scholarly journals MSA: The forgotten index for identifying inappropriate items before computing exploratory item factor analysis

Methodology ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. 296-306
Author(s):  
Urbano Lorenzo-Seva ◽  
Pere J. Ferrando
Keyword(s):  
Factor Analysis ◽  
Selection Process ◽  
Item Selection ◽  
Personality Measurement ◽  
Item Pool ◽  
Robust Approach ◽  
Variable Measure ◽  
Item Factor Analysis ◽  
Forgotten Index ◽  
Pool Dataset

Kaiser’s single-variable measure of sampling adequacy (MSA) is a very useful index for debugging inappropriate items before a factor analysis (FA) solution is fitted to an item-pool dataset for item selection purposes. For reasons discussed in the article, however, MSA is hardly used nowadays in this context. In our view, this is unfortunate. In the present proposal, we first discuss the foundation and rationale of MSA from a ‘modern’ FA view, as well as its usefulness in the item selection process. Second, we embed the index within a robust approach and propose improvements in the preliminary item selection process. Third, we implement the proposal in different statistical programs. Finally, we illustrate its use and advantages with an empirical example in personality measurement.

scholarly journals Comparing item parameter estimates and fit statistics of the Rasch model from three different traditions

2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Bahrul Hayat ◽  
Muhammad Dwirifqi Kharisma Putra ◽  
Bambang Suryadi
Keyword(s):  
Factor Analysis ◽  
Rasch Model ◽  
Measurement Model ◽  
Rasch Measurement ◽  
Item Parameter ◽  
Rasch Models ◽  
Rasch Measurement Model ◽  
Item Factor Analysis ◽  
The Rasch Model ◽  
Special Case

Rasch model is a method that has a long history in its application in the fields of social and behavioral sciences including educational measurement. Under certain circumstances, Rasch models are known as a special case of Item response theory (IRT), while IRT is equivalent to the Item Factor Analysis (IFA) models as a special case of Structural Equation Models (SEM), although there are other ‘tradition’ that consider Rasch measurement models not part of both. In this study, a simulation study was conducted to using simulated data to explain how the interrelationships between the Rasch model as a constraint version of 2-parameter logistic (2-PL) IRT, Rasch model as an item factor analysis were compared with the Rasch measurement model using Mplus, IRTPRO and WINSTEPS program, each of which came from its own 'tradition'. The results of this study indicate that Rasch models and IFA as a special case of SEM are mathematically equal, as well as the Rasch measurement model, but due to different philosophical perspectives people might vary in their understanding about this concept. Given the findings of this study, it is expected that confusion and misunderstanding between the three can be overcome.

Development of the Anxiety Scale for Chemistry

2021 ◽  
Vol 11 (3) ◽  
pp. 106-112
Author(s):  
Canan Kocak Altunday ◽  
Aysem Seda Yucel
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Internal Consistency Reliability ◽  
Anxiety Scale ◽  
Item Pool ◽  
Expert Opinions ◽  
Total Correlation ◽  
Confirmatory Factor ◽  
Keywords Anxiety

Chemistry as a subject is seen by many students as an abstract and difficult lesson that bears no relationship to life. This study aimed to develop an ‘Anxiety Scale for Chemistry’, which is essential to setting an alternative for existing tools to determine anxiety for chemistry of students. This study has been carried out to develop a tool to measure the level of anxiety of students studying in the high schools in Turkey. The scale was developed as a result of the following processes: literature scan and creation of the item pool, taking expert opinions, item-total correlation, item distinctiveness features, exploratory factor analysis, Cronbach’s alpha internal consistency reliability, examination of correlation between sub-dimensions and confirmatory factor analysis. According to the results obtained, the scale is acceptably reliable for the research in social sciences. This study recommends that the Anxiety Scale for Chemistry should be administered to students of other fields also. Keywords: Anxiety, scale, chemistry, reliability, validity, factor analysis.

Development of the Caregiving Burden Scale for Family Caregivers of Children with Cancer

2021 ◽  
pp. 019394592110411
Author(s):  
Hamide Nur Çevik Özdemir ◽  
Selmin Şenol
Keyword(s):  
Factor Analysis ◽  
Family Caregivers ◽  
Delphi Technique ◽  
Psychosocial Health ◽  
Item Pool ◽  
Children With Cancer ◽  
Caregiving Burden ◽  
Pediatric Cancer Patients ◽  
Confirmatory Factor ◽  
Burden Scale

Due to the burden of caregiving, family caregivers of children with cancer suffer from physical and psychosocial health problems. The aim of this study was to develop a Caregiving Burden Scale for Family Caregivers of Children with Cancer (CBSFC-CC) and to assess its psychometric properties. A methodological, descriptive study design was conducted. The study sample consisted of 217 family caregivers caring for children with cancer between the ages of 0 and 18 years. The item pool of the scale was created using the Delphi Technique. The Cronbach’s alpha coefficient for the overall scale was 0.93. The item-total score correlations ranged from 0.424 to 0.645. The exploratory factor analysis showed that the scale explained 68.34% of the total variance. The confirmatory factor analysis also showed that the factor loadings of the scale ranged from 0.46 to 0.96. NNFI, CFI, and IFI were found to be > 0.90, and RMSEA was found to be < 0.09. The CBSFC-CC was found to be a good multidimensional instrument for evaluating the burden on family caregivers of pediatric cancer patients. It can be used in clinical practice and research. This tool can be considered to tailor interventions aimed at improving caregiver outcomes.

scholarly journals An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Meseret Getnet Meharie ◽  
Zachary C. Abiero Gariy ◽  
Raphael Ngumbau Ndisya Mutuku ◽  
Wubshet Jekale Mengesha
Keyword(s):  
Factor Analysis ◽  
Variable Selection ◽  
Cost Estimation ◽  
Construction Projects ◽  
Early Stage ◽  
Selection Process ◽  
Fuzzy Ahp ◽  
Geometric Mean ◽  
Limited Information ◽  
Input Variables

Accurate cost estimates are vital to the effective realisation of construction projects. Extended knowledge, wide-ranging information, substantial expertise, and continuous improvement are required to attain accurate cost estimation. Cost estimation at the preliminary phase of the project is always a challenge as only limited information is available. Hence, rational selection of input variables for preliminary cost estimation could be imperative. A systematic input variable selection approach for preliminary estimating using an integrated methodology of factor analysis and fuzzy AHP is presented in this paper. First, the factor analysis is used to classify and reduce the input variables and their variable coefficients are determined. Second, fuzzy AHP based on the geometric mean method is employed to determine the weights of input variables in a fuzzy environment where the subjectivity and vagueness are handled with natural language expressions parameterized by triangular fuzzy numbers. Then, the input variables are suggested to be selected starting with those having high coefficient and high importance weight. A set of three variables, one from each group, can be added to the estimating model at a time so that the problem of collinearity can vanish and good accuracy of the estimate can be ensured. The proposed approach enables cost estimators to better understand the complete input variable selection process at the early stage of project development and provide a more accurate, rational, and systematic decision support tool.

An Item Factor Analysis of the Mooney Problem Check List

1976 ◽  
Vol 36 (2) ◽  
pp. 509-513 ◽  
Author(s):  
David W. Stewart ◽  
Thomas Deiker
Keyword(s):  
Factor Analysis ◽  
Check List ◽  
Item Factor Analysis
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