Reliability and validity are significant ideas and major concerns in research as they are used to enhance the accuracy and consistency of the assessment and evaluation of a research study (Tarakol and Dennick 2011). A research study is reliable when the findings are repeatable (Serem et al. 2013; Saunders et al. 2012; Babbie 2004). On the other hand, research is said to be valid when the conclusions are true or correct (McBurney and White 2010). Saunders et al. (2012) explain that reliability is the ability of the data collection techniques and analytic procedures to produce consistent findings if they are repeated on another occasion or if a different researcher replicated them. This sentiment concurs with that of Payne and Payne (2004) that reliability is the property of a measuring device for social phenomena (particularly in the quantitative methods tradition, which yields consistent measurement when the phenomena are stable regardless of who uses it, provided the underlying conditions remain the same.
To establish the degree of reliability, scholars have developed several different techniques, for instance, test-retest, split-half method, using established measures and parallel forms (Serem et al.
2013; Saunders et al. 2012; Krishnaswami and Ranganathan 2010). Serem et al. (2013) explain that in test-retest approach the same data collection instrument is used more than once, with the same group of people and the results compared statistically. As the same instrument has been used with the same group, theoretically, there should be a strong correlation (relationship) between the two data sets, so a statistical measure of the strength of the relationship between the two is calculated. The name of this test is the correlation coefficient, and in practical terms, a value of 0.3 - 0.7 is needed to regard the instrument as having sufficient reliability. Using established measures is another degree in resolving the problem of reliability. Serem et al. (2013) state that this involves the use of an instrument that has already been validated. On split-half method, Saunders et al.
(2012) state that the questions are randomly split into two sets and responses from each set correlated with the other set and the two should measure the variable in question in the same way.
Validity, on the other hand, is the degree to which an inference, conclusion or measurement corresponds precisely to the real world and offers the best possible approximation of its truth.
Thatcher (2010); and Kothari (2004) are of the opinion that validity is the extent to which the instrument measures what it is supposed to measure. This implies that whether research accurately measures the things that it is aimed to measure or how appropriate (close to the truth) the results
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of the research are (Gibbs 2012). According to Serem et al. (2013) there should be a clear relationship between the way a concept is defined, and the way it is operationalised. This measure aims to assess whether or not the relationship is well established, or whether there is a gap between the information that was sought, and the data collected.
There are a number of basic methods of testing validity (Serem et al. 2013; Saunders 2012; Yin 2003) including, construct validity (refers to establishing correct operational measures for the concepts being studied), internal validity (involves establishing a causal relationship between two variables that means certain conditions are shown to lead to other conditions), external validity (establishing the domain to which a study's findings can be generalised. It is concerned with the questions; can a study's research findings be generalised to other relevant settings or groups?) and content validity (the extent to which a measuring instrument provides adequate coverage of the topic under study. Pre-testing is an example of a technique used in content validation).
Reliability and validity of data collection tools was assessed in various ways. A pre-test was conducted at Kisii University (see appendices 8 and 11 respectively). This method is supported by Blanke and Simone (2009) who state that a pre-test should be done under circumstances that are similar as possible to actual data collection and with a population as similar as possible to those that will be involved in data collection. Casper and Peytchera (2011) observe that pre-testing involves a series of activities designed to evaluate an instrument's capacity to collect the desired data, the capabilities of the selected mode of data collection and the overall adequacy of the field procedures. The authors' further state that pre-testing takes place before the actual data collection to enable identification of errors and suggest ways of improving the instruments to achieve accuracy and consistency. The pre-test in this study used a randomly selected sample of 20 respondents in the category of records managers and action officers who were asked to complete the questionnaires. The researcher interviewed one Deputy Vice-Chancellor, one registrar, two deans, and two directors. Data collected were analysed to generate information for instance on appropriate use of language and logical flow of the questions that was used to refine the questionnaire.
The internal consistency of responses in the study was tested using the Cronbach alpha (α) statistics. The statistical test was applied to measure the internal consistency of responses for individual questions with multiple items in the questionnaire after the pre-test. Maseh (2015) and
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