Sentences with phrase «dyadic data»

This general approach — to first quantify the intradyad relationships and then examine interdyad differences in the intradyad relationships — is the basis for most contemporary dyadic data analysis techniques, including sequential and state space grid analyses, coupled dynamic systems, and multilevel modeling (Bakeman & Gottman, 1997; Bakeman & Quera, 2011; Boker & Laurenceau, 2007; Gonzalez & Griffin, 2012; Gottman, Murray, Swanson, Tyson, & Swanson, 2002; Hollenstein, 2013; Laurenceau & Bolger, 2005; Ram & Pedersen, 2008).

Using dyadic data from 108 older couples (MAge = 75.18 years) with six within - day emotion and activity reports over 7 days, we illustrate how grid - sequence analysis can be used to identify a taxonomy of dyads with different emotion dynamics.

To assess couples» attitudes, and associated factors toward using CVCT, a cross-sectional study design was used with a novel Internet - based recruitment method to collect dyadic data from a national sample of 275 HIV - negative gay couples.

Topics to be addressed include the measurement of nonindependence, the Actor - Partner Interdependence Model, the analysis of distinguishable and indistinguishable dyads, and the analysis of over-time dyadic data (e.g., dyadic growth curve models).

The collection and analysis of dyadic data present additional complexities compared to the study of individuals.

This study utilized dyadic data of 239 married couples from three waves of a longitudinal study on work and family issues conducted in Taiwan.

Dyadic data analysis with structural equation modeling is used to determine the respective contributions of each respondent's predictors (i.e., actor effects) and his / her spouse's or partner's predictors (i.e., partner effects).

SEM is one of the most commonly used data - analytic techniques for dyadic data.

[book] Kenny, D. A. / 2009 / Dyadic data analysis using multilevel modeling, In The handbook of multilevel analysis / Taylor Francis

Multilevel modeling with dyadic data from 142 couples was used to identify the characteristics associated with men who have had UAI with both their main partner and a casual MSM partner within the same timeframe.

Dyadic data analyses also revealed that when prosocial behavior was low, aggression was negatively... related to friendship quality.

Dyadic data analyses also revealed that when prosocial behavior was low, aggression was negatively

Finally, because his substantive interests involve processes that occur within relationships and therefore often involve non-independent data, Dr. Ackerman is particularly interested in analytic models for both cross-sectional and longitudinal dyadic data.

Working with dyadic data in studies of emerging adulthood: Specific recommendations, general advice, and practical tips.

Dyadic data analyses in a developmental context.

Our cross-sectional study used dyadic data from 142 gay male couples to assess actor — partner effects of relationship commitment, trust, and investment in one's sexual agreement for HIV risk.

The association between family factors and child behaviour problems using dyadic data.

105 David A. Kenny et al., Dyadic Data Analysis 78 — 79 (2006).

We account for the dependence structure of the dyadic data (i.e., the fact that each fMRI subject is involved in multiple dyads), which would otherwise underestimate the standard errors and increase the risk of type 1 error20, by clustering simultaneously on both members of each dyad21, 22.

Not exact matches

To gain insight into what brain regions may be driving the relationship between social distance and overall neural similarity, we performed ordered logistic regression analyses analogous to those described above independently for each of the 80 ROIs, again using cluster - robust standard errors to account for dyadic dependencies in the data.

His work includes the application of cross-spectral analysis and bootstrapping methods to dyadic sexual desire data.

Measures included demographic data, the Dyadic Adjustment Scale, the Parents Rating of Program Effectiveness, and the Areas of Change in Parenting Scale.

Because the data were dyadic (i.e., data from both members of the pair), mean couple scores were created for each variable, and these couple mean scores were used in the analyses below.

Data was also obtained from the Dyadic Parent - Child Interactive Coding System which allows recording of behaviors of children with conduct problems and their parents, and the Coder Impression Inventory, which describes parenting style, child affect, and behavior.

Multilevel modeling of data from 158 couples revealed that, at baseline, dyadic adjustment moderated the association between blame and distress for patients but not spouses (p < 0.05).

To take into account the dyadic structure in this data set, we could have specified two related TAR models, one for the husbands and one for the wives.

Multilevel modeling of data from 158 couples revealed that baseline spouses» reports of caregiving - related health problems were significantly associated with 3 - month (p < 0.001) and 6 - month (p = 0.01) follow - up distress in both patients and spouses even when controlling for baseline distress and dyadic adjustment.

At the first stage of assessment self - report questionnaires were administered to examine the presence of maternal psychiatric symptoms (SCL -90-R), perceived social support (MSPSS), and marital adjustment (Dyadic Adjustment Scale); dyadic interactions were observed and rated with the Emotional Availability Scales (Biringen, 2008) at each stage of data colleDyadic Adjustment Scale); dyadic interactions were observed and rated with the Emotional Availability Scales (Biringen, 2008) at each stage of data colledyadic interactions were observed and rated with the Emotional Availability Scales (Biringen, 2008) at each stage of data collection.

Behavioral data were skewed at both GCRC admissions; during the social support interactions, 2 or fewer of the total dyadic behaviors were categorized as hostile in 56.1 % of couples (range, 0 - 27).

In addition, a dyadic approach was used that included data from both the support - seeking and the support - providing spouse.

A wide variety of procedures are used to analyze dyadic longitudinal data, most of which distinguish dyad members (e.g., husbands, wives) and quantify time - dependent relationships between them (see Kenny et al., 2006 for overview).

With observational or experience sampling designs, dyadic longitudinal data are often placed in a multilevel / hierarchical framework that explicitly separates intradyad and interdyad associations.

In this initial demonstration, we apply grid - sequence analysis to dyadic experience sampling data obtained in a study of older couples» daily lives.

In particular, as a new method for the analysis of dyadic experience sampling data, we suggest that grid - sequence analysis will help identify new typologies of dyad - level microdynamics that indicate risk or protective factors that are useful for intervention efforts.