Relating Multidimensional Sets of Variables: Canonical Correlation or Factor Analysis?

1975 ◽  
Vol 36 (3) ◽  
pp. 859-862 ◽  
Author(s):  
John D. Morris ◽  
Wilson H. Guertin
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Canonical Correlation ◽  
Common Factor ◽  
Research Problem ◽  
Canonical Analysis ◽  
Factor Analyses ◽  
Specific Research ◽  
The Social ◽  
Separate Factor

Several prominent multivariate psychological methodologists recommend the use of canonical correlation to relate multidimensional sets of variables. This method along with separate factor analyses of the sets is considered in relation to the questions they may be able to answer on a specific research problem. Alternate analyses of data from the social sciences illustrate the value of common factor analysis compared with canonical analysis as a method for relating the underlying constructs across sets of variables.

scholarly journals Missing values in exploratory factor analysis: A ‘best of all possible worlds’ approach to imputation for incomplete survey data

2021 ◽  
Author(s):  
Bas Bosma ◽  
Arjen van Witteloostuijn
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Possible Worlds ◽  
Missing Values ◽  
Ease Of Use ◽  
Factor Analyses ◽  
Imputation Methods ◽  
The Social

In the social sciences, multi-item scales and factor analyses are standard tools in survey research. In the social sciences, such tools are omnipresent, as are, unavoidably, nonresponses. The question is how to handle missing values when an exploratory factor analysis is intended. Deletion methods will result in — oftentimes substantial and damaging — reduction of power. The seemingly obvious alternative is to keep all respondents and apply imputation to missing values. However, with the true factor structure unknown, theoretically recommendable multiple imputation methods cannot simply be applied. Instead of declaring an entire method unsuitable for exploratory analysis, we propose an approach that keeps the relevant aspects of various methods and combines these by sacrificing less relevant aspects. Doing so, we keep understanding and ease of use in mind, aiming for an approach that is more rigorous and ‘correct’ than what is commonly used in practice, whilst still being straightforward enough to actually be used.

scholarly journals Missing values in exploratory factor analysis: A ‘best of all possible worlds’ approach to imputation for incomplete survey data

2021 ◽  
Author(s):  
Bas Bosma ◽  
Arjen van Witteloostuijn
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Possible Worlds ◽  
Missing Values ◽  
Ease Of Use ◽  
Factor Analyses ◽  
Imputation Methods ◽  
The Social

In the social sciences, multi-item scales and factor analyses are standard tools in survey research. In the social sciences, such tools are omnipresent, as are, unavoidably, nonresponses. The question is how to handle missing values when an exploratory factor analysis is intended. Deletion methods will result in — oftentimes substantial and damaging — reduction of power. The seemingly obvious alternative is to keep all respondents and apply imputation to missing values. However, with the true factor structure unknown, theoretically recommendable multiple imputation methods cannot simply be applied. Instead of declaring an entire method unsuitable for exploratory analysis, we propose an approach that keeps the relevant aspects of various methods and combines these by sacrificing less relevant aspects. Doing so, we keep understanding and ease of use in mind, aiming for an approach that is more rigorous and ‘correct’ than what is commonly used in practice, whilst still being straightforward enough to actually be used.

scholarly journals Missing values in exploratory factor analysis: A ‘best of all possible worlds’ approach to imputation for incomplete survey data

2021 ◽  
Author(s):  
Bas Bosma ◽  
Arjen van Witteloostuijn
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Possible Worlds ◽  
Missing Values ◽  
Ease Of Use ◽  
Factor Analyses ◽  
Imputation Methods ◽  
The Social

