In recent years, the popularity of procedures for collecting intensive longitudinal data, such as experience-sampling method, has increased greatly. The data collected using designs allow researchers to study dynamics psychological functioning and how these differ across individuals. To this end, are often modeled with multilevel regression models. An important question that arises when design studies is determine number participants needed test specific hypotheses regarding parameters...

We address the question of equivalence between modeling results obtained on intra-individual and inter-individual levels psychometric analysis. Our focus is concept measurement invariance role it may play in this context. discuss general against background latent variable paradigm, complemented by an operational demonstration terms a linear state-space model, i.e., time series model with variables. Implemented multiple-occasion multiple-subject setting, simultaneously accounts for...

Reduced moment-to-moment blood oxygen level-dependent (BOLD) signal variability has been consistently linked to advanced age and poorer cognitive performance, showing potential as a functional marker of brain aging. To date, however, this promise rested exclusively on cross-sectional comparisons. In sample 74 healthy adults, we provide the first longitudinal evidence linking individual differences in BOLD variability, age, performance across multiple domains over an average period 2.5 years....

Abstract Long-lived simultaneous changes in the autodependency of dynamic system variables characterize crucial events as epileptic seizures and volcanic eruptions are expected to precede psychiatric conditions. To understand predict such phenomena, methods needed that detect multivariate time series. We put forward two methods: First, we propose KCP-AR, a novel adaptation general-purpose KCP (Kernel Change Point) method. Whereas is implemented on raw data does not shed light which parameter...

Autoregressive and vector autoregressive models are a driving force in current psychological research. In affect research they are, for instance, frequently used to formalize affective processes estimate dynamics. Discrete-time model variants most commonly used, but continuous-time formulations gaining popularity, because can handle data from longitudinal studies which the sampling rate varies within study period, yield results that be compared across sets with different rates. However,...

Much of recent affect research relies on intensive longitudinal studies to assess daily emotional experiences. The resulting data are analyzed with dynamic models capture regulatory processes involved in functioning. Daily contexts, however, commonly ignored. This may not only result biased parameter estimates and wrong conclusions, but also ignores the opportunity investigate contextual effects dynamics. With fixed moderated time series analysis, we present an approach that resolves this...

In recent years the popularity of procedures to collect intensive longitudinal data, such as Experience Sampling Method, has immensely increased. The data collected using designs allow researchers study dynamics psychological functioning, and how these differ across individuals. To this end, are often modeled with multilevel regression models. An important question that arises when designing studies is determine number participants needed test specific hypotheses regarding parameters models...

The longitudinal actor–partner interdependence model (L-APIM) is used to study actor and partner effects, both linear curvilinear, in dyadic intensive data. A burning question how conduct power analyses for different L-APIM variants. In this paper, we introduce an accessible analysis application, called PowerLAPIM, provide a hands-on tutorial conducting simulation-based 32 With target the number of dyads needed, but not repeated measurements partners (which often fixed studies). PowerLAPIM...

The multilevel autoregressive (MLAR) model is a popular method for quantifying affective inertia, the resistance of processes to change, using experience sampling (ESM) data. Although missed observations are common in ESM, their effect on estimation performance MLAR remains underexplored. We investigate compliance level, specific missingness mechanisms (i.e., missing completely at random versus tail-based missingness), and temporal patterns (whether or not missings occur consecutively) bias...

Affect dynamics are often studied by means of first-order autoregressive (AR) modeling applied to intensive longitudinal data. A key target in these studies is the AR parameter, which tied conceptually regulatory behavior affective process. The data typically gathered using experience sampling methods, designed pick up on fluctuations variables as they evolve over time naturalistic settings. In this manuscript, we compare classical time-contingent designs episode-contingent designs, initiate...

Affect dynamics are often studied by means of first-order autoregressive (AR) modeling applied to intensive longitudinal data. A key target in these studies is the AR parameter, which tied conceptually regulatory behavior affective process. The data typically gathered using experience sampling methods, designed pick up on fluctuations variables as they evolve over time naturalistic settings. In this manuscript, we compare classical time-contingent designs episode-contingent designs, initiate...

Affect dynamics are often studied by means of first-order autoregressive (AR) modeling applied to intensive longitudinal data. A key target in these studies is the AR parameter, which tied conceptually regulatory behavior affective process. The data typically gathered using experience sampling methods, designed pick up on fluctuations variables as they evolve over time naturalistic settings. In this article, we compare classical time-contingent designs episode-contingent designs, initiate...

Abstract Research investigating the effects of trauma exposure on brain structure and function in adults has mainly focused post-traumatic stress disorder (PTSD), whereas trauma-exposed individuals without a clinical diagnoses often serve as controls. However, this assumes dichotomy between subclinical populations that may not be supported at neural level. In current study we investigate whether repeated or long-term sample are similar to previous PTSD neuroimaging findings. We assessed 27...

Serial dependence is present in most time series data sets collected psychological research. This paper investigates the implications of various approaches for handling such serial dependence, when one interested linear effect a time-varying covariate on criterion. Specifically, either neglected, corrected by specifying autocorrelated residuals, or modeled including lagged version criterion as an additional predictor. Using both empirical and simulated data, we showcase that obtained results...

First-order autoregressive models are popular to assess the temporal dynamics of a univariate process. Researchers often extend these include time-varying covariates, such as contextual factors, investigate how they moderate processes' dynamics. We demonstrate that doing so has implications for well one can estimate and covariate effects, serial dependence in variables imply predictor collinearity. This is noteworthy contribution, since current practice rarely considered important. first...

Affect dynamics are often studied by means of first-order autoregressive (AR) modeling applied to intensive longitudinal data. A key target in these studies is the AR parameter, which tied conceptually regulatory behavior affective process. The data typically gathered using experience sampling methods, designed pick up on fluctuations variables as they evolve over time naturalistic settings. In this manuscript, we compare classical time-contingent designs episode-contingent designs, initiate...

Autoregressive and vector autoregressive models are a driving force in current psychological research. In affect research they for instance frequently used to formalize affective processes estimate dynamics. Discrete-time model variants most commonly used, but continuous-time formulations gaining popularity, because can handle data from longitudinal studies which the sampling rate varies within study period, yield results that be compared across sets with different rates. However, whether...

Nowadays research into affect frequently employs intensive longitudinal data to assess fluctuations in daily emotional experiences. The resulting are often analyzed with moderated autoregressive models capture the influences of contextual events on emotion dynamics. presence noise (e.g., measurement error) measures events, however, is commonly ignored these models. Disregarding covariates when it present may result biased parameter estimates and wrong conclusions drawn about underlying In a...

The longitudinal actor-partner interdependence model (L-APIM) is used to study actor and partner effects, both linear curvilinear, in dyadic intensive data. A burning question how conduct power analyses for different L-APIM variants. In this paper, we introduce an accessible analysis application, called PowerLAPIM, provide a hands-on tutorial conducting simulation-based 32 With target the number of dyads needed, but not repeated measurements partners (which often fixed studies). PowerLAPIM...