Background
The European Health Literacy Survey Questionnaire (HLS-EU-Q47) is becoming a widely used tool to measure health literacy (HL), including in Malaysia. There are efforts to reduce the 47 item scale to parsimonious short item scales that still reflects the assumptions and requirements of the conceptual model. This study used confirmatory factor analysis to reduce the 47 item scale to a short scale that can offer a feasible HL screening tool with sufficient psychometric properties.
Methods
A cross-sectional survey was conducted on the Malaysian population based on racial distribution to ensure that the short version instrument reflects the country’s varied ethnicities. The survey was administered by well-trained interviewers working for the Ministry of Health Malaysia. A total of 866 responses were obtained. Data was analysed using multi-factorial confirmatory factor analysis (CFA) with categorical variables.
Results
The analysis resulted to a satisfactory 18-item model. There were high correlations among the 18 items. The internal consistency reliability was robust, with no floor/ceiling effects. These results represented equivalence and consistency among the responses to items, suggesting that these items were homogenous in measuring people's health literacy. The strong convergent and discriminant validity of the model makes the proposed 18 items a suitable short version of health literacy instrument.
Conclusions
The researchers proposed the 18 items to be named HLS-M-Q18. This short version instrument may be used in measuring the health literacy index in Malaysia as it achieved robust reliability, structural validity and construct validity that fulfilled goodness-of-fit criteria.
Figure 1
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On 13 Apr, 2020
On 12 Apr, 2020
On 12 Apr, 2020
On 11 Apr, 2020
On 30 Mar, 2020
Received 30 Mar, 2020
Invitations sent on 26 Mar, 2020
On 25 Mar, 2020
On 24 Mar, 2020
On 24 Mar, 2020
Posted 16 Jan, 2020
On 28 Feb, 2020
Received 03 Feb, 2020
On 21 Jan, 2020
Invitations sent on 11 Nov, 2019
On 24 Sep, 2019
On 23 Sep, 2019
On 23 Sep, 2019
On 13 Apr, 2020
On 12 Apr, 2020
On 12 Apr, 2020
On 11 Apr, 2020
On 30 Mar, 2020
Received 30 Mar, 2020
Invitations sent on 26 Mar, 2020
On 25 Mar, 2020
On 24 Mar, 2020
On 24 Mar, 2020
Posted 16 Jan, 2020
On 28 Feb, 2020
Received 03 Feb, 2020
On 21 Jan, 2020
Invitations sent on 11 Nov, 2019
On 24 Sep, 2019
On 23 Sep, 2019
On 23 Sep, 2019
Background
The European Health Literacy Survey Questionnaire (HLS-EU-Q47) is becoming a widely used tool to measure health literacy (HL), including in Malaysia. There are efforts to reduce the 47 item scale to parsimonious short item scales that still reflects the assumptions and requirements of the conceptual model. This study used confirmatory factor analysis to reduce the 47 item scale to a short scale that can offer a feasible HL screening tool with sufficient psychometric properties.
Methods
A cross-sectional survey was conducted on the Malaysian population based on racial distribution to ensure that the short version instrument reflects the country’s varied ethnicities. The survey was administered by well-trained interviewers working for the Ministry of Health Malaysia. A total of 866 responses were obtained. Data was analysed using multi-factorial confirmatory factor analysis (CFA) with categorical variables.
Results
The analysis resulted to a satisfactory 18-item model. There were high correlations among the 18 items. The internal consistency reliability was robust, with no floor/ceiling effects. These results represented equivalence and consistency among the responses to items, suggesting that these items were homogenous in measuring people's health literacy. The strong convergent and discriminant validity of the model makes the proposed 18 items a suitable short version of health literacy instrument.
Conclusions
The researchers proposed the 18 items to be named HLS-M-Q18. This short version instrument may be used in measuring the health literacy index in Malaysia as it achieved robust reliability, structural validity and construct validity that fulfilled goodness-of-fit criteria.
Figure 1
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