Validity in surveys used in business research refers to the extent to which a survey instrument accurately measures what it intends to measure. It assesses whether the survey questions and items effectively capture the constructs or variables of interest. Validity is crucial in ensuring the survey results are reliable and meaningful for making informed business decisions (Blanche, Bodison, Chang & Reinoso, 2012).
Factor analysis & Surveys validity:
Factor analysis is a statistical technique commonly employed in business research to uncover underlying factors that explain the correlations among variables. Its application in surveys can significantly enhance their validity by ensuring that they accurately measure the intended constructs. For instance, when assessing customer satisfaction with a company's products or services through a survey, it can be challenging to distinguish which items truly capture customer satisfaction versus other factors like product quality or customer loyalty when the survey contains numerous items. Factor analysis enables the identification of the latent factors that elucidate the associations among the survey items. This information empowers researchers to refine the survey by eliminating items not aligning with the desired construct (Ang & Huan, 2006).
Beyond enhancing survey validity, factor analysis also facilitates the identification of novel constructs that may not have been explicitly measured. For example, an employee satisfaction survey may primarily target the work environment, but factor analysis could reveal an underlying factor related to employee stress. This finding could inform the development of interventions to reduce employee stress levels. Factor analysis contributes to survey validity in several ways. It identifies common factors among survey items, aiding the understanding of interrelationships and underlying constructs. Additionally, it helps create shorter and more efficient surveys by identifying the most relevant items strongly linked to the constructs. Furthermore, factor analysis enables the discovery of new constructs by uncovering patterns of correlations among survey items (Ferrell, Stein & Beck, 2000).
Applying to my project:
Factor analysis can be used in my project about construction pollution in several ways:
1. Identify common factors among types of construction pollution: I could use factor analysis to identify common factors among different types of construction pollution, such as air pollution, water pollution, and noise pollution. This could help me to understand better the different ways in which construction pollution can impact the environment.
2. Develop a shorter, more efficient survey to measure construction pollution: I could use factor analysis to develop a shorter, more efficient survey to measure construction pollution. This could be done by identifying the items most strongly related to construction pollution. By removing items that are not strongly related to the construct, I could create a shorter and easier survey to complete while still measuring the same construct.
3. Identify new constructs related to construction pollution: I could use factor analysis to identify new constructs related to construction pollution. This could be done by looking for patterns of correlations among the items on my survey (Seashore & Yuchtman, 1967). By identifying these patterns, I could develop new constructs that can be us help me better stand the data exclusion
Factor analysis is a powerful statistical technique that bolsters the validity and reliability of surveys employed in business research. By uncovering common factors, streamlining survey length, and revealing hidden constructs, factor analysis empowers researchers to gain deeper insights from survey data and make well-informed decisions.
References
Ang, R. P., & Huan, V. S. (2006). Academic expectations stress inventory: Development, factor analysis, reliability, and validity. Educational and
psychological measurement, 66(3), 522-539.
Blanche, E. I., Bodison, S., Chang, M. C., & Reinoso, G. (2012). Development of the Comprehensive Observations of Proprioception (COP): Validity,
reliability, and factor analysis. The American journJournal of Occupational Therapy6), 691-698.
Ferrell, B. A., Stein, W. M., & Beck, J. C. (2000). The Geriatric Pain Measure: validity, reliability and factor analysis. Journal of the American Geriatrics
Society, 48(12), 1669-1673.
Seashore, S. E., & Yuchtman, E. (1967). Factorial analysis of organizational performance. Administrative science quarterly, 377-395.