Cronbach's Alpha_Reliability Analysis | Data Analysis in IBM SPSS || Explained in Filipino

Summary

This video tutorial explains how to assess the reliability and internal consistency of a questionnaire using SPSS, focusing on Likert-scale items. It demonstrates the process of analyzing responses from 100 participants across seven items measuring a specific construct, such as anxiety. The tutorial covers calculating Cronbach's alpha, interpreting the coefficient (with 0.7 as the general benchmark), and examining item-total correlations to identify problematic items, such as negatively correlated ones that may need deletion. The video also briefly touches on separate constructs like mental exhaustion and cynicism, emphasizing practical steps for ensuring a questionnaire's reliability in research.

Takeaways

• 😀 The video tutorial focuses on assessing the reliability or internal consistency of a questionnaire using Cronbach's alpha in SPSS.
• 😀 The example questionnaire measures anxiety with 7 items and 100 respondents using a Likert scale (1–4, 1–5, or 1–7).
• 😀 Cronbach's alpha is used to determine how well the items in the questionnaire measure the same underlying construct.
• 😀 Inter-item correlations and covariances are examined to evaluate the relationship between items.
• 😀 Item-Total Statistics in SPSS help identify problematic items that may reduce overall reliability.
• 😀 The initial Cronbach's alpha for the 7-item questionnaire is 0.678, slightly below the generally accepted threshold of 0.7.
• 😀 Item 6 shows a negative corrected item-total correlation, suggesting it does not align with the other items.
• 😀 Deleting Item 6 improves Cronbach's alpha to 0.743, making the scale more reliable and acceptable.
• 😀 Items measuring other constructs like mental exhaustion and cynicism should be analyzed separately for reliability.
• 😀 The tutorial emphasizes reviewing item performance and making decisions based on corrected item-total correlations to ensure scale reliability.
• 😀 Cronbach's alpha values above 0.7 are acceptable, above 0.8 are good, and above 0.9 are excellent for research purposes.
• 😀 SPSS reliability analysis involves entering the data, selecting items, running the analysis, and interpreting the output carefully.

Q & A

What is the purpose of calculating Cronbach's alpha in a questionnaire?

Cronbach's alpha is used to measure the internal consistency or reliability of a set of items in a questionnaire, indicating how well the items collectively measure a particular construct.

How many items were included in the example questionnaire discussed in the video?

The example questionnaire included 7 items designed to measure a particular construct, such as anxiety.

What was the sample size used in the reliability analysis example?

The sample size used was 100 respondents.

What response scales were mentioned in the video for questionnaire items?

The video mentioned response scales such as 1–4, 1–5, and 1–7, depending on the item.

What is the generally accepted threshold for Cronbach's alpha to consider a scale reliable?

A Cronbach's alpha of 0.7 or above is generally considered acceptable for reliability.

What does a negative corrected item-total correlation indicate?

A negative corrected item-total correlation indicates that the item is inversely related to the overall scale and may need to be deleted to improve reliability.

In the example, what was the corrected item-total correlation for item 6, and what does it signify?

Item 6 had a corrected item-total correlation of 0.743, which is positive and indicates that the item contributes well to the scale, so no deletion was necessary.

What was the overall Cronbach's alpha for the 7-item questionnaire?

The overall Cronbach's alpha for the 7-item questionnaire was 0.678, which is slightly below the recommended threshold of 0.7 but still reasonably acceptable.

What other constructs were mentioned that could be measured using separate items?

Other constructs mentioned include anxiety, mental exhaustion, and cynicism, which can be measured using separate items for each construct.

Why is it important to check item variance in reliability analysis?

Checking item variance is important because items with very low variance contribute less to overall reliability and may reduce the Cronbach's alpha value.

How can SPSS assist in conducting reliability analysis?

SPSS can automate the calculation of Cronbach's alpha, inter-item correlations, corrected item-total correlations, and alpha-if-item-deleted values, making reliability analysis more efficient and accurate.