scholarly journals Measuring motivational relationship processes in experience sampling: A reliability model for moments, days, and persons nested in couples

2021 ◽  
Author(s):  
Felix D. Schönbrodt ◽  
Caroline Zygar-Hoffmann ◽  
Steffen Nestler ◽  
Sebastian Pusch ◽  
Birk Hagemeyer
Keyword(s):  
Variance Components ◽  
Generalizability Theory ◽  
Relative Proportion ◽  
Experience Sampling ◽  
Process Models ◽  
Sampling Studies ◽  
Reliable Assessment ◽  
Relationship Processes ◽  
Level Scale ◽  
Variance Estimates

AbstractThe investigation of within-person process models, often done in experience sampling designs, requires a reliable assessment of within-person change. In this paper, we focus on dyadic intensive longitudinal designs where both partners of a couple are assessed multiple times each day across several days. We introduce a statistical model for variance decomposition based on generalizability theory (extending P. E. Shrout & S. P. Lane, 2012), which can estimate the relative proportion of variability on four hierarchical levels: moments within a day, days, persons, and couples. Based on these variance estimates, four reliability coefficients are derived: between-couples, between-persons, within-persons/between-days, and within-persons/between-moments. We apply the model to two dyadic intensive experience sampling studies (n1 = 130 persons, 5 surveys each day for 14 days, ≥ 7508 unique surveys; n2 = 508 persons, 5 surveys each day for 28 days, ≥ 47764 unique surveys). Five different scales in the domain of motivational processes and relationship quality were assessed with 2 to 5 items: State relationship satisfaction, communal motivation, and agentic motivation; the latter consists of two subscales, namely power and independence motivation. Largest variance components were on the level of persons, moments, couples, and days, where within-day variance was generally larger than between-day variance. Reliabilities ranged from .32 to .76 (couple level), .93 to .98 (person level), .61 to .88 (day level), and .28 to .72 (moment level). Scale intercorrelations reveal differential structures between and within persons, which has consequences for theory building and statistical modeling.

scholarly journals Measuring motivational relationship processes in experience sampling: A reliability model for moments, days, and persons nested in couples

2019 ◽  
Author(s):  
Felix D. Schönbrodt ◽  
Caroline Zygar-Hoffmann ◽  
Steffen Nestler ◽  
Sebastian Pusch ◽  
Birk Hagemeyer
Keyword(s):  
Variance Components ◽  
Generalizability Theory ◽  
Relative Proportion ◽  
Experience Sampling ◽  
Process Models ◽  
Sampling Studies ◽  
Reliable Assessment ◽  
Relationship Processes ◽  
Level Scale ◽  
Variance Estimates

The investigation of within-person process models, often done in experience sampling designs, requires a reliable assessment of within-person change. In this paper, we focus on dyadic intensive longitudinal designs where both partners of a couple are assessed multiple times each day across several days. We introduce a statistical model for variance decomposition based on generalizability theory (extending P. E. Shrout & S. P. Lane, 2012), which can estimate the relative proportion of variability on four hierarchical levels: moments within a day, days, persons, and couples. Based on these variance estimates, three reliability coefficients are derived: between-persons, within-persons/between-days, and within-persons/between-moments. We apply the model to two dyadic intensive experience sampling studies (n_1 = 130 persons, 5 surveys each day for 14 days, >= 7.508 unique surveys; n_2 = 510 persons, 5 surveys each day for 28 days, >= 47.871 unique surveys). Five different scales in the domain of motivational processes and relationship quality were assessed with 2 to 5 items: State relationship satisfaction, communal motivation, and agentic motivation, which consist of two subscales, namely power and independence motivation. Largest variance components were on the level of persons, moments, couples, and days, where within-day variance was generally larger than between-day variance. Reliabilities ranged from .95 to .98 (person level), .52 to .86 (day level), and .28 to .70 (moment level). Scale intercorrelations reveal differential structures between and within persons, which has consequences for theory building and statistical modeling.

On Describing an Incidence Sample

1982 ◽  
Vol 7 (4) ◽  
pp. 311-331 ◽  
Author(s):  
Gwyneth M. Boodoo
Keyword(s):  
Variance Components ◽  
Generalizability Theory ◽  
Standard Errors ◽  
Eighth Grade ◽  
Eighth Grade Students ◽  
Competency Based ◽  
Mean And Variance ◽  
The Mean ◽  
Using Data ◽  
Variance Estimates

Parameters used to describe an incidence sample are estimated using the theory of generalized symmetric means and generalizability theory. The former is used to compute estimates of the mean and variance components in an ANOVA framework, while the latter is used in obtaining generalizability coefficients. Standard errors of the variance estimates are obtained. The procedure is illustrated using data from two competency-based tests given to eighth grade students in mathematics and reading.

