Reliability Analysis #

Reliability refers to the stability and consistency of a measurement tool, assessing whether the tool can produce consistent results across different times or situations. Common methods to assess reliability include:

• Test-Retest Method: Administer the same questionnaire to the same group at different time points and calculate the correlation between the two sets of results, assessing the stability of the measurement tool.
• Equivalent-Forms Method: Use two measurement tools that are similar in content and difficulty, administer them to the same group, and calculate the correlation between the two results.
• Split-Half Method: Divide a questionnaire into two halves and calculate the correlation between the two halves to assess internal consistency.
• Cronbach’s α Coefficient: This coefficient is used to assess the internal consistency of a scale, typically applied to multiple-choice questionnaires. The higher the Cronbach’s α value, the higher the reliability of the scale. Generally, a value above 0.7 indicates good reliability.

Cronbach’s α Reliability Table:

• 0.9 and above: Excellent reliability
• 0.8 - 0.9: Good reliability
• 0.7 - 0.8: Acceptable reliability
• Below 0.7: Poor reliability

Validity Analysis #

Validity refers to whether the measurement tool accurately measures what it is supposed to measure. The more closely the results align with the target concept, the higher the validity.

Validity can be divided into three types:

1. Content Validity: Refers to whether the items in the measurement tool represent the content or concept to be measured. Content validity is typically assessed through expert judgment and logical analysis.

   • Statistical Method: Use item-total correlation analysis to assess the correlation between individual items and the total score.

2. Criterion Validity: Refers to the correlation between the measurement tool and a criterion (such as an external standard or another test). Criterion validity is assessed through correlation analysis or significance testing.

   • Predictive Validity: Assesses whether the tool can predict future outcomes or behaviors.
   • Concurrent Validity: Assesses the correlation between the tool and an established standard at the same time.

3. Construct Validity: Refers to whether the tool accurately reflects the underlying concept or structure it is intended to measure. Construct validity is often assessed through factor analysis.

   • Factor Analysis: Extract latent factors from the questionnaire data and assess whether they align with the hypothesized structure.
   • Key Indicators: Cumulative explained variance, communalities, and factor loadings. Cumulative explained variance reflects the extent to which the factors explain the scale, while factor loadings indicate the correlation between each variable and the common factor.

Pre-Factor Analysis Testing #

Before performing factor analysis, the data should be tested for the following:

• KMO (Kaiser-Meyer-Olkin) Test: The KMO value should be greater than 0.5, indicating that the data is suitable for factor analysis.
• Bartlett’s Test of Sphericity: A p-value less than 0.05 indicates that the data can be used for factor analysis.

These tests ensure that the data is appropriate for structural validity analysis.

Practical Application In practice, statistical software (such as SPSS) is used to conduct reliability and validity analysis. Reliability analysis primarily focuses on the internal consistency of the scale, while validity analysis verifies whether the scale accurately measures the intended construct.

For example:

1. Use Cronbach’s α coefficient to assess the internal consistency of the questionnaire.
2. Conduct factor analysis to assess the structural validity of the questionnaire, ensuring that the items reflect the core concept being measured.

#SPSS

Last modified on 2024-01-04