Statistical Techniques for Research

2021 ◽  
pp. 624-636
Author(s):  
Jose Carlos Casas-Rosal ◽  
Carmen León-Mantero ◽  
Noelia Jiménez-Fanjul ◽  
Alexander Maz-Machado
Keyword(s):  
Principal Component Analysis ◽  
Structural Equation ◽  
Descriptive Analysis ◽  
Principal Component ◽  
Equation Modeling ◽  
Statistical Techniques ◽  
Research Fields ◽  
Graphic Analysis ◽  
Definition Of

Data analysis and statistics are tools that are involved in research and are essential to it. There is a wide variety of techniques that allow one to analyze any set of data depending of the desired goal. However, and due to the variety of techniques, its misuse is very common, and the results obtained from its application cannot be taken into consideration because of its non-validity. The objective of this chapter is to create a classification of the main statistical techniques used in different research fields, adding a brief definition of them and specifying their utility, data hypothesis needed for their use, and software required to use them. Special emphasis is placed in R, a free and open source software with multiple packages what allows one to apply these techniques in an effective and simple way. Among the statistical techniques that are desired to be included in this classification are descriptive analysis, graphic analysis, parametrical and non-parametrical hypothesis testing, principal component analysis, factor analysis, and structural equation modeling.

Principal Component Analysis Using the Factor Procedure

2010 ◽  
pp. 171-193
Author(s):  
Sean Eom
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Techniques ◽  
Data Input ◽  
Number Of Factors ◽  
Rotation Method ◽  
Definition Of

This chapter describes the factor procedure. The first section of the chapter begins with the definition of factor analysis. This is the statistical techniques whose common objective is to represent a set of variables in terms of a smaller number of hypothetical variables (factor). ACA uses principal component analysis to group authors into several catagories with similar lines of research. We also present many different approaches of preparing datasets including manual data inputs, in-file statement, and permanent datasets. We discuss each of the key SAS statements including DATA, INPUT, CARDS, PROC, and RUN. In addition, we examine several options statements to specify the followings: method for extracting factors; number of factors, rotation method, and displaying output options.

Industry Software Reviews Survey Results and Findings

2011 ◽  
pp. 196-233
Author(s):  
Yuk Kuen Wong
Keyword(s):  
Principal Component Analysis ◽  
Structural Equation ◽  
Reliability And Validity ◽  
Principal Component ◽  
Equation Modeling ◽  
Data Analyses ◽  
Inner Model ◽  
Survey Results ◽  
Software Reviews ◽  
Industry Survey

This chapter describes the industry survey results and findings, which include preliminary data analysis (missing value and descriptive data analyses), exploratory analyses (principal component analysis, and reliability and validity analysis) and hypotheses tests using Structural Equation Modeling Using Partial Least Squares (PLS) methodology. A total of 15 constructs with 48 indicators are used in the model, but only 15 paths are significant. The results of the model, using an inner model path weighting scheme show a substantial R-square of 0.398 for performance, a moderate level of 0.239 for experience and 0.335 for teamwork and a weak level of 0.024 for reports and 0.049 for previously reviewed software.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

scholarly journals Mental Health of Refugees and Migrants during the COVID-19 Pandemic: The Role of Experienced Discrimination and Daily Stressors

2021 ◽  
Vol 18 (12) ◽  
pp. 6354
Author(s):  
Eva Spiritus-Beerden ◽  
An Verelst ◽  
Ines Devlieger ◽  
Nina Langer Primdahl ◽  
Fábio Botelho Guedes ◽  
...  
Keyword(s):  
Mental Health ◽  
Structural Equation ◽  
Descriptive Analysis ◽  
Health Impact ◽  
Equation Modeling ◽  
Daily Stressors ◽  
Health Crisis ◽  
The Impact ◽  
Migrant Groups

The COVID-19 pandemic is a defining global health crisis of our time. While the impact of COVID-19, including its mental health impact, is increasingly being documented, there remain important gaps regarding the specific consequences of the pandemic on particular population groups, including refugees and migrants. This study aims to uncover the impact of the COVID-19 pandemic on the mental health of refugees and migrants worldwide, disentangling the possible role of social and daily stressors, i.e., experiences of discrimination and daily living conditions. Descriptive analysis and structural equation modeling were used to analyze the responses of N = 20,742 refugees and migrants on the self-reporting global ApartTogether survey. Survey findings indicated that the mental health of refugees and migrants during the COVID-19 pandemic was significantly impacted, particularly for certain subgroups, (i.e., insecure housing situation and residence status, older respondents, and females) who reported experiencing higher levels of increased discrimination and increases in daily life stressors. There is a need to recognize the detrimental mental health impact of the COVID-19 pandemic on particular refugee and migrant groups and to develop interventions that target their unique needs.

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