Beliefs about stressors alter stressors’ impact: Evidence from two experience-sampling studies

2006 ◽  
Vol 59 (9) ◽  
pp. 1261-1285 ◽  
Author(s):  
Kevin Daniels ◽  
Ruth Hartley ◽  
Cheryl J. Travers
Keyword(s):  
Experience Sampling ◽  
Sampling Studies

Recommendations for Reporting Sample and Measurement Information in Experience Sampling Studies

2021 ◽  
Author(s):  
Eric D. Heggestad ◽  
Liana Kreamer ◽  
Mary M. Hausfeld ◽  
Charmi Patel ◽  
Steven G. Rogelberg
Keyword(s):  
Experience Sampling ◽  
Measurement Information ◽  
Sampling Studies

scholarly journals The affectability of writing assessment scores: a G-theory analysis of rater, task, and scoring method contribution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ali Khodi
Keyword(s):  
Writing Assessment ◽  
Generalizability Theory ◽  
Error Variance ◽  
Total Variance ◽  
Scoring Method ◽  
Writing Assessments ◽  
Assessment Scores ◽  
Peer Rating ◽  
Reliable Assessment ◽  
Writing Scores

AbstractThe present study attempted to to investigate  factors  which affect EFL writing scores through using generalizability theory (G-theory). To this purpose, one hundred and twenty students participated in one independent and one integrated writing tasks. Proceeding, their performances were scored by six raters: one self-rating,  three peers,-rating and two instructors-rating. The main purpose of the sudy was to determine the relative and absolute contributions of different facets such as student, rater, task, method of scoring, and background of education  to the validity of writing assessment scores. The results indicated three major sources of variance: (a) the student by task by method of scoring (nested in background of education) interaction (STM:B) with 31.8% contribution to the total variance, (b) the student by rater by task by method of scoring (nested in background of education) interaction (SRTM:B) with 26.5% of contribution to the total variance, and (c) the student by rater by method of scoring (nested in background of education) interaction (SRM:B) with 17.6% of the contribution. With regard to the G-coefficients in G-study (relative G-coefficient ≥ 0.86), it was also found that the result of the assessment was highly valid and reliable. The sources of error variance were detected as the student by rater (nested in background of education) (SR:B) and rater by background of education with 99.2% and 0.8% contribution to the error variance, respectively. Additionally, ten separate G-studies were conducted to investigate the contribution of different facets across rater, task, and methods of scoring as differentiation facet. These studies suggested that peer rating, analytical scoring method, and integrated writing tasks were the most reliable and generalizable designs of the writing assessments. Finally, five decision-making studies (D-studies) in optimization level were conducted and it was indicated that at least four raters (with G-coefficient = 0.80) are necessary for a valid and reliable assessment. Based on these results, to achieve the greatest gain in generalizability, teachers should have their students take two writing assessments and their performance should be rated on at least two scoring methods by at least four raters.

Does suppressing negative emotion impair subsequent emotions? Two experience sampling studies

2019 ◽  
Vol 44 (3) ◽  
pp. 427-435 ◽  
Author(s):  
Yan Ruan ◽  
Harry T. Reis ◽  
Wojciech Zareba ◽  
Richard D. Lane
Keyword(s):  
Negative Emotion ◽  
Experience Sampling ◽  
Sampling Studies

Differentiate to Regulate: Low Negative Emotion Differentiation Is Associated With Ineffective Use but Not Selection of Emotion-Regulation Strategies

2019 ◽  
Vol 30 (6) ◽  
pp. 863-879 ◽  
Author(s):  
Elise K. Kalokerinos ◽  
Yasemin Erbas ◽  
Eva Ceulemans ◽  
Peter Kuppens
Keyword(s):  
Emotion Regulation ◽  
Negative Emotion ◽  
Experience Sampling ◽  
Well Being ◽  
Emotion Regulation Strategies ◽  
Emotion Differentiation ◽  
Sampling Studies ◽  
Regulation Strategies ◽  
Selection Of

