Individual differences in satisfaction with activity-based work environments

Satisfaction with activity-based work environments (ABW environments) often falls short of expectations, with striking differences among individual workers. A better understanding of these differences may provide clues for optimising satisfaction with ABW environments and associated organisational outcomes. The current study was designed to examine how specific psychological needs, job characteristics, and demographic variables relate to satisfaction with ABW environments. Survey data collected at seven organizations in the Netherlands (N = 551) were examined using correlation and regression analyses. Significant correlates of satisfaction with ABW environments were found: need for relatedness (positive), need for privacy (negative), job autonomy (positive), social interaction (positive), internal mobility (positive), and age (negative). Need for privacy appeared to be a powerful predictor of individual differences in satisfaction with ABW environments. These findings underline the importance of providing work environments that allow for different work styles, in alignment with different psychological need strengths, job characteristics, and demographic variables. Improving privacy, especially for older workers and for workers high in need for privacy, seems key to optimizing satisfaction with ABW environments.

No Menstrual Cyclicity in Mood and Interpersonal Behaviour in Nine Women with Self-Reported Premenstrual Syndrome

BACKGROUND/AIMS

Before diagnosing premenstrual dysphoric disorder (PMDD), 2 months of prospective assessment are required to confirm menstrual cyclicity in symptoms. For a diagnosis of premenstrual syndrome (PMS), this is not required. Women with PMDD and PMS often report that their symptoms interfere with mood and social functioning, and are said to show cyclical changes in interpersonal behaviour, but this has not been examined using a prospective approach. We sampled cyclicity in mood and interpersonal behaviour for 2 months in women with self- reported PMS.

METHODS

Participants met the criteria for PMS on the Premenstrual Symptoms Screening Tool (PSST), a retrospective questionnaire. For 2 menstrual cycles, after each social interaction, they used the online software TEMPEST to record on their smartphones how they felt and behaved. We examined within-person variability in negative affect, positive affect, quarrelsomeness, and agreeableness.

RESULTS

Participants evaluated TEMPEST as positive. However, we found no evidence for menstrual cyclicity in mood and interpersonal behaviour in any of the individual women (n = 9).

CONCLUSION

Retrospective questionnaires such as the PSST may lead to oversampling of PMS. The diagnosis of PMS, like that of PMDD, might require 2 months of prospective assessment.

Micro-level mechanisms of identity development: The role of emotional experiences in commitment development

Based on Marcia's theory, many researchers consider exploration and commitment as the main processes in identity development. Although some identity theorists have hypothesized that emotional experience may also be an important part of the mechanisms of identity development, empirical research to investigate this claim has been lagging behind. In this study, we shed light on the role of emotional experiences in micro-level commitment dynamics, and compare this to the role of exploration. We take a within-individual approach, and particularly focus on educational commitment. We collected weekly measurements among 103 first year university students over several months, resulting in 22 to 30 measurements for each individual. Every week, the students reported an important experience and accompanying positive and negative emotions, their level of educational exploration and commitment. We generated linear growth models for each individual separately, using Dynamic Linear Modeling. These individual models generate regression weights that indicate how strong the impact is of exploration, positive and negative emotional experiences on changes in micro-level commitment for each individual. Our main finding is that both positive and negative emotional experiences are indeed related to changes in educational commitment. Positive experiences, but surprisingly, also negative experiences, are related to increases in educational commitment for the majority of individuals. Moreover, for the large majority of individuals, the impact of emotional experiences is larger than the impact of exploration. Therefore, we conclude that it is highly likely that emotional experiences are an essential part of the micro-level mechanisms of identity development. (PsycINFO Database Record

Model Selection in Continuous Test Norming With GAMLSS

To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box-Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box-Cox Power Exponential model for test norming requires model selection, but it is unknown how well this can be done with an automatic selection procedure. In a simulation study, we compared the performance of two stepwise model selection procedures combined with four model-fit criteria (Akaike information criterion, Bayesian information criterion, generalized Akaike information criterion (3), cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design. The new procedure combined with one of the generalized Akaike information criterion was the most efficient model selection procedure (i.e., required the smallest sample size). The advocated model selection procedure is illustrated with norming data of an intelligence test.

Regression assumptions in clinical psychology research practice-a systematic review of common misconceptions

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

The Use of Subscores in Higher Education: When Is This Useful?

Assessment in higher education is challenging because teachers face more students, with less contact time as compared to primary and secondary education. Therefore, teachers and management are often interested in efficient ways of giving students diagnostic feedback and providing information on the basis of subscores is one method that is often used in large-scale standardized testing. In this article we discuss some recent psychometric literature that warns against the use of subscores in addition to the use of total scores. We illustrate how the added value of subscores can be evaluated using two college exams: A multiple choice exam and a combined open-ended question and multiple choice exam; these formats are often used in higher education and represent cases in which using subscores may be informative. We discuss the implications of our findings for future classroom evaluation.

