Empirical research inevitably includes constructing a data set by processing raw data into a form ready for statistical analysis. Data processing often involves choices among several reasonable ...options for excluding, transforming, and coding data. We suggest that instead of performing only one analysis, researchers could perform a multiverse analysis, which involves performing all analyses across the whole set of alternatively processed data sets corresponding to a large set of reasonable scenarios. Using an example focusing on the effect of fertility on religiosity and political attitudes, we show that analyzing a single data set can be misleading and propose a multiverse analysis as an alternative practice. A multiverse analysis offers an idea of how much the conclusions change because of arbitrary choices in data construction and gives pointers as to which choices are most consequential in the fragility of the result.
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BFBNIB, NMLJ, NUK, OILJ, PNG, SAZU, UKNU, UL, UM, UPUK
Over the years, many studies have demonstrated a relation between emotion dynamics and psychological well-being
. Because our emotional life is inherently time-dynamic
, affective scientists argue ...that, next to how positive or negative we feel on average, patterns of emotional change are informative for mental health
. This growing interest initiated a surge in new affect dynamic measures, each claiming to capture a unique dynamical aspect of our emotional life, crucial for understanding well-being. Although this accumulation suggests scientific progress, researchers have not always evaluated (a) how different affect dynamic measures empirically interrelate and (b) what their added value is in the prediction of psychological well-being. Here, we address these questions by analysing affective time series data from 15 studies (n = 1,777). We show that (a) considerable interdependencies between measures exist, suggesting that single dynamics often do not convey unique information, and (b) dynamic measures have little added value over mean levels of positive and negative affect (and variance in these affective states) when predicting individual differences in three indicators of well-being (life satisfaction, depressive symptoms and borderline symptoms). Our findings indicate that conventional emotion research is currently unable to demonstrate independent relations between affect dynamics and psychological well-being.
People display a remarkable variability in the patterns and trajectories with which their feelings change over time. In this article, we present a theoretical account for the dynamics of affect ...(DynAffect) that identifies the major processes underlying individual differences in the temporal dynamics of affective experiences. It is hypothesized that individuals are characterized by an affective home base, a baseline attractor state around which affect fluctuates. These fluctuations vary as the result of internal or external processes to which an individual is more or less sensitive and are regulated and tied back to the home base by the attractor strength. Individual differences in these 3 processes-affective home base, variability, and attractor strength-are proposed to underlie individual differences in affect dynamics. The DynAffect account is empirically evaluated by means of a diffusion modeling approach in 2 extensive experience-sampling studies on people's core affective experiences. The findings show that the model is capable of adequately capturing the observed dynamics in core affect across both large (Study 1) and shorter time scales (Study 2) and illuminate how the key processes are related to personality and emotion dispositions. Implications for the understanding of affect dynamics and affective dysfunctioning in psychopathology are also discussed.
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CEKLJ, FFLJ, NUK, ODKLJ, PEFLJ, UPUK
Abstract Background The symptoms for Major Depression (MD) defined in the DSM-5 differ markedly from symptoms assessed in common rating scales, and the empirical question about core depression ...symptoms is unresolved. Here we conceptualize depression as a complex dynamic system of interacting symptoms to examine what symptoms are most central to driving depressive processes. Methods We constructed a network of 28 depression symptoms assessed via the Inventory of Depressive Symptomatology (IDS-30) in 3,463 depressed outpatients from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. We estimated the centrality of all IDS-30 symptoms, and compared the centrality of DSM and non-DSM symptoms; centrality reflects the connectedness of each symptom with all other symptoms. Results A network with 28 intertwined symptoms emerged, and symptoms differed substantially in their centrality values. Both DSM symptoms (e.g., sad mood) and non-DSM symptoms (e.g., anxiety) were among the most central symptoms, and DSM criteria were not more central than non-DSM symptoms. Limitations Many subjects enrolled in STAR*D reported comorbid medical and psychiatric conditions which may have affected symptom presentation. Conclusion The network perspective neither supports the standard psychometric notion that depression symptoms are equivalent indicators of MD, nor the common assumption that DSM symptoms of depression are of higher clinical relevance than non-DSM depression symptoms. The findings suggest the value of research focusing on especially central symptoms to increase the accuracy of predicting outcomes such as the course of illness, probability of relapse, and treatment response.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Intra-individual processes are thought to continuously unfold across time. For equally spaced time intervals, the discrete-time lag-1 vector autoregressive (VAR(1)) model and the continuous-time ...Ornstein-Uhlenbeck (OU) model are equivalent. It is expected that by taking into account the unequal spacings of the time intervals in real data between observations will lead to an advantage for the OU in terms of predictive accuracy. In this paper, this is claim is being investigated by comparing the predictive accuracy of the OU model to that of the VAR(1) model on typical ESM data obtained in the context of affect research. It is shown that the VAR(1) model outperforms the OU model for the majority of the time series, even though time intervals in the data are unequally spaced. Accounting for measurement error does not change the result. Deleting large abrupt changes on short time intervals (that may be caused by externally driven events) does however lead to a significant improvement for the OU model. This suggests that processes in psychology may be continuously evolving, but that there are factors, like external events, which can disrupt the continuous flow.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
