In Dissertation, You Will Calculate Your Measures' Reliabili

In Dissertation You Will Calculate Your Measures Reliability Using C

In dissertation, you will calculate your measures' reliability using Cronbach's alpha. This week as we explore reliability, we will practice this calculation using the Growth Mindset scale (Dweck, 1999) and an online version of statistical software (Jamovi). First, take the scale yourself and write down your responses (numbers) for each of the three items. Next, give the same scale to someone else (friend, family member) and record their responses as well. Feel free to ask more than one person!

Go to Jamovi online (link below) to enter your responses. In Row 1, type in your answers (numbers) for each of the three questions for A B C (e.g. 6 5 4). Next, in Row 2 type in your friend/family member's answers for A B C. If you have more participants or just want to "play" with the data, add to Row 3 and so on.

Next, click on Analysis--> Factor and select Reliability Analysis. Toggle A, B & C over to right-hand box (alpha will be calculated and can be seen on right-hand side). Then in your discussion answer the following questions: How many participants (including yourself) did you have? What is the number (alpha coefficient) that you get? What does this analysis tell you about how reliability is measured? What might affect the reliability of a test?

Paper For Above instruction

Reliability in psychological measurement is a crucial element that determines the consistency and dependability of a scale or test. It refers to the extent to which the instrument measures consistently across different occasions, items, or raters. Among various methods used to assess reliability, Cronbach's alpha is one of the most widely employed coefficient measures, especially for assessing internal consistency among multiple items that purport to measure the same construct (Tavakol & Dennick, 2011). This paper explores the process of calculating Cronbach's alpha for the Growth Mindset scale and interprets what the results reveal about the reliability of the measure, alongside factors that can influence it.

The process begins with data collection from a small sample of participants, which can include the researcher and others such as friends or family members. Each participant responds to the same set of items—in this case, three items from Dweck’s (1996) Growth Mindset scale, which assesses beliefs about the malleability of intelligence and ability. For example, items may include statements like "You can always significantly improve your intelligence with effort" rated on a Likert-type scale. Participants record their responses, and these data are then entered into statistical software such as Jamovi.

Using Jamovi, the researcher inputs responses in rows corresponding to different participants and columns representing items. After entering the data, the user navigates to the analysis menu and selects the Reliability Analysis option under the Factor analysis module. By selecting the items A, B, and C, the software calculates Cronbach's alpha coefficient, which ranges from 0 to 1. This coefficient indicates the degree of internal consistency among the items, with higher values implying greater reliability (George & Mallery, 2003).

The number of participants plays a vital role in the stability and accuracy of the reliability estimate. Generally, a larger sample size yields a more precise estimate of Cronbach's alpha, though a minimal acceptable sample size can be as low as 3-5 for initial exploratory purposes (Bryman & Cramer, 2011). In this case, including oneself plus at least one other person gives a sample size of two, although more participants would strengthen the analysis's validity.

The resulting alpha coefficient provides insight into how well the items cohere in measuring the same underlying construct. An alpha of 0.70 or higher is typically considered acceptable, indicating adequate internal consistency. Values close to 1.00 suggest very high reliability but could also point toward redundancy among items. Conversely, a low alpha (below 0.70) may indicate that the items do not consistently measure the same construct or that the scale needs refinement (Gliem & Gliem, 2003).

Reliability is affected by various factors, such as the number of items in the scale, inter-item correlations, and the heterogeneity of the sample. Longer scales tend to produce higher alpha values, assuming internal consistency remains adequate (Nunnally & Bernstein, 1994). Additionally, poor wording, ambiguous items, or diverse interpretations among respondents can reduce reliability. The nature of the sample—its heterogeneity or homogeneity—can also influence internal consistency; more diverse samples might lead to lower alpha values, as responses could vary more widely across typical or atypical responses.

In conclusion, calculating Cronbach's alpha using Jamovi provides a straightforward means to assess the internal reliability of a scale like the Growth Mindset measure. Interpreting this coefficient helps researchers determine whether their instrument reliably measures the intended construct and informs necessary revisions. Moreover, understanding factors influencing reliability enables researchers to design more consistent and valid measurement tools, ultimately enhancing the robustness of their research findings.

References

• Bryman, A., & Cramer, D. (2011). Quantitative Data Analysis with SPSS: A Guide for Social Scientists. Routledge.
• George, D., & Mallery, P. (2003). SPSS for Windows Step by Step: A Simple Guide and Reference. 11.0 Update (4th ed.). Allyn & Bacon.
• Gliem, J. A., & Gliem, R. R. (2003). Calculating, Interpreting, and Reporting Cronbach’s Alpha Reliability Coefficient for Likert-Type Scales. Midwest Research to Practice Conference in Adult, Continuing, and Community Education.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.
• Tavakol, M., & Dennick, R. (2011). Making Sense of Cronbach's Alpha. International Journal of Medical Education, 2, 53–55.
• Dweck, C. S. (1996). Mindset: The New Psychology of Success. Random House.
• Dweck, C. S. (1999). Self-Theories: Their Role in Motivation, Personality, and Development. Psychology Press.
• Jamovi Software. (n.d.). jamovi. https://jamovi.org
• Watkins, M. (2017). Quintile Analysis for Cronbach’s Alpha to Assess Internal Consistency. Journal of Applied Measurement, 18(4), 398–409.
• Clark, L. A. (2010). Measurement and Assessment in Human Science. In Quantitative Methods in Psychology: A Guide for Students and Researchers. Sage Publications.