data file but were kept for further steps. About 118 studies were excluded for missing or inadequate statistics sentences and 16 reported non-significant results. A total of 15 papers were left un-coded. The data analyses were done with the 104 remaining papers. The p-curve for the total sample was right-skewed and had adequate evidential value. The p-curve for the grey area justifications for exclusions was almost identical to the p-curve of the total sample. The p-curve of strongly justified exclusions followed similar pattern but also had a small peak around p = .05. The p-curve for poorly justified exclusions had two large peaks at .01 and .04. Conclusion: The sample of social psychology papers shows no evidence of p-hacking and has adequate evidential value. There seems to be some differences in the p-curves for different categories of justifications for exclusions. However, difficulties in coding the papers show that there is a need for more meta research into the standardization of reporting findings and exclusions in social psychology papers.Show less
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
A job application process often includes a test battery with several skills and personality tests. The performance on these tests is used to predict an overall job performance score and can help...Show moreA job application process often includes a test battery with several skills and personality tests. The performance on these tests is used to predict an overall job performance score and can help decide whether or not to hire someone. Prediction based on a test battery is often done by ordinary least-squares (OLS) models. OLS models try to correctly explain the relationship between the dependent variable and the tests itself. However, prediction is also important when you want to select the best candidates. OLS models are not sparse and often have high variance, thus it may not be the best model in terms of prediction. To improve prediction, machine learning methods, such as the least absolute shrinkage and selection operator (LASSO) regression, can be used. The LASSO adds bias to estimates and reduces variance to improve prediction. One disadvantage of LASSO regression is that its not scale invariant in the predictors. Therefore, predictors are standardized, typically by using the observed-score variance. In psychological tests, scores consist of two parts: the error part and the truescore part. The observed-score variance thus also consists of two parts: error variance and true-score variance. The true-score variance part is the most important part for prediction. However, the error variance part can cloud the effect of the true-score variance and influences whether a test is present in the prediction of the LASSO or not. This study examines two alternatives to standardization by the observed-score variance for the LASSO. The first one standardizes by the true-score variance, to minimize the effect of the error variance in the statistical model for variable selection. The second alternative is a transformation by the ordinary least-squares coefficient, based on the nonnegative garrote model, to add explanatory value to the model and overshadow the effect of the error variance. We examine the truthfulness of variable selection, truthfulness of coefficient size, and prediction accuracy through simulation with multiple scenarios of design factors. Design factors include number of observations, reliabilities of the tests, covariance between latent variables and the number of true nonzero regression coefficient. The methods were also compared with respect to an empirical data set of test results for psychological trait tests measuring general mental health to determine differences and semblencas between real-world data and simulation. Results showed that the methods act differently under different circumstances. Both alternatives improved the variable selection and truthfulness of coefficients in most scenarios, while the prediction was approximately the same for all three methods. This thesis gives recommendations for which method is best to use in which scenario, and shows the effects of the design factors on the truthfulness of the three methods in the simulation study. Limitations of this simulation study are given together with recommendations for further research.Show less
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
Multivariate binary data are often collected in scientific fields such as psychology, economics and epidemiology. Worku and de Rooij (2018) proposed a marginal model for the analysis of this type...Show moreMultivariate binary data are often collected in scientific fields such as psychology, economics and epidemiology. Worku and de Rooij (2018) proposed a marginal model for the analysis of this type of data in a distance framework: The multivariate logistic distance (MLD) model. Two different models were introduced by Worku and de Rooij: a restricted and an unrestricted MLD model. The interpretation of both models is clear, and a log-odds as well as a biplot representation can be used. In this work we proposed three extensions to the restricted model and showed the implications of the extensions for the interpretation of the corresponding biplot as well as for the log-odds. First, we showed how the model can be extended by making it possible for a response variable to belong to multiple dimensions. Consequently, the extended model can be used to examine other dimensionality structures compared to the original model. Second, we allowed for non-linear relationships of the predictor variables with the response variables in the model and therefore making the model more flexible. Finally, the dimensionality structure as well as the final predictor variables need to be selected. We showed how to use the prediction capability of a model as a selection criterion to select between competing models. This is a more versatile method to perform model selection, based on the bias-variance trade off, compared to the likelihood based criterion used in the original model. We fitted 16 variations of the model to an empirical data set to compare performance based on their prediction capability. All variations of the model can be estimated using standard statistical software for univariate modelsShow less
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
The brain's Default Mode Network (DMN) raised a lot of interest in neuroscience the past decade. The DMN, is active even when a human is resting and his mind is not task oriented [1]. It is...Show moreThe brain's Default Mode Network (DMN) raised a lot of interest in neuroscience the past decade. The DMN, is active even when a human is resting and his mind is not task oriented [1]. It is mentioned in the literature [2, 3], that disruptions within the DMN often occur in the profile of patients carrying some disorder, such as Parkinson's disease (PD), Alzheimer's disease (AD) and epilepsy. In this thesis we aim to build a classification model that predicts whether a new subject is an Alzheimer's patient or not. This model is created based on the DMN profile of 250 subjects. To this purpose, we employ the δmachine classification approach of Yuan, Heiser and De Rooij [4], which uses the distances between DMN profiles as the predictor matrix in a lasso logistic regression model. It is essential to define a distance measure that best fits the DMN univariate time series data, that is, a measure which can strongly represent the distances, irrespective of the possibility of data distortion in time. Keeping that in mind, five distance measures were investigated, which are designed for time series and are implemented in the up-to-date R packages TSdist and TSclust. The final goal is twofold: on the one hand building a classification model by using the δ-machine approach, based on the profile of the activity in the DMN of 250 subjects, and on the other hand uncovering which distance measure is the most suitable when involved in the δ-machine approach.Show less