Emotion differentiation, which involves experiencing and labeling emotions in a granular way, has been linked with well-being. It has been theorized that differentiating between emotions facilitates effective emotion regulation, but this link has yet to be comprehensively tested. In two experience-sampling studies, we examined how negative emotion differentiation was related to (a) the selection of emotion-regulation strategies and (b) the effectiveness of these strategies in downregulating negative emotion ( Ns = 200 and 101 participants and 34,660 and 6,282 measurements, respectively). Unexpectedly, we found few relationships between differentiation and the selection of putatively adaptive or maladaptive strategies. Instead, we found interactions between differentiation and strategies in predicting negative emotion. Among low differentiators, all strategies (Study 1) and four of six strategies (Study 2) were more strongly associated with increased negative emotion than they were among high differentiators. This suggests that low differentiation may hinder successful emotion regulation, which in turn supports the idea that effective regulation may underlie differentiation benefits.

scholarly journals Estimation of additive, dominance and epistatic variance components using finite locus models implemented with a single-site Gibbs and a descent graph sampler

2000 ◽  
Vol 76 (2) ◽  
pp. 187-198 ◽  
Author(s):  
F.-X. DU ◽  
I. HOESCHELE
Keyword(s):  
Variance Components ◽  
Additive Genetic Variance ◽  
Genetic Parameter ◽  
Genetic Effects ◽  
Single Site ◽  
Phenotypic Data ◽  
Variance Components Estimation ◽  
Locus Number ◽  
Variance Estimates ◽  
Finite Locus

In a previous contribution, we implemented a finite locus model (FLM) for estimating additive and dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we extended the FLM to include two-locus epistasis, and implemented the analysis with two genotype samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and variable across loci, uniform and constant across loci, and normal). Phenotypic data were simulated for two pedigrees with 6300 and 12300 individuals in closed populations, using several different, non-additive genetic models. Replications of these data were analysed with FLMs differing in the number of loci. Simulation results indicate that the dependency of non-additive genetic variance estimates on locus number persisted in all implementation strategies we investigated. However, this dependency was considerably diminished with normal priors for genetic effects as compared with uniform priors (constant or variable across loci). Descent graph sampling of genotypes modestly improved variance components estimation compared with Gibbs sampling. Moreover, a larger pedigree produced considerably better variance components estimation, suggesting this dependency might originate from data insufficiency. As the FLM represents an appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion of polygenic background variation in QTL mapping analyses, further improvements are warranted and might be achieved via improvement of the sampler or treatment of the number of loci as an unknown.

Capturing Affective Experiences using the SMS Experience Sampling (SMS-ES) method

2011 ◽  
Vol 53 (4) ◽  
pp. 479-506 ◽  
Author(s):  
Lynda Andrews ◽  
Rebekah Russell Bennett ◽  
Judy Drennan
Keyword(s):  
Repeated Measures ◽  
Experience Sampling ◽  
Practical Implementation ◽  
Short Message ◽  
Quality Of Data ◽  
Sampling Studies ◽  
For Profit ◽  
Affective Experiences ◽  
Methodological Considerations ◽  
Message System

This paper reports the feasibility and methodological considerations of using the Short Message System Experience Sampling (SMS-ES) method, which is an experience sampling research method developed to assist researchers to collect repeat measures of consumers' affective experiences. The method combines SMS with web-based technology in a simple yet effective way. It is described using a practical implementation study that collected consumers' emotions in response to using mobile phones in everyday situations. The method is further evaluated in terms of the quality of data collected in the study, as well as against the methodological considerations for experience sampling studies. These two evaluations suggest that the SMS-ES method is both a valid and reliable approach for collecting consumers' affective experiences. Moreover, the method can be applied across a range of for-profit and not-for-profit contexts where researchers want to capture repeated measures of consumers' affective experiences occurring over a period of time. The benefits of the method are discussed, to assist researchers who wish to apply the SMS-ES method in their own research designs.

An Alternative Method for Estimating Variance Components in Generalizability Theory

1990 ◽  
Vol 66 (2) ◽  
pp. 379-386 ◽  
Author(s):  
George A. Marcoulides
Keyword(s):  
Maximum Likelihood ◽  
Analysis Of Variance ◽  
Variance Components ◽  
Generalizability Theory ◽  
Simulated Data ◽  
Restricted Maximum Likelihood ◽  
Alternative Method ◽  
Sample Data

This study compares, using simulated data, two methods for estimating variance components in generalizability (G) studies. Traditionally variance components are estimated from an analysis of variance of sample data. The alternative method for estimating variance components is restricted maximum likelihood (REML). The results indicate that REML provides estimates for the components in the various designs that are closer to the true parameters than the estimates from analysis of variance.

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