According to Pellissier (2007), the quality and rigor of research stem from the validity and reliability of the research methodologies. The researcher needs to apply specific evaluation techniques to enhance data quality and rigor. The following sections discuss how data quality and rigor were insured in the present study.
5.6.1 Data quality and rigor in Qualitative studies
Qualitative studies do not usually use standardized research instruments (Saunders et al., 2016), and they often adopt non-probability sampling techniques and smaller sample sizes (Sekeran & Bougie, 2013; Bryman, 2016). Because of this, assessing the truthfulness of qualitative findings is difficult.
Qualitative researchers normally use four criteria for ensuring data quality namely, credibility, dependability, transferability and confirmability (Lincoln & Guba, 1985; Denzel, 2009; Bryman, 2012).
These criteria are analogous to internal and external validity, neutrality, and reliability in quantitative studies (Pellissier, 2007). The next section presents a discussion of each criterion.
5.6.1.1 Credibility
Credibility is analogous to internal validity (Pellissier, 2007). Credibility is the extent to which the data collected and the results are believable and trustworthy (Creswell & Plano Clark, 2011). Merriam (2009) credibility addresses the question "How congruent are the research findings with reality?" The researcher employed well-established research methods and tools to ensure credibility in the study. The use of a collection instrument that is questionnaires and two different interview guides. The researcher also critically analysed the data collected before documenting the findings. The researcher made sure that there was a link to the findings with the reviewed literature.
5.6.1.2 Dependability
Dependability is analogous to reliability (Polit & Beck, 2012; Riege, 2003), that is, the extent to which the same results are observed under similar circumstances (Wahyuni, 2012). It is the extent to which study results can be replicated (Merriam, 1998). Dependability is often problematic and almost practically impossible as the behaviour of researchers and subjects varies continuously depending on various factors (Pellissier, 2007). In a way to enhance reliability, the researcher provided an in-depth methodological description to allow for the study to be replicated with the same results being discovered and new data emerging. The researcher also took reasonable time and care in ensuring that the process
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of research was logical and traceable. Thus, the researcher documented the research process, giving all the details.
5.6.1.3 Transferability
Transferability corresponds to external validity in quantitative studies (Shenton, 2004). Thus, it refers to the extent to which results of the study can be applied to other settings or situations (Polit & Beck, 2012). Shenton (2004) claims that transferability is a major challenge as the researcher's subjectivity is the key instrument in qualitative studies.
To ensure generalisability the researcher provided a detailed and rich description of the settings studied.
This was to ensure that readers are provided with sufficient information to enable them to judge the applicability of research results to other settings or populations they know as well as making their judgment concerning the transferability of study outcomes (Shenton, 2004). Secondly, the researcher throughout the research process was sensitive to potential biases by being mindful of the prospects for multiple interpretations of study findings.
5.6.1.4 Confirmability
Polit & Beck (2012) posit that the researcher must make sure that his/her views should not manipulate the interpretation of data collected. Thus, confirmability means that the interpretation of findings should be based on or supported by information provided by the participants and not created by the investigator.
Shenton (2004) concurs and adds that researchers should be unbiased concerning their choices of paperwork to a reviewer who would want to substantiate their work. To ensure confirmability, qualitative data analysis was based on the participants' voices as reflected by direct quotations in the qualitative findings section of data analysis in Chapter 6.
5.6.2 Data quality in Quantitative studies
Quantitative data quality is a major concern of all management and entrepreneurship researchers.
Reliability and validity are the two most important quality measures in quantitative research (Saunders
& Rojon, 2014). Sekeran and Bougie (2013) posit that these two data quality concepts attest to the scientific rigour that has gone into the research study. The following sections discuss how quality and rigour were ensured in the quantitative phase of the study.
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5.6.2.1 Reliability
Saunders and Rojon (2014) note that reliability entails the extent to which consistent results are found by using the same research instrument. Sekeran and Bougie (2013) concur and add that reliability indicates the extent to which a measure is error-free. Sekeran and Bougie (2013) add that reliability is concerned with the stability of results.
The fundamental premise of reliability is that research instruments should produce similar results when re-tested on the same people at different times (Saunders et al., 2016). Thus, to ensure reliability, the researcher drafted the instrument in such a manner that "instrumentation, data, and findings were controllable, predictable, consistent and replicable" (Cohen, Manion & Morrison, 2007). Furthermore, the researcher developed the instrument making sure that the respondents would easily understand (Sekeran& Bougie, 2013).
A pilot study was also done to assess the questionnaire's reliability. The questionnaire's internal consistency was checked using the widely used index of Cronbach's Alpha test. Acceptable reliability is shown by alpha values from 0.70 to 1 (Pallant, 2007; Sekeran & Bougie, 2016), 0.75 to 1 (Coolican, 2009). Table 6.2 in chapter 6 shows the results of the pilot study. These findings reflect a highly acceptable degree of reliability for the questionnaire items (Bron & Vidaver-Cohen, 2008). Thus, the questionnaire was suitable for gathering data.
5.6.2.2 Validity
Validity is concerned with “whether the findings are really about what they appear to be about”
(Saunders et al., 2016). Sekeran and Bougie (2013) concur and add that validity is a "test of how well an instrument measures the concept it intends to measure". Simply, validity is concerned with whether the instrument measures the right concept (Sekeran & Bougie, 2013). This section discusses how the researcher ensured internal validity, external validity, conclusion validity, and construct validity (Saunders et al., 2016).
Saunders et al. (2016) view the internal validity of the experiment as "the extent to which research findings can be attributed to the interventions rather than any flaws in the research design". In other words, it answers the question "does the instrument measure what its designer claims it does?" (Cooper
& Schindler, 2012). To ensure that the questionnaire measured the impact of strategy formulation on financial performance in SMEs, the researcher carefully planned his research methodology that is sampling, instrumentation, and suitable statistical treatments of the data (Saunders et al., 2016). The researcher followed the scientific approach to research to ensure that the study and its results were valid.
The use and design of the questionnaire were informed by theory and literature.
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The external validity of research findings is the data’s ability to be generalized across persons, settings, and times (Cooper & Schindler, 2012; Saunders et al., 2016). In a study that lacks external validity, it is likely to be difficult that the findings be applied beyond the narrow confines of the study (Saunders et al., 2016). The current study ensured external validity by way of triangulation. Furthermore, the researcher's supervisor and colleagues reviewed the entire research project before the national and international examiners.
Construct validity is the extent to which a questionnaire or test measures a theoretical concept (Saunders et al., 2009; Bryman & Bell, 2015). Sekeran and Bougie (2013) note that "construct validity testifies to how well the results obtained from the use of the measure the theories around which the test is designed". Thus, construct validity is related to the theoretical concepts underpinning the study. Hence, to enhance construct validity for the current study the study instruments items were aligned with the theoretical underpinnings in strategy.
According to Cooper and Schindler (2012), the content validity of a measuring instrument is the extent to which it provides adequate coverage of the investigative questions guiding the study. Sekeran and Bougie (2013) emphasise that with content validity, the measures should include an adequate and representative set of items that defines a particular concept. Thus, to ensure content validity, the researcher made sure that the research instruments adequately covered the strategy formulation elements and the financial performance aspects. The researcher also asked for insights and recommendations from the supervisor as well as other doctoral graduates from the UKZN School of Management, IT, and Governance to judge how well the instrument meets the standard.