scholarly journals A guide for multilevel modeling of dyadic data with binary outcomes using SAS PROC NLMIXED

2006 ◽  
Vol 50 (12) ◽  
pp. 3663-3680 ◽  
Author(s):  
James M. McMahon ◽  
Enrique R. Pouget ◽  
Stephanie Tortu
Keyword(s):  
Multilevel Modeling ◽  
Binary Outcomes ◽  
Dyadic Data ◽  
Proc Nlmixed

A Multilevel Modeling Approach to Examine Incongruent Illness Appraisals in Family Care Dyads Over Time

2020 ◽  
Vol 26 (3) ◽  
pp. 229-239
Author(s):  
Karen S. Lyons ◽  
Christopher S. Lee
Keyword(s):  
Multilevel Modeling ◽  
Multilevel Models ◽  
Family Care ◽  
Dyadic Data ◽  
Research And Practice ◽  
Factors Associated ◽  
The Past ◽  
Family Nursing ◽  
Clinical Questions ◽  
Over Time

Over the past two decades, there has been movement toward a dyadic perspective of the illness experience. Although multilevel models have led to great insights into how dyads are affected by illness as family units, these models are still underutilized for understanding incongruent illness appraisals. Such incongruent appraisals can have implications for how the dyad collaborates to manage illness, the health of the dyad, and clinical outcomes. The focus of this article is to describe and promote the application of multilevel models to longitudinal dyadic data to understand incongruent illness appraisals over time. In particular, we present a data exemplar so researchers can apply these models to their own data and clinical questions to understand the ways care dyads converge and diverge in their appraisals and determine factors associated with such variability. We comment on the implications and extensions of these models for family nursing research and practice.

scholarly journals Multilevel Modeling of Binary Outcomes with Three-Level Complex Health Survey Data

2017 ◽  
Vol 07 (01) ◽  
pp. 27-43 ◽  
Author(s):  
Shafquat Rozi ◽  
Sadia Mahmud ◽  
Gillian Lancaster ◽  
Wilbur Hadden ◽  
Gregory Pappas
Keyword(s):  
Multilevel Modeling ◽  
Survey Data ◽  
Health Survey ◽  
Binary Outcomes ◽  
Health Survey Data

Narcissism and Perceived Inequity in Attractiveness in Romantic Relationships

2011 ◽  
Vol 16 (4) ◽  
pp. 295-302 ◽  
Author(s):  
Elke Rohmann ◽  
Hans-Werner Bierhoff ◽  
Martina Schmohr
Keyword(s):  
Romantic Relationships ◽  
Self Esteem ◽  
Gender Effect ◽  
Dyadic Analysis ◽  
Dyadic Data ◽  
Actor Partner Interdependence Model ◽  
Narcissistic Personality ◽  
Intraclass Correlations ◽  
Narcissistic Personality Inventory ◽  
Additional Variance

In three studies of romantic relationships (N = 253, N = 81, and N = 98) the hypothesis was tested that high narcissists, relative to low narcissists, distort the assessment of equity in attractiveness. Narcissism was measured by the Narcissistic Personality Inventory. In Study 1 the hypothesis was confirmed. In Study 2 it was shown that although narcissism correlated significantly with self-esteem, it was the unique variance in narcissism which predicted the tendency to feel underbenefited in respect to attractiveness. Finally in Study 3, dyadic data were analyzed on the basis of the Actor-Partner Interdependence Model. The data of 49 couples who lived together were included. The dyadic analysis indicated that actor narcissism exerted the expected influence on perceived inequity in attractiveness, whereas partner narcissism explained no additional variance. High narcissists felt more underbenefited than low narcissists. The analysis of dyadic data in Study 3 indicates that the link between narcissism and equity in attractiveness turns out to be an intrapersonal phenomenon because only actor narcissism, not partner narcissism, is significantly correlated with perceived inequity. In addition, partial intraclass correlations revealed that if one partner tended to feel underbenefited, the other partner tended to feel overbenefited. The results are explained on the basis of the agentic model of narcissism. All three studies consistently revealed a gender effect indicating that women felt more underbenefited than men in terms of attractiveness.

Power Values and Power Distance Moderate the Relationship Between Workplace Supervisory Power and Job Satisfaction

2020 ◽  
Vol 19 (3) ◽  
pp. 135-141
Author(s):  
Kenneth D. Locke
Keyword(s):  
Job Satisfaction ◽  
Multilevel Modeling ◽  
Power Distance ◽  
European Social Survey ◽  
Social Survey ◽  
The Relationship ◽  
Workplace Power ◽  
Over Time

Abstract. Person–job (or needs–supplies) discrepancy/fit theories posit that job satisfaction depends on work supplying what employees want and thus expect associations between having supervisory power and job satisfaction to be more positive in individuals who value power and in societies that endorse power values and power distance (e.g., respecting/obeying superiors). Using multilevel modeling on 30,683 European Social Survey respondents from 31 countries revealed that overseeing supervisees was positively associated with job satisfaction, and as hypothesized, this association was stronger among individuals with stronger power values and in nations with greater levels of power values or power distance. The results suggest that workplace power can have a meaningful impact on job satisfaction, especially over time in individuals or societies that esteem power.

Translocation Relative to Range

Methodology ◽  
2008 ◽  
Vol 4 (3) ◽  
pp. 132-138 ◽  
Author(s):  
Michael Höfler
Keyword(s):  
Risk Difference ◽  
Binary Outcomes ◽  
Number Needed To Treat ◽  
Likert Scales ◽  
Psychological Science ◽  
The Absolute ◽  
The Difference ◽  
Variance Parameters ◽  
Standardized Index ◽  
Maximum Possible Magnitude

A standardized index for effect intensity, the translocation relative to range (TRR), is discussed. TRR is defined as the difference between the expectations of an outcome under two conditions (the absolute increment) divided by the maximum possible amount for that difference. TRR measures the shift caused by a factor relative to the maximum possible magnitude of that shift. For binary outcomes, TRR simply equals the risk difference, also known as the inverse number needed to treat. TRR ranges from –1 to 1 but is – unlike a correlation coefficient – a measure for effect intensity, because it does not rely on variance parameters in a certain population as do effect size measures (e.g., correlations, Cohen’s d). However, the use of TRR is restricted on outcomes with fixed and meaningful endpoints given, for instance, for meaningful psychological questionnaires or Likert scales. The use of TRR vs. Cohen’s d is illustrated with three examples from Psychological Science 2006 (issues 5 through 8). It is argued that, whenever TRR applies, it should complement Cohen’s d to avoid the problems related to the latter. In any case, the absolute increment should complement d.

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