Review of Introduction to factor analysis: What it is and how to do it Quantitative applications in the social sciences, No 13.

1979 ◽  
Vol 24 (11) ◽  
pp. 940-940
Author(s):  
JOHN W. COTTON
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
The Social

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 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.

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 Condition 9 and 10 Tests of Model Confirmation: A Review of James, Mulaik, and Brett (1982) and Contemporary Alternatives

2017 ◽  
Vol 23 (1) ◽  
pp. 6-29 ◽  
Author(s):  
Larry J. Williams ◽  
Ernest H. O’Boyle ◽  
Jia (Joya) Yu
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Unique Contribution ◽  
New Techniques ◽  
The Past ◽  
The Social ◽  
Current Article

Structural equation modeling (SEM) serves as one of the most important advances in the social sciences in the past 40 years. Through a combination of factor analysis and path analysis, SEM allows organizational researchers to test causal models while accounting for random and nonrandom (bias) measurement error. SEM is now one of the most commonly used analytic techniques and its modern day ubiquity can be traced in large part to a series of intellectual contributions by Larry James. The current article focuses on the seminal work, James, Mulaik, and Brett (1982), and the unique contribution of the “conditions” required for appropriate confirmatory inference with the path and latent variable models. We discuss the importance of James et al.’s Condition 9 and 10 tests, systematically review 14 years of studies using SEM in leading management journals and reanalyze results based on new techniques that extend James et al. (1982), and conclude with suggestions for improved Condition 9 and 10 assessments.

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 PSYCHOMETRIC PROPERTIES OF TEN ITEM PERSONALITY IVENTORY (TIPI)

2016 ◽  
Vol 1 (2) ◽  
pp. 115 ◽  
Author(s):  
Sonia Brito-Costa ◽  
Ana Moisão ◽  
Hugo De Almeida ◽  
Florencio Vicente Castro
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Psychometric Properties ◽  
Software Package ◽  
Factor Model ◽  
Elite Athletes ◽  
Five Factor Model ◽  
Statistical Software ◽  
The Social

This study based on the Five Factor Model of Costa and McCrae (1987) sought to determine theninternal consistency and the psychometric properties of the Ten Item Personality Inventory (TIPI) of Gosling, Rentfrow, & Swann (2003), Lima and Castro (2009) Portuguese version. The sample consisted of 170 male soccer athletes whose average age stood at 18.50 years, with a minimum of 13 and a maximum of 33 years. Statistical analysis was performed by the Statistical Software Package for the Social Sciences (SPSS) in its 19 version for Windows. The TIPI showed low internal consistency (=0.462) and factor analysis that meets the criteria postulated by the instrument authors, so considered it valid to evaluate the personality rapidly in samples with little time available, for example elite athletes.

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