Validity and Reliability of a Questionnaire Students Name Institution of Affiliation Validity and Reliability of a Questionnaire A questionnaire is one of the most common and essential tools used in a research study to collect data

Validity and Reliability of a Questionnaire Students Name Institution of Affiliation Validity and Reliability of a Questionnaire A questionnaire is one of the most common and essential tools used in a research study to collect data. This is due to its numerous advantages such as practicality, cost efficiency scalability and user anonymity among others (Neuman, 2016). Despite these merits, the validity and reliability of a questionnaire design must be determined as done with the other research data collection techniques. This is to ensure the accuracy and consistency of the research tool in question (Bolarinwa, 2015). Validity, with regard to data collection tools, is the level to which a data collection technique determines what it is intended to measure. In a nutshell, validity refers to the extent to which data collection tools truly represents the actual meaning of the notion under study (Csikszentmihalyi, Larson, 2014). Determining validity ensure that the research questions are answered and the responses are obtained using the appropriate procedure and tool (Brace, 2018). The concept of reliability is used to refer to the extent to which the results acquired by a research tool can be reproduced or replicated (Zhang, Xiang, Cheng, Chen, 2017). This means that similar scores should be reached at each time the tool or procedure is used. In the case of data collection tools, a questionnaire is considered reliable if it obtains similar responses repeatedly. The degree of reliability of a questionnaire can be determined using correlation coefficients. Some of the types of validity include content validity, criterion-related, construct validity, face validity, concurrent validity and predictive validity. Content validity measures the coverage scope of the data collection tool with regard to the sample that reflects the domain of the aspects measured. It ensures the coverage of the full range of the topic (Zurlo, Pes, Capasso, 2013). Face validity refers to the degree to which a tool seems to be valid, criterion validity determines the accuracy with which a tool predict behaviour in a particular area, predictive validity attempts to foresee future performance, concurrent validity focuses on present performance and construct validity determines the accuracy with which the data collection tool measure theoretical concepts. To ensure the validity of a questionnaire design, a researcher needs to establish face validity of the tool, then carry out a pilot test on the target population. After pilot data collection, the information acquired is entered into a spreadsheet and cleaned. The analysis of principal components is then done followed by checking the internal consistency of questions using Cronbachs Alpha (Groth, Hartmann, Klie, Selbig, 2013). The last step is to revise the survey using principal components analysis and Cronbachs Alpha techniques (Bonett, Wright, 2015). Using the pilot test, determine the nature of the data collected and establish if it is nominal, ordinal or interval data. Internal consistency is then determined based on the nature of the data obtained (Heale, Twycross, 2015). The information can be examined with the help of the Statistical Package for Social Sciences (SPSS) and other statistical techniques such as the correlation matrix. A reliability coefficient level of 0.70 and above is accepted. If the alpha level is below 0.70 then the questionnaire designed is considered unreliable. In conclusion, data collection is an important procedure for all research studies. This is because the analysis of this information is used to make conclusions and find answers to the issue or problem being covered (Herrmann, Heumann, Der Ananian, Ainsworth, 2013). In this sense, it is essential to ensure the reliability and validity of the tool used for data collection. If the tool does not meet the reliability and validity standards then there is a chance that the results of the study will be unreliable as well. References Bolarinwa, O. A. (2015). Principles and methods of validity and reliability testing of questionnaires used in social and health science researches.Nigerian Postgraduate Medical Journal,22(4), 195. Bonett, D. G., Wright, T. A. (2015). Cronbachs alpha reliability Interval estimation, hypothesis testing, and sample size planning.Journal of Organizational Behavior,36(1), 3-15. Brace, I. (2018).Questionnaire design How to plan, structure and write survey material for effective market research. Kogan Page Publishers. Csikszentmihalyi, M., Larson, R. (2014). Validity and reliability of the experience-sampling method. InFlow and the foundations of positive psychology(pp. 35-54). Springer, Dordrecht. Groth, D., Hartmann, S., Klie, S., Selbig, J. (2013). Principal components analysis. InComputational Toxicology(pp. 527-547). Humana Press, Totowa, NJ. Heale, R., Twycross, A. (2015). Validity and reliability in quantitative studies.Evidence-based nursing, ebnurs-2015. Herrmann, S. D., Heumann, K. J., Der Ananian, C. A., Ainsworth, B. E. (2013). Validity and reliability of the global physical activity questionnaire (GPAQ).Measurement in Physical Education and Exercise Science,17(3), 221-235. Neuman, W. L. (2016).Understanding research. Pearson. Zhang, F., Xiang, L. I., Cheng, S., Chen, C. (2017). Reliability and validity of the Chinese version of Safety Attitudes Questionnaire.Chinese Journal of Practical Nursing,33(4), 250-254. Zurlo, M. C., Pes, D., Capasso, R. (2013). Teacher Stress Questionnaire validity and reliability study in Italy.Psychological reports,113(2), 490-517. QUESTIONNAIRE VALIDITY AND RELIABILITY PAGE MERGEFORMAT 2 Running head QUESTIONNAIRE VALIDITY AND RELIABILITY 1 Y, B8L 1(IzZYrH9pd4n(KgVB,lDAeX)Ly5otebW3gpj/gQjZTae9i5j5fE514g7vnO( ,[email protected] /[email protected] 6Q