Assessment of reliability using Cronbach’s α in survey research

Introduction

Survey research is one of the most widely used methods for collecting data in social sciences, management, education, agriculture, and health sciences. Researchers frequently use questionnaires and rating scales to gather information about attitudes, perceptions, opinions, and behaviours of respondents. The accuracy and usefulness of research findings depend largely on the quality of the measurement instrument employed. Therefore, it is essential to ensure that survey instruments are capable of producing consistent and dependable results before they are used for data analysis and decision-making (Sekaran & Bougie, 2019) [5] . One of the most frequently reported measures of reliability in survey research is Cronbach's α (α). Originally developed within the framework of classical test theory, Cronbach's α is commonly used to examine the internal consistency of a set of questionnaire items. In practical survey research, it is often calculated for Likert-type scales to determine the degree to which items are related to one another and can reasonably be considered part of the same scale (Streiner, 2003; Gliem et al., 2003) [6,10] . Because of its simplicity and availability in widely used statistical software, Cronbach's α has become a standard statistic in empirical research. Reliability is a fundamental characteristic of a good measurement instrument. It refers to the degree to which a scale or questionnaire yields consistent results when administered under similar conditions. An instrument with high reliability minimizes random measurement errors and enhances the credibility of the collected data. In survey research, where multiple items are often used to measure a single construct, assessing reliability becomes crucial for ensuring that all items contribute meaningfully to the measurement process (Nunnally & Bernstein, 1994) [4] . Among the various methods available for assessing reliability, Cronbach's Alpha is the most commonly used statistic for evaluating internal consistency. Introduced by Cronbach (1951) [1] , this coefficient measures the extent to which the items within a scale are correlated and collectively represent the same underlying construct. The value of Cronbach's Alpha ranges from 0 to 1, with higher values indicating greater internal consistency among scale items. Due to its simplicity and effectiveness, it has become a standard reliability measure in survey-based studies across different academic disciplines (Cronbach, 1951) [1] . The assessment of reliability using Cronbach's Alpha plays a significant role in questionnaire development and validation. Researchers use this technique to identify items that may weaken the consistency of a scale and to refine instruments for improved measurement accuracy. Reliability analysis not only strengthens the quality of research instruments but also contributes to the validity of subsequent statistical analyses and interpretations. A well-designed and reliable instrument increases confidence in research findings and supports evidence-based conclusions (DeVellis, 2017) [2] . The present study focuses on the assessment of reliability using Cronbach's Alpha in survey research. The objective is to examine the importance of internal consistency analysis and to evaluate the reliability of survey instruments used for data collection. By understanding and applying Cronbach's Alpha, researchers can improve the quality of measurement scales and ensure that their findings are based on reliable and consistent data. Consequently, reliability assessment serves as a critical step in enhancing the scientific rigor and credibility of survey research (Hair et al., 2019) [3] .

Another important issue concerns the assumptions underlying Cronbach's α. Traditional applications of α are associated with assumptions such as essential tau-equivalence, which implies particular relationships among the true-score components of the items. When these assumptions are violated, α may not provide an accurate estimate of the reliability of a scale. Raykov (1997) [9] demonstrated that violations of essential tau-equivalence can affect the interpretation of coefficient α, highlighting the need for researchers to understand the statistical conditions under which the coefficient is being applied. The widespread use of arbitrary cut-off values has also contributed to the misunderstanding of Cronbach's α. Researchers frequently classify scales according to fixed criteria, such as considering an α value of .70 or above to indicate acceptable reliability. However, such universal standards should not be applied mechanically to every research context. Lance, Butts, and Michels (2006) [13] examined the origins of commonly reported cut-off criteria and demonstrated that their widespread use does not necessarily provide a sufficient theoretical justification for treating a particular value as universally acceptable. The appropriate interpretation of reliability may depend on the purpose of the scale, the stage of research, the characteristics of the sample, and the consequences associated with measurement error. The limitations of Cronbach's α have been extensively discussed in psychometric literature. Sijtsma (2009) [8] argued that researchers often place excessive emphasis on α despite its limited ability to address broader questions of measurement quality. Similarly, Streiner (2003) [6] emphasized that coefficient α should be interpreted carefully rather than treated as an automatic indicator of a good measurement instrument. These discussions suggest that reliability assessment should move beyond reporting a single coefficient and should consider the theoretical structure and intended purpose of the scale. Psychometric theory provides a broader framework for understanding these issues. Raykov and Marcoulides (2011) [14] explain that reliability concerns the consistency and precision of measurement and can be approached through different models and estimators. Thus, Cronbach's α should not be viewed as the only possible method for evaluating the reliability of a multi-item instrument. Rather, its appropriateness should be considered in relation to the characteristics of the data, the measurement model, and the assumptions underlying the scale However, interpreting Cronbach's α is more complicated than simply determining whether its value exceeds a particular numerical threshold. A high α value does not necessarily demonstrate that a scale is unidimensional or that it provides a valid measurement of the intended construct. Internal consistency and validity represent different aspects of measurement quality, and reliability should therefore be considered as one component of a broader assessment of a research instrument (Streiner, 2003) [6] . In addition, α is influenced by factors such as the number of items in a scale and the relationships among those items. Consequently, an increase in the number of items may produce a higher α even when the items do not necessarily represent a stronger or more meaningful measurement of the construct (Cortina, 1993; Peterson, 1994) [11,12] .

Material and methods

Reliability test

The instrument validation processes that have been used in this study include content validity, construct validity and reliability. In order to have a reliable survey instrument and thus confidence in the research findings, the researcher employed content validity (interviews) as a pre-data collection validity, and a construct validity and reliability for post-data collection validity.

which is then subtracted from 1 large α.

Conclusions

In this research, awareness, infrastructure, trained personnel, advancement, supplementary resources, internet, content availability, strong opposition, and electricity have been identified as key constructs by the government for the effective implementation of any policy in Agriculture department. The main ICT-based constraints advanced personnel, extremely aware, strong opposition, internet, infrastructure, content availability, trained, slightly aware, language proficiency, and electricity explain a comparatively large amount of variance, while other factors explain only a small amount of variance in implementing policies for rural development. Based on this, we can conclude that these key factors can play a significant role for the government in effectively implementing any ICT-based scheme.

References

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