<div><p>This study explores the integration of sexual and reproductive health (SRH) education in Sri Lanka, utilizing the Health Belief Model (HBM) to predict the perceived quality of SRH education among non-state undergraduate students. In many Asian countries, including Sri Lanka, cultural resistance and skepticism often challenge SRH education initiatives. The research is based on a questionnaire survey, examining factors influencing the perceived quality of SRH education, such as cultural norms, embarrassment, attitudes, awareness, and institutional support. In recent years, the Sri Lankan telecom industry has experienced significant expansion and transformation, leading to a notable increase in mergers and acquisitions (M&A). This study investigates the impact of human resource practices on M&A performance, utilizing Lewin’s Change Management Model as a framework. The research is based on a questionnaire survey, examining factors influencing the performance of M&A in Sri Lanka Telecom industry, such as communication (Unfreeze), training (Change), leadership (Refreeze), and performance (M&A outcomes). Structural Equation Modeling (SEM) reveals that communication significantly influences training (β = 0.800), while training has a strong effect on leadership (β = 1.062), both directly and indirectly via communication (β = 0.850). Additionally, performance is positively impacted by training (β = 0.819) and leadership (β = 0.459), with communication exerting a substantial indirect influence on performance (β = 0.655). These results underscore the necessity for an integrated approach that enhances organizational performance and adaptability in an evolving business landscape. To optimize M&A outcomes, organizations are encouraged to prioritize leadership development, invest in comprehensive training programs, and align communication strategies effectively. This research contributes valuable insights into the management of change within the telecom sector, promoting sustainable growth and success in future M&A endeavours.</p></div>

Cited by 0Open access

Factor analysis.

Alexandra M. J. Denham (6680951), Olivia Wynne (819181), Amanda L. Baker (5985959) et al. · Figshare · 2020

<p>Factor analysis used to establish the types of unmet needs reported by caregivers.</p> <p>(DOCX)</p>

Cited by 0Open access

Factor analysis.

Molla Yigzaw Birhanu (7490309), Samuel Derbie Habtegiorgis (10711552), Wodaje Gietaneh (9572474) et al. · Figshare · 2022

Cited by 0Open access

Factor Analysis

Dipayan Maiti, Dipayan Maiti, Dipayan Maiti et al. · 2008

What to do when you have too many predictors in a model? ◮ For example you have expression level data for 1000 genes! ◮ Or you have customer attributes in hundreds and you are interested in making a predictive model based on customer attributes! ◮ Or you have second by second stock market data over a trading day for stocks! ◮ Or in survey data where multiple questions might capture the same kind of information (highly correlated) Dipayan Maiti

Cited by 0

Factor Analysis

Graffelman, Jan · RECERCAT (Consorci de Serveis Universitaris de Catalunya)

Factor analysis is a multivariate statistical method for data reduction that originated in psychometrics and has found applications in many branches of science. The method aims to describe the correlation structure between a large set of observed variables in terms of a few underlying latent variables called factors. Factor analysis employs a specific model, where observed variables are modelled as linear combinations of common factors plus a specific error term. This model can be estimated by using principal components, by using the iterative principal factor method, or by maximum likelihood. After estimation, factors may be rotated in order to improve their interpretation. An example of the application of factor analysis to a set of pollutants in an environmental monitoring study is discussed.

Cited by 0Open access

Factor analysis

Claire Lemercier · OpenEdition (OpenEdition) · 2019

Factor analysis is addressed in our Chapter 4. Factor analysis graphs may look strange; it certainly takes some time to learn how to read them (to that effect, you should re-read our chapter and look into our suggestions for further reading). Still, many historians want to use factor analysis, once they have understood that it is an exploratory method that allows them to get a general idea of "everything that is in their dataset," and to create typologies. (Perhaps this is also true in other ...

Cited by 0

Factor Analysis

Klaus Backhaus, Bernd Erichson, Sonja Gensler et al. · RePEc: Research Papers in Economics

Abstract The explorative factor analysis is a procedure of multivariate analysis which aims at identifying structures in large sets of variables. Large sets of variables are often characterized by the fact that as the number of variables increases, it may be assumed that more and more variables are correlated. The exploratory factor analysis aims to structure the relationships in a large set of variables to the extent that it identifies groups of variables that are highly correlated with each other and separates them from less correlated groups. The groups of highly correlated variables are called factors. Apart from the structuring function, factor analysis is also used for data reduction. At the end of the chapter, there is also a brief outlook on confirmatory factor analysis, in which predefined factor structures are examined.

Cited by 0

Factor analysis

ヒデブミカスガ, Hidebumi Kasuga, 秀文春日 · KeiO Associated Repository of Academic Resources (Keio University) · 1968

Summaries of Doctor and Master Theses

Cited by 0

Factor analysis

Yeoh Sok Foon · 2026

Cited by 0

Factor analysis

Philip Spencer Camilleri · 1977

Interests} are presented not so much as items of the M.A.C.P.I. pertaining to culture but as a method for assessing the subject 1 s background in order to see if the respondents' backgrounds are variedc Demographic Questions Vl--Sex.

Cited by 0Open access