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

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

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

Social Psychological and Personality Science ◽

10.1177/1948550618757600 ◽

2018 ◽

Vol 10 (3) ◽

pp. 409-419 ◽

Cited By ~ 28

Author(s):

Sarah Humberg ◽

Steffen Nestler ◽

Mitja D. Back

Keyword(s):

Response Surface ◽

Surface Analysis ◽

Empirical Studies ◽

Response Surface Analysis ◽

Psychological Effects ◽

Outcome Variable ◽

Congruence Effect ◽

Social Psychological ◽

Psychological Constructs ◽

Powerful Approach

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.

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Cubic Response Surface Analysis: Investigating Asymmetric and Level-Dependent Congruence Effects With Third-Order Polynomial Models

10.31234/osf.io/v6m35 ◽

2020 ◽

Author(s):

Sarah Humberg ◽

Felix D. Schönbrodt ◽

Mitja Back ◽

Steffen Nestler

Keyword(s):

Response Surface ◽

Surface Analysis ◽

Statistical Approach ◽

Real Data ◽

Response Surface Analysis ◽

Outcome Variable ◽

Congruence Effect ◽

Third Order ◽

Order Polynomial ◽

Polynomial Models

Congruence hypotheses play a major role in many areas of psychology. They refer to, for example, the consequences of person-environment fit, similarity, or self-other agreement. For example, are people psychologically better adjusted when their self-view is in line with their reputation? A valid statistical approach that can be applied to investigate congruence hypotheses of this kind is quadratic Response Surface Analysis (RSA) in which a second-order polynomial model is fit to the data and appropriately interpreted. However, quadratic RSA does not allow researchers to investigate more precise expectations about a congruence effect. Do the data support an asymmetric congruence effect, in the sense that congruence leads to the highest (or lowest) outcome, but incongruence in one direction (e.g., self-view exceeds reputation) affects the outcome differently than incongruence in the other direction (e.g., self-view falls behind reputation)? Is there a level-dependent congruence effect, such that the amount of congruence is more strongly related to the outcome variable for some levels of the predictors (e.g., high self-view and reputation) than for others (e.g., low self-view and reputation)? Such complex congruence hypotheses have frequently been suggested in the literature, but they could not be investigated because an appropriate statistical approach has yet to be developed. Here, we present analytical strategies, based on third-order polynomial models, that enable users to investigate asymmetric and level-dependent congruence effects, respectively. To facilitate the correct application of the suggested approaches, we provide respective step-by-step guidelines, corresponding R syntax, and illustrative analyses using simulated and real data.

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Testing similarity effects with dyadic response surface analysis

10.31234/osf.io/8mpua ◽

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.

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Response Surface Analysis: A Tutorial for Examining Linear and Curvilinear Effects

Revista de Administração Contemporânea ◽

10.1590/1982-7849rac2021200293.en ◽

2021 ◽

Vol 25 (6) ◽

Author(s):

Antonio Carlos Rodrigues

Keyword(s):

Mathematical Modeling ◽

Response Surface ◽

Surface Analysis ◽

Response Surface Analysis ◽

Outcome Variable ◽

Basic Principles ◽

Study Objective ◽

Predictive Variables ◽

Conceptual Background ◽

Research Questions

ABSTRACT Context: response surface analysis (RSA) is an approach that allows examining the extent to which combinations of two predictive variables relate to one outcome variable. The method is particularly interesting in cases where (in)congruence between the two predictive variables is a central consideration of the study. Objective: the purpose of this article is to provide a tutorial on applying RSA. Method: the method’s conceptual background and an illustrative example are provided so that the reader can understand some of the basic principles of the technique. This tutorial’s target audience is researchers who use mathematical modeling but are not yet familiar with the method. Results: the technique has the potential for application in various research questions in the field of Administration. Conclusions: besides providing a tutorial on how to use the investigated technique, the study demonstrates its relevance in the analysis of congruence and incongruence between the scores.

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How to Test Questions About Similarity in Personality and Social Psychology Research

Social Psychological and Personality Science ◽

10.1177/1948550617698204 ◽

2017 ◽

Vol 8 (4) ◽

pp. 465-475 ◽

Cited By ~ 36

Author(s):

Maxwell Barranti ◽

Erika N. Carlson ◽

Stéphane Côté

Keyword(s):

Social Psychology ◽

Response Surface ◽

Surface Analysis ◽

Response Surface Analysis ◽

Psychology Research ◽

Personality Psychology ◽

Difference Scores ◽

Bias Model ◽

Current Article ◽

Traditional Approaches

Social and personality psychologists are often interested in the extent to which similarity, agreement, or matching matters. The current article describes response surface analysis (RSA), an approach designed to answer questions about how (mis)matching predictors relate to outcomes while avoiding many of the statistical limitations of alternative, often-used approaches. We explain how RSA provides compressive and often more valid answers to questions about (mis)matching predictors than traditional approaches provide, outline steps on how to use RSA (including modifiable syntax), and demonstrate how to interpret RSA output with an example. To bolster our argument that RSA overcomes many limitations of traditional approaches (i.e., incomplete or misleading inferences), we compare results from four popular approaches (i.e., difference scores, residuals, moderated regression, and the truth and bias model) to those obtained from RSA. We discuss specific applications of RSA to social and personality psychology research.

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Testing Similarity Effects with Dyadic Response Surface Analysis

European Journal of Personality ◽

10.1002/per.2169 ◽

2018 ◽

Vol 32 (6) ◽

pp. 627-641 ◽

Cited By ~ 12

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

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Using Response Surface Analysis to Estimate Time of The Gas-Water Contact Lift in Yamburg Gas Field Conditions

Journal of Advanced Research in Fluid Mechanics and Thermal Sciences ◽

10.37934/arfmts.87.3.105112 ◽

2021 ◽

Vol 87 (3) ◽

pp. 105-112

Author(s):

Mugisho Joel Bacirheba ◽

Tanoh Boguy Eddy Martial ◽

Mirsamiev Narzullo Abdugaforovich ◽

Madumarov Mukhriddin Mukhammadjon Ugli

Keyword(s):

Response Surface ◽

Surface Analysis ◽

Contact Time ◽

Oil And Gas ◽

Three Dimensional ◽

Response Surface Analysis ◽

Outcome Variable ◽

Model Parameters ◽

Gas Field ◽

Water Contact

Like many of the fields in northwest Siberia, the Yamburg oil and gas condensate field is in the final production stages. This, therefore, results in an accumulation of a large amount of formation water in the inflow at the bottom of the well. Response surface analysis is used as a new technique to gain a detailed understanding of the relationships between combinations of two predictor variables and an outcome variable. This approach was applied to the Yamburg field to estimate the time of the gas-water contact, considered as the result variable, by taking into account two groups of predictive variables which correspond to the reservoirs grouped by their lithological characteristics. The results of the predicted gas-water contact time were compared to the expected gas-water contact time, the data of which were taken into account for the study. Using the model parameters as well as the three-dimensional response surface, which was constructed to facilitate and improve the interpretation of the results, it was then possible to predict the gas-water contact time under certain conditions.

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