In the social sciences, multi-item scales and factor analyses are standard tools in survey research. In the social sciences, such tools are omnipresent, as are, unavoidably, nonresponses. The question is how to handle missing values when an exploratory factor analysis is intended. Deletion methods will result in — oftentimes substantial and damaging — reduction of power. The seemingly obvious alternative is to keep all respondents and apply imputation to missing values. However, with the true factor structure unknown, theoretically recommendable multiple imputation methods cannot simply be applied. Instead of declaring an entire method unsuitable for exploratory analysis, we propose an approach that keeps the relevant aspects of various methods and combines these by sacrificing less relevant aspects. Doing so, we keep understanding and ease of use in mind, aiming for an approach that is more rigorous and ‘correct’ than what is commonly used in practice, whilst still being straightforward enough to actually be used.

scholarly journals Educating Engineers Under a Social Justice Perspective

2020 ◽  
Vol 10 (3) ◽  
pp. 82
Author(s):  
Patricia P. Jiménez ◽  
Jimena Pascual ◽  
Andrés Mejía
Keyword(s):  
Social Sciences ◽  
Social Justice ◽  
Factor Analysis ◽  
Quantitative Study ◽  
Two Dimensions ◽  
Public Welfare ◽  
Interdisciplinary Learning ◽  
The Social ◽  
Systems Simulation ◽  
Cultural Features

Although the need for an engineering education oriented to public welfare and social justice has been acknowledged for many years, the efforts to put it in practice seem insufficient and a culture of disengagement still appears dominant. The aim of this article is twofold: (1) to examine beliefs and motivations of university faculty towards the social responsibility of engineers, and (2) to develop pedagogical principles to deal with the culture of disengagement in engineering. A survey-based quantitative study was conducted among faculty from a university in Chile. A factor analysis revealed two dimensions of social justice in their conceptions, with significantly higher scores for the first one: environmental/ethical versus public/community. Additionally, faculty value less the humanities and social sciences than other non-technical topics in the curriculum. Results, for this university, confirm the prevailing cultural features reported elsewhere. Some guidelines to counteract the cultural pillars of disengagement are based on critical thinking, context-based learning or situated practice, and interdisciplinary learning. These are illustrated in a course on Systems Simulation.

scholarly journals The effect of materialistic and nonmaterialistic motivational factors to employees' commitment to work

2021 ◽  
Vol 13 (2) ◽  
pp. 97-112
Author(s):  
Suvad Isaković ◽  
Ajdin Isaković ◽  
Kanita Isaković
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Interpersonal Relations ◽  
Motivational Factors ◽  
Independent Variables ◽  
The Social ◽  
Work Done ◽  
The Relationship ◽  
Structured Questionnaire

The success of each business relies on the employees' commitment to work, i.e., how and in which way employees perform their work. When consumers are offered the same or similar products produced by different companies and at different prices, and when the company's business result greatly depends on the quality of the work done, company management is more interested in securing its employees' full dedication to work. The generally accepted phrase "you get what you pay for" encouraged this research, whose purpose is to determine the strength of the relationship between materialistic and nonmaterialistic motivational factors to employees' commitment to work. This research starts with the assumption that materialistic factors of motivation are more important motivational factors for employees when compared to nonmaterialistic ones. Listed indicators of motivational factors represent independent variables, while the dependent variable represents the indicator 'work satisfaction', which determines the level of employees' commitment to work. The research had 147 participants who work in companies from different industries and different sizes. According to the Likert scale, a structured questionnaire was used to measure the employees' attitudes. Various methods for data processing in Statistical Package for the Social Sciences (SPSS) and Smart PLS3 program were used: Descriptive statistics of the sample (SPSS); Exploratory factor analysis - PCA analysis of principal components (SPSS): Factor analysis - a test of validity and confidence of the instruments (SmartPLS3); Bootstrapping analysis - testing of the hypothesis (SmartPLS3). The conducted research shows that nonmaterialistic motivational factors, including Interpersonal relations and advancement, statistically significantly influence satisfaction at work, i.e., employees' commitment to the work.

scholarly journals International Students’ Perceptions of Service Quality in the UK Banking Sector: An Exploratory Study

