An audit of the statistics and the comparison with the parameter in the population

Bujang, Mohamad Adam; Sa’at, Nadiah; Joys, A. Reena; Ali, Mariana Mohamad

doi:10.1063/1.4932510

Cited by 16 publications

(19 citation statements)

References 16 publications

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“…In other words, besides content validity, the conceptualization of the three domains was successfully validated. A sufficient sample size in addition to the fact that sufficient sample is needed to conduct factor analysis and to capture an accurate pattern of the responses from the respondents in the intended population is presented [31, 32]. The findings were also supported by Rasch analysis.…”

Section: Discussionmentioning

confidence: 90%

A Revised Version of Diabetes Quality of Life Instrument Maintaining Domains for Satisfaction, Impact, and Worry

Bujang

Adnan

Hatta

et al. 2018

Journal of Diabetes Research

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Background Diabetes quality of life (DQoL) instrument has been widely used to measure quality of life among diabetes patients. This study aimed to develop a revised version of DQoL instrument that incorporated issues of redundancies in the items and strengthen the basis of validity of the instrument. Methods This was a cross-sectional study where diabetes patients were recruited from December 1, 2014, until end of March 2015 at a public health clinic in Peninsular Malaysia. A questionnaire that included patients' information and DQoL instrument was distributed to patients. Item selection of DQoL instrument was conducted to screen and finalize the items based on issues of missing values and redundancy. Validity testing was conducted for the revised DQoL instrument based on exploratory factor analysis, confirmatory factor analysis, and Rasch analysis. Results The pattern structure matrix yielded three domains similar to the original version with 18 items. The minimum factor loading from the structure matrix was 0.358. The item's and person's reliability was excellent with 0.92 and 0.84 for “satisfaction” domain, 0.98 and 0.60 for “impact” domain, and 0.99 and 0.57 for “worry” domain, respectively. Confirmatory factor analysis has dropped 5 items and the revised version of DQoL contained 13 items. Composite reliability of the revised version was computed for “satisfaction” domain (0.922; 95% CI: 0.909–0.936), “impact” domain (0.781; 95% CI: 0.745–0.818), and “worry” domain (0.794; 95% CI: 0.755–0.832). Conclusion A revised version of DQoL that maintains the conceptualization of “satisfaction,” “impact,” and “worry” with 13 items was successfully developed.

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Section: Discussionmentioning

confidence: 90%

A Revised Version of Diabetes Quality of Life Instrument Maintaining Domains for Satisfaction, Impact, and Worry

Bujang

Adnan

Hatta

et al. 2018

Journal of Diabetes Research

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“…This is because, most inferential studies rely on the P -value less than 0.05 or 0.01 as the indicator of evidence for inference where the parameters remain unknown until census study is conducted for a particular population (22–23). Therefore, to ensure the estimates are valid, it is recommended that research studied to be conducted with a sufficient sample size especially when the analysis involves multivariable analysis and this is usually the case for observational studies (11–12, 24–27).…”

Section: Discussionmentioning

confidence: 99%

Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data

Sa’at

Tmitab

et al. 2018

MJMS

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367

281

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BackgroundDifferent study designs and population size may require different sample size for logistic regression. This study aims to propose sample size guidelines for logistic regression based on observational studies with large population.MethodsWe estimated the minimum sample size required based on evaluation from real clinical data to evaluate the accuracy between statistics derived and the actual parameters. Nagelkerke r-squared and coefficients derived were compared with their respective parameters.ResultsWith a minimum sample size of 500, results showed that the differences between the sample estimates and the population was sufficiently small. Based on an audit from a medium size of population, the differences were within ± 0.5 for coefficients and ± 0.02 for Nagelkerke r-squared. Meanwhile for large population, the differences are within ± 1.0 for coefficients and ± 0.02 for Nagelkerke r-squared.ConclusionsFor observational studies with large population size that involve logistic regression in the analysis, taking a minimum sample size of 500 is necessary to derive the statistics that represent the parameters. The other recommended rules of thumb are EPV of 50 and formula; n = 100 + 50i where i refers to number of independent variables in the final model.

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“…Therefore, taking large sample is also reasonable since it will increase the accuracy of the estimates. Some studies suggest that by taking sample of more than 300, the statistics that will be derived from the sample will be likely the same with the true value in the intended population (Bujang et al, 2009;Bujang et al, 2015). This guideline perhaps is suitable for survey that has relatively large population.…”

Section: Other Considerationsmentioning

confidence: 99%

Sample Size Guideline for Correlation Analysis

Bujang

Baharum

2016

WJSSR

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215

124

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An audit of the statistics and the comparison with the parameter in the population

Cited by 16 publications

References 16 publications

A Revised Version of Diabetes Quality of Life Instrument Maintaining Domains for Satisfaction, Impact, and Worry

A Revised Version of Diabetes Quality of Life Instrument Maintaining Domains for Satisfaction, Impact, and Worry

Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data

Sample Size Guideline for Correlation Analysis

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