scholarly journals Testing similarity effects with dyadic response surface analysis

2018 ◽  
Author(s):  
Felix D. Schönbrodt ◽  
Sarah Humberg ◽  
Steffen Nestler
Keyword(s):  
Statistical Method ◽  
Response Surface ◽  
Surface Analysis ◽  
Polynomial Regression ◽  
Response Surface Analysis ◽  
Outcome Variable ◽  
Personality Dimension ◽  
Profile Similarity ◽  
Similarity Indices ◽  
Similarity Effect

Dyadic similarity effect hypotheses state that the (dis)similarity between dyad members (e.g., the similarity on a personality dimension) is related to a dyadic outcome variable (e.g., the re- lationship satisfaction of both partners). Typically, these hypotheses have been investigated by using difference scores or other profile similarity indices as predictors of the outcome variables. These approaches, however, have been vigorously criticized for their conceptual and statistical shortcomings. Here, we introduce a statistical method that is based on polynomial regression and addresses most of these shortcomings: Dyadic response surface analysis (DRSA). This model is tailored for similarity effect hypotheses and fully accounts for the dyadic nature of relationship data. Furthermore, we provide a tutorial with an illustrative example and reproducible R and Mplus scripts that should assist substantive researchers in precisely formulating, testing, and interpreting their dyadic similarity effect hypotheses.

scholarly journals Testing Similarity Effects with Dyadic Response Surface Analysis

2018 ◽  
Vol 32 (6) ◽  
pp. 627-641 ◽  
Author(s):  
Felix D. Schönbrodt ◽  
Sarah Humberg ◽  
Steffen Nestler
Keyword(s):  
Response Surface ◽  
Surface Analysis ◽  
Polynomial Regression ◽  
Response Surface Analysis ◽  
Outcome Variable ◽  
Personality Dimension ◽  
Personality Psychology ◽  
Profile Similarity ◽  
Similarity Effect ◽  
The Relationship

Dyadic similarity effect hypotheses state that the (dis)similarity between dyad members (e.g. the similarity on a personality dimension) is related to a dyadic outcome variable (e.g. the relationship satisfaction of both partners). Typically, these hypotheses have been investigated by using difference scores or other profile similarity indices as predictors of the outcome variables. These approaches, however, have been vigorously criticized for their conceptual and statistical shortcomings. Here, we introduce a statistical method that is based on polynomial regression and addresses most of these shortcomings: dyadic response surface analysis. This model is tailored for similarity effect hypotheses and fully accounts for the dyadic nature of relationship data. Furthermore, we provide a tutorial with an illustrative example and reproducible R and Mplus scripts that should assist substantive researchers in precisely formulating, testing, and interpreting their dyadic similarity effect hypotheses. © 2018 European Association of Personality Psychology

scholarly journals Preprint of "Response Surface Analysis in Personality and Social Psychology: Checklist and Clarifications for the Case of Congruence Hypotheses"

2018 ◽  
Author(s):  
Sarah Humberg ◽  
Steffen Nestler ◽  
Mitja Back
Keyword(s):  
Social Psychology ◽  
Response Surface ◽  
Surface Analysis ◽  
Empirical Studies ◽  
Response Surface Analysis ◽  
Psychological Effects ◽  
Outcome Variable ◽  
Congruence Effect ◽  
Social Psychological ◽  
Psychological Constructs

Response Surface Analysis (RSA) enables researchers to test complex psychological effects, for example, whether the congruence of two psychological constructs is associated with higher values in an outcome variable. RSA is increasingly applied in the personality and social psychological literature, but the validity of published results has been challenged by some persistent oversimplifications and misconceptions. Here, we describe the mathematical fundamentals required to interpret RSA results, and we provide a checklist for correctly identifying congruence effects. We clarify two prominent fallacies by showing that the test of a single RSA parameter cannot indicate a congruence effect, and when there is a congruence effect, RSA cannot indicate whether a predictor mismatch in one direction (e.g., overestimation of one’s intelligence) is better or worse than a mismatch in the other direction (underestimation). We hope that this contribution will further enhance the validity and strength of empirical studies that apply this powerful approach.Humberg, S., Nestler, S., & Back, M. D. (2019). Response Surface Analysis in Personality and Social Psychology: Checklist and Clarifications for the Case of Congruence Hypotheses. Social Psychological and Personality Science, 10(3), 409–419. doi:10.1177/1948550618757600The journal version of this article can be found at: http://journals.sagepub.com/doi/full/10.1177/1948550618757600

Polynomial Regression with Response Surface Analysis: A Powerful Approach for Examining Moderation and Overcoming Limitations of Difference Scores

2010 ◽  
Vol 25 (4) ◽  
pp. 543-554 ◽  
Author(s):  
Linda Rhoades Shanock ◽  
Benjamin E. Baran ◽  
William A. Gentry ◽  
Stacy Clever Pattison ◽  
Eric D. Heggestad
Keyword(s):  
Response Surface ◽  
Surface Analysis ◽  
Polynomial Regression ◽  
Response Surface Analysis ◽  
Difference Scores ◽  
Powerful Approach

Optimization of Separation Process of Waste Polyester-Cotton Fiber by Using Response Surface Analysis

2012 ◽  
Vol 535-537 ◽  
pp. 1564-1568
Author(s):  
Huang Huang ◽  
Yong Ling Yu ◽  
Wei Kong
Keyword(s):  
Hydrochloric Acid ◽  
Response Surface ◽  
Surface Analysis ◽  
Polynomial Regression ◽  
Response Surface Analysis ◽  
Hydrolysis Method ◽  
Blend Fiber ◽  
Acid Reaction ◽  
Solid Liquid ◽  
Central Composite

In this study, the response surface methodology was used to optimize parameters of the diluted hydrochloric acid hydrolysis method, which was adopted to separate the polyester-cotton blend fiber. The four parameters reaction time, mass fraction of hydrochloric acid, reaction temperature and solid-liquid ratio were determined by the single factor experiment as they are significant for the process of separation. By introducing the experiment of four factors on three levels designed by Box-Benhnken central composite method, a quadric polynomial regression model for the fiber weight loss rate was established. And the response surface graphs were plotted to illustrate the optimizing process. The response surface analysis determined that the optimized value of the four parameters were 98 minutes, 10.7%, 96.5 °C and 4.3 g/100ml respectively. Under these conditions, polyester-cotton blend fiber was completely separated.

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