Cancer Screening Perception Scale: Development and Construct Validation

Abstract

The aim of this study was to demonstrate the construct validity of a newly developed cancer screening perception scale as a measure of the perception of cancer screening in general among high-risk but healthy asymptomatic groups.The cancer screening perception scale (CSPS) was developed based on extensive literature reviews guided by The Health Belief Model. Fifty-five written items were initially pooled, reviewed by experts for face validity, pretested by 25 healthcare workers and translated into Malay using simple back translation. The scale was then distributed to 300 respondents from two health clinics for construct validation purposes. The obtained data were analyzed using the varimax rotation method for exploratory factor analysis (EFA). The data was submitted for further confirmatory factor analysis using AMOS software.Based on EFA, the results produced five constructs as predicted: perceived severity, perceived susceptibility, perceived benefits, perceived barriers, and cues for action. Two items with low factor loading and unrelated to the recovered domains were removed. Perceived barriers and cues for action had three and two sub-domains respectively which were further confirmed to fit the measurement and structural models. CFA demonstrated the scale fitted GFI = 0.936, CFI = 0.935, RMSEA = 0.076, NORMEDCHISQ = 2.162. The scale discriminated between the domains. Cronbach's alpha for perceived severity, perceived susceptibility, perceived benefits, perceived barrier, and cues for action were 0.907, 0.877, 0.940, 0.864 and 0.938, respectively.The cancer screening perception scale with its promising psychometric properties is now available to measure risks to high-risk but healthy, asymptomatic groups aged 18 and above and can also be used for larger scale study purposes.

Factor Analysis of a Novel Scoring-Based Instrument on Forecasting Malaysian Travelers’ Behavioral Preparedness for Travel-Related Infectious Diseases

“…Several established methods exist for validating questionnaires, including the qualitative approach 21 ; however, validation using factor analysis involving both exploratory and confirmatory components is not uncommon and, indeed, has become more prominent recently due to its capability of producing quantitative and objective parameters for both validity and reliability aspects. 22,23 While the EFA is used to factorize all the items into their common factors based on the patternlinearity of the factor loadings, CFA complements the method by confirming and ensuring the discriminant and convergent validity (construct validity) of the extracted factors and their respective items. 22,24,25 As has been shown by the results of this study, factor analysis through CFA has complemented the other validity components of this questionnaire (the face and content validity) by proving that it also has good construct validity with acceptable discriminant and convergent validity.…”

“…22,23 While the EFA is used to factorize all the items into their common factors based on the patternlinearity of the factor loadings, CFA complements the method by confirming and ensuring the discriminant and convergent validity (construct validity) of the extracted factors and their respective items. 22,24,25 As has been shown by the results of this study, factor analysis through CFA has complemented the other validity components of this questionnaire (the face and content validity) by proving that it also has good construct validity with acceptable discriminant and convergent validity. Moreover, CFA has also proven that this questionnaire has good internal consistencies for all of its measured domains, all of which have a CR of above 0.7 and are comparable to the conventionally calculated Cronbach α coefficient.…”

Development of a Diagnostic Questionnaire for Damp Phlegm Pattern and Blood Stasis Pattern in Coronary Heart Disease Patients (CHD-DPBSPQ)

“…Used to Evaluate the Scale. SPSS version 22.0 and Amos software 22.0 were used for data analysis [41,42].…”

The chronic kidney disease perception scale (CKDPS): development and construct validation

“…Those at a higher risk of developing CKD were not screened, making them particularly vulnerable to a late diagnosis [6]. Proactive interpretation has led to people with symptoms being more likely to receive treatment for any health problem and to frequently use medical services.…”

“…They could also ask the main researcher for clarity as to whether their response was vague or complicated. To ensure there were no missing answers, their replies were thoroughly checked [6]. Structural Equation Modeling (SEM) is a very powerful multivariate test method that describe a network of relationships between variables.…”