week 6 DQ

Question 1

The Reliability and Validity section of the Doctoral Study Proposal is part of Section 2 and is exclusively for Qualitative Studies only. This is a major difference from Quantitative research Studies as these criteria are not measurable and are used in Qualitative methods only. Section 2.15 is the Study Validity portion of Section 2 that applies to only Quantitative Studies. Depending on the design of your study, this section may or may not address Internal Validity. My Study is a Quantitative Correlational design study where internal validity is not relevant. My Study is also non-experimental, and threats to internal validity for instance, are not applicable. Other sub-sections to the Study Validity section (ie. Threats to Statistical Conclusion Validity, Reliability of the Instrument, Data Assumptions, Sample Size and External Validity) need to be accounted for in this Section only if Applicable to your Study.

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Question 2
At an operational level, a research methodology refers to specific methods used to gather adequate evidence of phenomena, develop appropriate ways to analyze data, and demonstrate the validity of findings (Knight & Cross, 2012). Validity is central in all research but even more so for positivist and deductive research (Lameck, 2013). Validity refers to the degree to which evidence and theory support the conclusions drawn from the findings of the research and is a characteristic of which the judgment of research as valid and generalizable is possible (Fan, 2013). Four levels of validity are internal validity, external validity, construct validity, and reliability. Issues such as Type I and Type II errors, violated assumptions, misspecification errors, multicollinearity, distorted graphics, confirmation bias, and causal error are threats to internal and external validity.
Internal validity. I

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Question 3

Statistical conclusion validity is the confidence a researcher can have in any conclusions about relationships among variables (Heale & Twycross, 2015. There are two types of statistical conclusion errors. Type I error occurs when a researcher concludes that there is a relationship among variables when in fact there is no such relationship; a Type II error occurs when a researcher concludes that there is not a relationship among variables when in fact there is a relationship (Yin, 2013). Researchers exercise precautionary measures to minimize statistical conclusion errors.
To guard against making a Type I error, I used a two-tailed test with alpha < .05, which is a conventional level of statistical significance. Thus, I only reported results that had less than a 5% likelihood of having occurred by chance alone. If the results I obtained in my sample were unlikely to have occurred by chance, it meant that it was reasonable to generalize from the sample to the larger population. That is, any relationship between the predictor variables and the outcome measure (bank failure) was likely to exist in the larger population as well. The likelihood of a Type II error decreases when researchers use larger samples (Bradley & Brand, 2013; Gheondea-Eladi, 2014). To guard against making a Type II error, I used a sufficiently large sample, as determined by a power analysis. 1) What are you think this answer or explore the answer or ask questions? Question 4 Threats to statistical conclusion validity Threats to statistical conclusion validity are conditions that inflate the Type I error rates, (rejecting the null hypothesis when it is in fact true), and Type II error rates (accepting the null hypothesis when it is false.) The three conditions that you need to cover here are: (a) reliability of the instrument, (b) data assumptions, and (c) sample size (Walden DBA Handbook, 2016). Hamann, Schiemann, Bellora, & Guenther, (2013) stated that statistical conclusion validity occurs when a researcher infers statistical description of the relationship among the study variables. Using a regression model will require me to verify the following assumptions, such as: homoscedasticity, normality, independence of residuals, linearity, outliers, and multicollinearity. Also, the MS-GARCH model will require me to perform a out-of-sample volatility forecasting precision measure for the model (Hemanth, & Basavaraj, 2016). Bezzina and Saunders (2014) stated that a large sample size leads to better precision and high statistical power. Therefore, the sample size will consists of a large sample of hedge funds that invest in South Africa to limit threat to validity. 4What are you think this answer or explore the answer or ask questions? The Reliability and Validity section of the Doctoral Study Proposal is part of Section 2 and is exclusively for Qualitative Studies only. This is a major difference from Quantitative research Studies as these criteria are not measurable and are used in Qualitative methods only. Section 2.15 is the Study Validity portion of Section 2 that applies to only Quantitative Studies. Depending on the design of your study, this section may or may not address Internal Validity. My Study is a Quantitative Correlational design study where internal validity is not relevant. My Study is also non-experimental, and threats to internal validity for instance, are not applicable. Other sub-sections to the Study Validity section (ie. Threats to Statistical Conclusion Validity, Reliability of the Instrument, Data Assumptions, Sample Size and External Validity) need to be accounted for in this Section only if Applicable to your Study. 1) What are you think this answer or explore the answer or ask question? P(5.u) Prime Essay Services , written from scratch, delivered on time, at affordable rates!

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