Bayesian dynamic modelling to assess differential treatment effects on panic attack frequencies

Abstract:

To represent the complex structure of intensive longitudinal data of multiple individuals, we propose a hierarchical Bayesian Dynamic Model (BDM). This BDM is a generalized linear hierarchical model where the individual parameters do not necessarily follow a normal distribution. The model parameters can be estimated on the basis of relatively small sample sizes and in the presence of missing time points. We present the BDM and discuss the model identification, convergence and selection. The use of the BDM is illustrated using data from a randomized clinical trial to study the differential effects of three treatments for panic disorder. The data involves the number of panic attacks experienced weekly (73 individuals, 10–52 time points) during treatment. Presuming that the counts are Poisson distributed, the BDM considered involves a linear trend model with an exponential link function. The final model included a moving average parameter and an external variable (duration of symptoms pre-treatment). Our results show that cognitive behavioural therapy is less effective in reducing panic attacks than serotonin selective re-uptake inhibitors or a combination of both. Post hoc analyses revealed that males show a slightly higher number of panic attacks at the onset of treatment than females.

Derivation and Applicability of Asymptotic Results for Multiple Subtests Person-Fit Statistics

In high-stakes testing, it is important to check the validity of individual test scores. Although a test may, in general, result in valid test scores for most test takers, for some test takers, test scores may not provide a good description of a test taker's proficiency level. Person-fit statistics have been proposed to check the validity of individual test scores. In this study, the theoretical asymptotic sampling distribution of two person-fit statistics that can be used for tests that consist of multiple subtests is first discussed. Second, simulation study was conducted to investigate the applicability of this asymptotic theory for tests of finite length, in which the correlation between subtests and number of items in the subtests was varied. The authors showed that these distributions provide reasonable approximations, even for tests consisting of subtests of only 10 items each. These results have practical value because researchers do not have to rely on extensive simulation studies to simulate sampling distributions.

Comparison of Estimation Procedures for Multilevel AR(1) Models

To estimate a time series model for multiple individuals, a multilevel model may be used. In this paper we compare two estimation methods for the autocorrelation in Multilevel AR(1) models, namely Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo. Furthermore, we examine the difference between modeling fixed and random individual parameters. To this end, we perform a simulation study with a fully crossed design, in which we vary the length of the time series (10 or 25), the number of individuals per sample (10 or 25), the mean of the autocorrelation (-0.6 to 0.6 inclusive, in steps of 0.3) and the standard deviation of the autocorrelation (0.25 or 0.40). We found that the random estimators of the population autocorrelation show less bias and higher power, compared to the fixed estimators. As expected, the random estimators profit strongly from a higher number of individuals, while this effect is small for the fixed estimators. The fixed estimators profit slightly more from a higher number of time points than the random estimators. When possible, random estimation is preferred to fixed estimation. The difference between MLE and Bayesian estimation is nearly negligible. The Bayesian estimation shows a smaller bias, but MLE shows a smaller variability (i.e., standard deviation of the parameter estimates). Finally, better results are found for a higher number of individuals and time points, and for a lower individual variability of the autocorrelation. The effect of the size of the autocorrelation differs between outcome measures.

Flexibility in use

Purpose

Despite their growing popularity among organisations, satisfaction with activity-based work (ABW) environments is found to be below expectations. Research also suggests that workers typically do not switch frequently, or not at all, between different activity settings. Hence, the purpose of this study is to answer two main questions: Is switching behaviour related to satisfaction with ABW environments? Which factors may explain switching behaviour?

Design/methodology/approach

Questionnaire data provided by users of ABW environments (n = 3,189) were used to carry out ANOVA and logistic regression analyses.

Findings

Satisfaction ratings of the 4 per cent of the respondents who switched several times a day appeared to be significantly above average. Switching frequency was found to be positively related to heterogeneity of the activity profile, share of communication work and external mobility.

Practical implications

Our findings suggest that satisfaction with ABW environments might be enhanced by stimulating workers to switch more frequently. However, as strong objections against switching were observed and switching frequently does not seem to be compatible with all work patterns, this will presumably not work for everyone. Many workers are likely to be more satisfied if provided with an assigned (multifunctional) workstation.

Originality/value

In a large representative sample, clear evidence was found for relationships between behavioural aspects and appreciation of ABW environments that had not been studied previously.

A comparative simulation study of AR(1) estimators in short time series

Various estimators of the autoregressive model exist. We compare their performance in estimating the autocorrelation in short time series. In Study 1, under correct model specification, we compare the frequentist r₁ estimator, C-statistic, ordinary least squares estimator (OLS) and maximum likelihood estimator (MLE), and a Bayesian method, considering flat (B_f) and symmetrized reference (B_sr) priors. In a completely crossed experimental design we vary lengths of time series (i.e., T = 10, 25, 40, 50 and 100) and autocorrelation (from −0.90 to 0.90 with steps of 0.10). The results show a lowest bias for the B_sr, and a lowest variability for r₁. The power in different conditions is highest for B_sr and OLS. For T = 10, the absolute performance of all measurements is poor, as expected. In Study 2, we study robustness of the methods through misspecification by generating the data according to an ARMA(1,1) model, but still analysing the data with an AR(1) model. We use the two methods with the lowest bias for this study, i.e., B_sr and MLE. The bias gets larger when the non-modelled moving average parameter becomes larger. Both the variability and power show dependency on the non-modelled parameter. The differences between the two estimation methods are negligible for all measurements.

