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    Factor Analysis – A Dimension Reduction approach Introduction: The study considers a Data Set that comprises of a random sample (n=1000) of call center representatives’ (CSRs) responses to a survey. Several demographic variables were captured as well as 36 variables measuring the role of ambiguity, role of conflict, work overload, work and family conflict, IT support, organizational commitment, job satisfaction, both extrinsic and intrinsic, and intention to quit. The selected variables for the

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    The first factor template in Equation (3.1) models the co-occurrence between input variables and output variables. Moreover, the factor in the unigram factor template are defined as: φ_u (y_t,x_t;_t)= exp ∑_k^(K(T))▒〖_TK f_TK (y_t,x_t)〗 (3.2) On the other hand, the second factor template in Equation (3.1) models the co-occurrence between contiguous output variables. The factors in the bigram factor template are defined as: φ_b (y_t,y_(t-1);_)=exp ∑_k^(K())▒〖_K f_K (y_t,y_(t-1))〗 (3.3) As

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    Factor Analysis

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    The Factor analysis summarizes many variables by few factors and helps to understand the structure of a correlation matrix. It accounts for multi-collinearity among a large number of interrelated independent quantitative variables by grouping the variables into a few factors and reduces correlations. In our case, we have countries as the units of observations. We have data on different aspects of these countries like population, density, percentage of people living in cities, religion, life expectancy

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    THE CONFIRMATORY FACTOR ANALYSIS Based on the Schumacker and Lomaz (2004), Confirmatory factor analysis Passing KMO and Bartlett’s test is a pre-requisite to factor analysis. The study’s investigations disclosed precise decent results, where the KMO score is above the .80 value which it is indicates that a significant Barlett’s Test. This figure however describes that the collected data are suitable for conducting factor analysis. The rotated component matrix above displayed eight separate components

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    The first article is Factor Analysis of Cardiovascular Risk Clustering in Pediatric Metabolic Syndrome: CASPIAN Study. This article examined multiple variable factors to determine if children who have metabolic syndrome (MetS) were more prong to have coronary artery disease. Kelishadi, Ardalan, Adeli, Motaghian, Majdzadeh, Delavari, and Namazi (2007) investigated data from over 4,800 nationally represented school students aged 6-18 years. In order to find out if there was a correlation within

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    Factor Analysis Essay

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    Factor analysis can only be applied to continuous variables (or) interval scaled variables. Factor analysis is like Regression analysis as it tries to ‘best fit’ the factors to a scatter diagram of data in such a way that the factors explain the variance associated with the responses to each statements. Factor analysis was conducted by the researcher in the present research in the following stages. 1. Desk Research 2. Formulation of questionnaire 3. Collection of data 4. Feeding and processing the

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    3.5 Data analysis methods According to Pallant (2007), the obtained data needs to be analysed and interpreted very carefully in order for the researcher to gain valuable and useful information from the study (p. 100). The researcher should have at least understanding statistical techniques used in the study for data analysis. Moreover, Hair et al. (2003) have also mentioned that it is very important to determine the goodness of the collected data and analyse it accurately because the wrong data will

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    EFE The external Factor Evaluation (EFE) Matrix examines a company’s external environment to help identify its opportunities and threats. In this matrix you figure out what your companies, in our case McDonald’s, opportunities and threats, and you give them a weight and rating. The weight for each opportunity and threat can range from 0.0 (low importance) to 1.0 (high importance). The number indicates how important the factor is if a company wants to succeed in an industry. The sum of the weights

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    date, profile updates, job applications, etc. The IPIP-NEO is a personality inventory of the five factor model that appraise the measurement of openness, conscientiousness, extraversion, agreeableness, and neuroticism, also known as OCEAN. The IPIP-NEO develops the report of estimating one’s near accurate personality traits by an objective viewpoint. The purpose of the IPIP-NEO assessment is

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    Descriptive statistic is a statistic analysis to describe the characteristic of the respondents (Pallant, 2013). This study employs descriptive statistical analysis which gives value of mean, median and standard deviation of the respondents based on several indicators, such as sex/gender, educational level, position at work and income of the respondents. By using these indicators, the researcher describes the profile of the respondents. Hence, it can give some valuable information about the respondents

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