2011 ◽  
Vol 1 (2) ◽  
pp. 50-58
Author(s):  
Christopher Bond ◽  
Marc Ting-Chun Hsu
Keyword(s):  
Social Sciences ◽  
International Students ◽  
Service Quality ◽  
Survey Data ◽  
Exploratory Study ◽  
Banking Sector ◽  
Specific Research ◽  
The Social ◽  
The Uk ◽  
Physical Layout

This study reviews and evaluates international students’ perceptions of UK banks. The specific research objectives were to identify international students’ expectations and perceptions of service quality from UK banks and to assess the quality GAP or dissonance between these. A total of 297 international students studying in the UK responded to the survey. Data gathered was analyzed using Statistical Package for the Social Sciences (SPSS 16). The study reveals that the main areas of service quality with which international students are generally satisfied relates to tangibles such as physical layout and appearance. The key areas of dissatisfaction that the study identified were with factors related to reliability and empathy. This appears to be the first study in the UK banking sector that has focused on service quality with respect to international students. 

Factor Analysis of Some Psychometric Measures of Impulsiveness and Anxiety

1965 ◽  
Vol 16 (2) ◽  
pp. 547-554 ◽  
Author(s):  
Ernest S. Barratt
Keyword(s):  
Factor Analysis ◽  
Personality Traits ◽  
Item Analysis ◽  
Emotional Stability ◽  
Second Order ◽  
High Loading ◽  
Factor Analyses ◽  
Barratt Impulsiveness Scale ◽  
Psychometric Measures ◽  
Separate Factor

Two orthogonal personality traits or predispositions, impulsiveness and anxiety, have been invariant in five separate factor analyses. One of these analyses is presented here along with an item analysis of the Barratt Impulsiveness Scale (BIS) which has a high loading on the impulsiveness factor. The BIS has never significantly correlated with any of the various anxiety or emotional stability measures in over 50 administrations of the scale, further evidence for the invariance of these two second-order factors.

scholarly journals An Entropy-Based Tool to Help the Interpretation of Common-Factor Spaces in Factor Analysis

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 140
Author(s):  
Nobuoki Eshima ◽  
Claudio Giovanni Borroni ◽  
Minoru Tabata ◽  
Takeshi Kurosawa
Keyword(s):  
Factor Analysis ◽  
Correlation Analysis ◽  
Canonical Correlation ◽  
Common Factor ◽  
Common Factors ◽  
Correlation Coefficients ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Numerical Examples ◽  
The Common

This paper proposes a method for deriving interpretable common factors based on canonical correlation analysis applied to the vectors of common factors and manifest variables in the factor analysis model. First, an entropy-based method for measuring factor contributions is reviewed. Second, the entropy-based contribution measure of the common-factor vector is decomposed into those of canonical common factors, and it is also shown that the importance order of factors is that of their canonical correlation coefficients. Third, the method is applied to derive interpretable common factors. Numerical examples are provided to demonstrate the usefulness of the present approach.

scholarly journals Tracking Homo Oeconomicus: Development of the Neoliberal Beliefs Inventory

2015 ◽  
Vol 3 (1) ◽  
pp. 71-88 ◽  
Author(s):  
Laina Y. Bay-Cheng ◽  
Caroline C. Fitz ◽  
Natalie M. Alizaga ◽  
Alyssa N. Zucker
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Self Concept ◽  
Homo Oeconomicus ◽  
Three Samples ◽  
The Social ◽  
Confirmatory Factor ◽  
Neoliberal Ideology

Researchers across the social sciences are beginning to note that neoliberalism’s influence is no longer restricted to macroeconomic and social policies, but can now be detected in individuals’ behaviors, relationships, perceptions, and self-concept. However, psychologists lack a means of assessing neoliberal beliefs directly. We collected data from three samples of U.S. undergraduates to develop and test a measure of neoliberal ideology, the Neoliberal Beliefs Inventory (NBI). Using first exploratory and then confirmatory factor analysis, we devised a 25-item measure that is both reliable and valid, at least within a particular demographic (i.e., U.S. traditionally-aged undergraduates). The NBI may help psychologists specify and analyze the role of neoliberal ideology in shaping human behavior and functioning.

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