Severity of borderline personality symptoms in adolescence: relationship with maternal parenting stress, maternal psychopathology, and rearing styles

The development of borderline personality disorder (BPD) has been associated with parenting styles and parental psychopathology. Only a few studies have examined current parental rearing styles and parental psychopathology in relationship to BPD symptoms in adolescents. Moreover, parenting stress has not been examined in this group. The current study examined 101 adolescents (14-19 years old) with BPD symptoms and their mothers. Assessments were made on severity of BPD symptoms, youth-perceived maternal rearing styles, and psychopathology and parenting stress in mothers. Multiple regression analyses were used to examine potential predictors of borderline severity. No correlation was found between severity of BPD symptoms in adolescents and parenting stress. Only youth-perceived maternal overprotection was significantly related to BPD severity. The combination of perceived maternal rejection with cluster B traits in mothers was significantly related to BPD severity in adolescents. This study provides a contribution to the disentanglement of the developmental pathways that lead to BPD.

Measurement bias detection through Bayesian factor analysis

Measurement bias has been defined as a violation of measurement invariance. Potential violators—variables that possibly violate measurement invariance—can be investigated through restricted factor analysis (RFA). The purpose of the present paper is to investigate a Bayesian approach to estimate RFA models with interaction effects, in order to detect uniform and nonuniform measurement bias. Because modeling nonuniform bias requires an interaction term, it is more complicated than modeling uniform bias. The Bayesian approach seems especially suited for such complex models. In a simulation study we vary the type of bias (uniform, nonuniform), the type of violator (observed continuous, observed dichotomous, latent continuous), and the correlation between the trait and the violator (0.0, 0.5). For each condition, 100 sets of data are generated and analyzed. We examine the accuracy of the parameter estimates and the performance of two bias detection procedures, based on the DIC fit statistic, in Bayesian RFA. Results show that the accuracy of the estimated parameters is satisfactory. Bias detection rates are high in all conditions with an observed violator, and still satisfactory in all other conditions.

Goodness of fit testing using a specific density estimate

To test the hypothesis H 0: f=ψ that an unknown density f is equal to a specified one, ψ, an estimate f^ of f is compared with ψ. The total variation distance ∥ f^-ψ∥1 is used as test statistic.

The density estimate f^ considered is a peculiar one. A table of critical values is provided, this table is applicable for arbitrary ψ.

Relations with other methods, Neyman´s smooth tests in particular, are discussed and power comparisons are performed. In certain situations, our test is recommendable. An example from practice is provided.

SIMAGE: simulation of DNA-microarray gene expression data

Background

Simulation of DNA-microarray data serves at least three purposes: (i) optimizing the design of an intended DNA microarray experiment, (ii) comparing existing pre-processing and processing methods for best analysis of a given DNA microarray experiment, (iii) educating students, lab-workers and other researchers by making them aware of the many factors influencing DNA microarray experiments.

Results

Our model has multiple layers of factors influencing the experiment. The relative influence of such factors can differ significantly between labs, experiments within labs, etc. Therefore, we have added a module to roughly estimate their parameters from a given data set. This guarantees that our simulated data mimics real data as closely as possible.

Conclusion

We introduce a model for the simulation of dual-dye cDNA-microarray data closely resembling real data and coin the model and its software implementation "SIMAGE" which stands for simulation of microarray gene expression data. The software is freely accessible at: .

A generally applicable validation scheme for the assessment of factors involved in reproducibility and quality of DNA-microarray data

BACKGROUND

In research laboratories using DNA-microarrays, usually a number of researchers perform experiments, each generating possible sources of error. There is a need for a quick and robust method to assess data quality and sources of errors in DNA-microarray experiments. To this end, a novel and cost-effective validation scheme was devised, implemented, and employed.

RESULTS

A number of validation experiments were performed on Lactococcus lactis IL1403 amplicon-based DNA-microarrays. Using the validation scheme and ANOVA, the factors contributing to the variance in normalized DNA-microarray data were estimated. Day-to-day as well as experimenter-dependent variances were shown to contribute strongly to the variance, while dye and culturing had a relatively modest contribution to the variance.

CONCLUSION

Even in cases where 90% of the data were kept for analysis and the experiments were performed under challenging conditions (e.g. on different days), the CV was at an acceptable 25%. Clustering experiments showed that trends can be reliably detected also from genes with very low expression levels. The validation scheme thus allows determining conditions that could be improved to yield even higher DNA-microarray data quality.

Combining microarrays and genetic analysis

Gene expression can be studied at a genome-wide scale with the aid of modern microarray technologies. Expression profiling of tens to hundreds of individuals in a genetic population can reveal the consequences of genetic variation. In this paper it is argued that the design and analysis of such a study is not a matter of simply applying the existing and more-or-less standard computational tools for microarrays to a new type of experimental data. It is shown how to fully exploit the power of genetics through optimal experimental design and analysis for two major microarray technologies, cDNA two-colour arrays and Affymetrix short oligonucleotide arrays.