In the network approach to psychopathology, disorders are conceptualized as networks of mutually interacting symptoms (e.g., depressed mood) and transdiagnostic factors (e.g., rumination). This ...suggests that it is necessary to study how symptoms dynamically interact over time in a network architecture. In the present paper, we show how such an architecture can be constructed on the basis of time-series data obtained through Experience Sampling Methodology (ESM). The proposed methodology determines the parameters for the interaction between nodes in the network by estimating a multilevel vector autoregression (VAR) model on the data. The methodology allows combining between-subject and within-subject information in a multilevel framework. The resulting network architecture can subsequently be analyzed through network analysis techniques. In the present study, we apply the method to a set of items that assess mood-related factors. We show that the analysis generates a plausible and replicable network architecture, the structure of which is related to variables such as neuroticism; that is, for subjects who score high on neuroticism, worrying plays a more central role in the network. Implications and extensions of the methodology are discussed.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Retrospective analyses of experience sampling (ESM) data have shown that changes in mean and variance levels may serve as early warning signs of an imminent depression. Detecting such early warning ...signs prospectively would pave the way for timely intervention and prevention. The exponentially weighted moving average (EWMA) procedure seems a promising method to scan ESM data for the presence of mean changes in real-time. Based on simulation and empirical studies, computing and monitoring day averages using EWMA works particularly well. We therefore expand this idea to the detection of variance changes and propose to use EWMA to prospectively scan for mean changes in day variability statistics (i.e.,
s
2
,
s
, ln(
s
)). When both mean and variance changes are of interest, the multivariate extension of EWMA (MEWMA) can be applied to both the day averages and a day statistic of variability. We evaluate these novel approaches to detecting variance changes by comparing them to EWMA-type procedures that have been specifically developed to detect a combination of mean and variance changes in the raw data: EWMA-
S
2
, EWMA-ln(
S
2
), and EWMA-
X
¯
-
S
2
. We ran a simulation study to examine the performance of the two approaches in detecting mean, variance, or both types of changes. The results indicate that monitoring day statistics using (M)EWMA works well and outperforms EWMA-
S
2
and EWMA-ln(
S
2
); the performance difference with EWMA-
X
¯
-
S
2
is smaller but notable. Based on the results, we provide recommendations on which statistic of variability to monitor based on the type of change (i.e., variance increase or decrease) one expects.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
How feelings change over time is a central topic in emotion research. To study these affective fluctuations, researchers often ask participants to repeatedly indicate how they feel on a self-report ...rating scale. Despite widespread recognition that this kind of data is subject to measurement error, the extent of this error remains an open question. Complementing many daily-life studies, this study aimed to investigate this question in an experimental setting. In such a setting, multiple trials follow each other at a fast pace, forcing experimenters to use a limited number of questions to measure affect during each trial. A total of 1398 participants completed a probabilistic reward task in which they were unknowingly presented with the same string of outcomes multiple times throughout the study. This allowed us to assess the test–retest consistency of their affective responses to the rating scales under investigation. We then compared these consistencies across different types of rating scales in hopes of finding out whether a given type of scale led to a greater consistency of affective measurements. Overall, we found moderate to good consistency of the affective measurements. Surprisingly, however, we found no differences in consistency across rating scales, which suggests that the specific rating scale that is used does not influence the measurement consistency.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Differential item functioning (DIF) is an important issue of interest in psychometrics and educational measurement. Several methods have been proposed in recent decades for identifying items that ...function differently between two or more groups of examinees. Starting from a framework for classifying DIF detection methods and from a comparative overview of the most traditional methods, an R package for nine methods, called
difR
, is presented. The commands and options are briefly described, and the package is illustrated through the analysis of a data set on verbal aggression.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, ODKLJ, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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