We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent—but mostly unresolved—issues in the area of dynamic multilevel modeling.

https://www.tandfonline.com/doi/pdf/10.1080/00273171.2018.1446819

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study.

This 4-year, multi-informant longitudinal study (N = 480, initial age: 15) investigated the interplay between parental support, behavioral and psychological control, and adolescents' emotion regulation development. We examined reciprocal effects between parents and children, mothers' versus fathers' unique roles in emotion regulation development, and sex differences. Multi-informant data allowed us to compare effects of adolescent-perceived and parent-reported parenting. Finally, innovative analyses allowed us to disentangle between-family differences from within-family predictive processes. Parenting and emotion regulation were associated at the between-family and within-family levels, especially according to adolescent reports. Support primarily played a role between mothers and adolescents, and perceived behavioral control between fathers and adolescents. Sex moderation revealed that support played a more prominent role in mother-daughter than mother-son relationships, and that daughters experienced greater behavioral control. Child effects outnumbered parent effects, which might reflect the increasing equality of adolescent-parent relationships. Finally, adolescent-perceived parenting was a stronger correlate of emotion regulation than parent-reports, suggesting that adolescents' perceptions are a relevant source of information for research and practice. Consistent with the self-determination theory perspective on parenting, emotion regulation flourished when adolescents felt like mothers provided support, and fathers loosened behavioral control. These results are in line with the notion that mother-child relationships are supportive attachment relationships, whereas fathers provide "activation" relationships, challenging adolescents to regulate emotions autonomously by providing less explicit structure. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The role of fathers' versus mothers' parenting in emotion-regulation development from mid-late adolescence: Disentangling between-family differences from within-family effects.

The use of ambulatory assessment (AA) and related methods (experience sampling, ecological momentary assessment) has greatly increased within the field of adolescent psychology. In this guide, we describe important practices for conducting AA studies in adolescent samples. To better understand how researchers have been implementing AA study designs, we present a review of 23 AA studies that were conducted in adolescent samples from 2017. Results suggest that there is heterogeneity in how AA studies in youth are conducted and reported. Based on these insights, we provide recommendations with regard to participant recruitment, sampling scheme, item selection, power analysis, and software choice. Further, we provide a checklist for reporting on AA studies in adolescent samples that can be used as a guideline for future studies.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jora.12471

A Review of Current Ambulatory Assessment Studies in Adolescent Samples and Practical Recommendations.

Dynamic structural equation modeling (DSEM) is a novel, intensive longitudinal data (ILD) analysis framework. DSEM models intraindividual changes over time on Level 1 and allows the parameters of these processes to vary across individuals on Level 2 using random effects. DSEM merges time series, structural equation, multilevel, and time-varying effects models. Despite the well-known properties of these analysis areas by themselves, it is unclear how their sample size requirements and recommendations transfer to the DSEM framework. This article presents the results of a simulation study that examines the estimation quality of univariate 2-level autoregressive models of order 1, AR(1), using Bayesian analysis in Mplus Version 8. Three features are varied in the simulations: complexity of the model, number of subjects, and number of time points per subject. Samples with many subjects and few time points are shown to perform substantially better than samples with few subjects and many time points.

Number of Subjects and Time Points Needed for Multilevel Time-Series Analysis: A Simulation Study of Dynamic Structural Equation Modeling

This article considers experimental design based on the strategy of rerandomization to increase the efficiency in experiments. Two aspects of rerandomization are addressed. First, we propose a two-...

Rerandomization Strategies for Balancing Covariates Using Pre-Experimental Longitudinal Data

Studies suggest that householders who turn prosumers become more energy aware, change their pattern of electricity use and may even start to engage in other pro-environmental activities. However, few of these studies were equipped to investigate such causal relationships—mainly because most prosumers are inherently self-selected. However, as real estate companies and building owners have begun installing photovoltaics on their customers’ behalf, a new breed of non-self-selected prosumers is emerging, which presents new opportunities to address questions of causality and thus improve our understanding of the possible implications of a more prosumer-dense future. Using a sample of 54 apartment households with a shared rooftop PV installation, this article presents the first causal analysis on non-self-selected prosumers’ response to information about having become prosumers, what that means for themselves and the collective they are part of, how to self-consume solar electricity and why they should do so. Using a stepped wedge design and stratified randomization procedure we were able to design an experimental study with sufficient power. A panel regression model and various statistical analyses on pre and post treatment survey- and electricity use data were used to evaluate the intervention. In line with studies of self-selected prosumers, the self-reported measures suggest that householders have shifted the use of major appliances to increase their self-consumption. However, based on the electricity use data, we find no evidence of a such a shift and no indications of spillovers to other pro-environmental behaviours—highlighting the need to use multiple measures to assess behavioural change.

Mårten Schultzberg

Papers

Number of Subjects and Time Points Needed for Multilevel Time-Series Analysis: A Simulation Study of Dynamic Structural Equation Modeling

Rerandomization Strategies for Balancing Covariates Using Pre-Experimental Longitudinal Data

Rising with the sun? Encouraging solar electricity self-consumption among apartment owners in Sweden

Identifying and estimating the effects of a mandatory billing demand charge

On optimal rerandomization designs