Social Desirability and Judged Frequency of Occurrence: Reanalysis and Comment on Bernhardson and Fisher

The conclusions of Bernhardson and Fisher regarding the “direct contribution” of the social desirability scale value and judged probability of occurrence in the population to the prediction of the proportion of respondents answering True to personality items were re-examined basing new estimates of “direct contribution” upon multiple regression model comparisons which emphasize the common “contribution” between the judged probability of occurrence and social desirability and are not influenced by the ordering of variables in the regression equation.

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Admissible Linear Estimates of the Regression Parameters

Calcutta Statistical Association Bulletin ◽

10.1177/0008068319810302 ◽

1981 ◽

Vol 30 (3-4) ◽

pp. 107-113

Author(s):

Malay Ghosh ◽

Ahmad Parsian

Keyword(s):

Regression Model ◽

Multiple Regression ◽

Multiple Regression Model ◽

Common Variance ◽

Regression Parameters ◽

Generalized Bayes ◽

The Common ◽

Mean Vector

In Ghosh and Parsian (1981), the Lindley-Smith (1972) linear estimates of the multinonnal mean vector are shown to be generalized Bayes with respect to symmetric bowl shaped loss both when the common variance is known and unknown . The admissibility of such estimators is also proved in both these oases. The present paper gencralizes the findings of Ghosh and Parsian (1981) to a multiple regression model.

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A new multiple regression model to identify multi-family houses with a high prevalence of sick building symptoms “SBS”, within the healthy sustainable house study in Stockholm (3H)

International Archives of Occupational and Environmental Health ◽

10.1007/s00420-009-0447-0 ◽

2009 ◽

Vol 83 (1) ◽

pp. 85-94 ◽

Cited By ~ 14

Author(s):

Karin Engvall ◽

M. Hult ◽

R. Corner ◽

E. Lampa ◽

D. Norbäck ◽

...

Keyword(s):

Regression Model ◽

Multiple Regression ◽

Multiple Regression Model ◽

High Prevalence ◽

Sick Building

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Coping, Defense and Socially Desirable Responses

Psychological Reports ◽

10.2466/pr0.1968.22.3.985 ◽

1968 ◽

Vol 22 (3) ◽

pp. 985-988 ◽

Cited By ~ 4

Author(s):

Henry A. Alker

Keyword(s):

Social Desirability ◽

Socially Desirable Responding ◽

Social Desirability Scale ◽

Strategic Capacity ◽

Defensive Behaviors ◽

The Social

Coping and defensive behaviors, assessed by intensive interviews, covary, respectively, with the presence of socially desirable and socially undesirable inventory responses. Minimizing the influence of the social desirability variable consequently interferes with the strategic capacity of inventory items to index coping and defense. Furthermore, using low social-desirability scale value items most effectively discriminates between genuine and defensively distorted inventory responses. Neutral items are less efficient in this connection even though they minimize socially desirable responding.

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Improving the Determination of Heat Exchanger Thermal Resistances by Means of a Covariance Multiple Regression Model

10.4271/931118 ◽

1993 ◽

Author(s):

H. F. Khartabil ◽

E. F. C. Somerscales

Keyword(s):

Heat Exchanger ◽

Regression Model ◽

Multiple Regression ◽

Multiple Regression Model

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Factors Affecting NPAs in Indian Banking Sector

Paradigm ◽

10.1177/09718907211035594 ◽

2021 ◽

Vol 25 (2) ◽

pp. 181-193

Author(s):

Nitya Garg

Keyword(s):

Regression Model ◽

Multiple Regression ◽

Banking Sector ◽

Relative Weight ◽

Multiple Regression Model ◽

Factors Affecting ◽

Weight Analysis ◽

Specific Factors ◽

Relative Weight Analysis ◽

Bank Specific Factors

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.

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