Data Sufficiency in Organizational Diagnostics

The Parameters for Evaluating the Sufficiency of Different Types of Data

There are two major types of errors that can occur due to the insufficient evaluation of data – false negative and false positive; the former error happens when the null hypothesis that was actually disproved by the data is mistakenly accepted for the future research; at the same time, the latter type of error occurs when the insufficient evaluation of data results in the wrongful rejection of a hypothesis that was actually true. Moreover, the primary parameters that need to be taken into consideration when the data sufficiency is determined include data equality (based on the previously established validity, relevance, and reliability), its significance for the field of knowledge, the level of assurance required, the level of risk of making a mistake while interpreting the data, how costly it would be to obtain additional data to support the existing findings (American Psychological Association, 2010).

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Becoming Certain that the Situation Is Evaluated Appropriately

In every research, there is a possibility that at the stage of the interpretation of data, the researchers could allow bias influence their decision-making or perspective on the information collected. In particular, this type of problem may appear when the researchers’ personal experience and professional background produce the impact on their vision of data and its interpretation. In order to become certain that the data is collected and evaluated appropriately, the researchers could make sure that the sample is selected correctly, the strategy of data evaluation is selected before the data was gathered, and all of the possible interpretations were reviewed apart from the one that seems to match the initial hypothesis or theory.

Specific Approaches to Increase Accuracy of Diagnosis or Assessment

The processes of diagnosing and assessment are rather complex and involve a variety of stakeholders and levels. The factors that contribute to the accuracy of these processes include tasks, technologies, tools used, teams of professionals involved, organizations, individuals, their environments (Di Pofi, 2002). In that way, avoiding biases that originate from these factors is the key to performing an accurate assessment. In particular, the specific strategies of eliminating biases include reviewing the sample and making sure that the sampling method allowed including all the potentially useful participants from diverse environments, the data collection technique gathers exhaustive information, engaging many different researchers in the interpretation of results for better objectivity.

The Use of Multiple Types of Data in an Assessment of an Organization, Team or Individual

Assessments may rely on both qualitative and quantitative types of data. Combining the two types it is important that the practitioners ensure that the data collection techniques for them work together and collect results that do not overlap but complement one another. Carrying out an assessment that relies on a survey using a combination of different types of data, I would prefer to employ open-ended questions for qualitative data collection, and closed-ended ones for more precise quantitative data (Di Pofi, 2002). The former would be interpreted by several different people by means of coding and reviews, and the latter would be organized and measured using statistical tools.

Using Yourself as an Instrument of Assessment

As an observer, the practitioner is a life-long learner and a critical thinker. Observing behaviors, tendencies, practices, and performances, the practitioner is to be able to collect and process information thoughtfully and evaluate his or her own thinking process to avoid biases. As a diagnostician, the practitioner is to focus on the objectives and remove their personal biases from the assessment keeping the personal knowledge and experience impartial (McCormick & White, 2000). The major ethical challenges come from the assessor’s access to data protected by privacy and the potential reluctance of the assessed organizations or individuals as to sharing this data. Moreover, the challenge may occur when one is assessing an organization being its insider and having to deal with the post-research environments.

References

American Psychological Association. (2010). Ethical principles of psychologists and code of conduct. Web.

Di Pofi, J. A. (2002). Organizational diagnostics: Integrating qualitative and quantitative methodology. Journal of Organizational Change Management, 15(2), 156-168.

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McCormick, D. W., & White, J. (2000). Using one’s self as an instrument for organizational diagnosis. Organization Development Journal, 18(3), 49-62.

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StudyCorgi. "Data Sufficiency in Organizational Diagnostics." November 30, 2020. https://studycorgi.com/data-sufficiency-in-organizational-diagnostics/.

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StudyCorgi. 2020. "Data Sufficiency in Organizational Diagnostics." November 30, 2020. https://studycorgi.com/data-sufficiency-in-organizational-diagnostics/.

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StudyCorgi. (2020) 'Data Sufficiency in Organizational Diagnostics'. 30 November.

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