Exercise 12: Algorithm For Determining Appropriateness

Exercise 12 Algorithm For Determining The Appropriateness Of Inferent

Answer the following questions by reviewing the statistical content and applying the algorithm in Figure 12-1 of this exercise (Grove & Cipher, 2020).

Paper For Above instruction

Introduction

The proper selection of statistical techniques in research is crucial for accurately analyzing data and drawing valid conclusions. Researchers must understand differences between parametric and nonparametric methods, the level of measurement of variables, and the appropriate techniques based on study design and data characteristics. This paper explores various statistical techniques through the lens of specific research scenarios and applies the algorithm outlined in Grove & Cipher (2020) to determine the most suitable inferential statistical method.

Differences between Parametric and Nonparametric Techniques

Parametric statistical techniques rest on assumptions about the distribution of the data, most notably that the data follow a normal distribution. They generally require interval or ratio level data, which involves continuous variables, enabling the use of techniques such as t-tests and ANOVA (Fox, 2016). These methods are more powerful than nonparametric techniques when their assumptions are met and are often used for smaller sample sizes with normally distributed data.

Nonparametric methods, in contrast, do not assume any specific data distribution and are suitable for ordinal or nominal data or when the assumptions necessary for parametric tests are violated (Gibbons & Chakraborti, 2011). These techniques include tests like the Mann-Whitney U test and the Kruskal-Wallis H test. They are more flexible but generally less powerful, suitable for data that are skewed or for small sample sizes.

An example of a parametric test is the independent samples t-test, which compares the means of two groups. A nonparametric counterpart is the Mann-Whitney U test, which compares the medians between two independent groups without assuming normal distribution.

Analysis of Specific Study Scenarios Using the Algorithm

1. Association between variables measured at the ordinal level with one group: The appropriate technique is Spearman’s rank correlation coefficient, which assesses the monotonic relationship between two ordinal variables within a single group (Cohen et al., 2013).
2. Differences among three independent groups with ratio level variables: A one-way ANOVA is suitable if assumptions are met; otherwise, the Kruskal-Wallis test if data violate normality or homogeneity of variances (Field, 2013).
3. Predicting a dependent variable using one independent interval/ratio variable in a sample of patients: Simple linear regression would be appropriate to evaluate the predictive relationship (Tabachnick & Fidell, 2013).
4. Differences among three paired samples measured at the ordinal level: The Friedman test, a nonparametric alternative to repeated measures ANOVA, would be suitable for related samples with ordinal data (Zimmerman, 2012).

Testing the Hypothesis on Nurse Job Satisfaction with Magnet Status

The hypothesis states that nurses working in magnet hospitals have higher job satisfaction than those in non-magnet hospitals, with job satisfaction measured via a Likert scale. Because this involves comparing the mean scores of two independent groups, an independent samples t-test is appropriate if assumptions hold. If the Likert scale data are not normally distributed or if variances are unequal, the Mann-Whitney U test serves as a nonparametric alternative (Pallant, 2016).

Level of Measurement of Variables in Lee et al. (2018)

- Dyspnea (FEV1): Ratio level, as it is a continuous measurement of lung function.

- Anxiety, Depression, Fatigue: Ordinal or interval level, based on multi-item Likert scales, often treated as interval data in analysis.

- 6-Minute Walk Test distance: Ratio level, as it is a continuous measure of physical performance (Lee et al., 2018).

Inferential Statistical Technique Used in Lee et al. (2018)

Lee et al. likely conducted multiple regression analysis to determine how symptoms (dyspnea, anxiety, depression, fatigue) predict physical performance. Regression analysis is appropriate for evaluating the predictive capacity of several independent variables on a continuous dependent variable (Field, 2013).

Analysis of Variables in Smith et al. (2014)

Variables—perceived stress, sleep quality, loneliness, and self-esteem—measured with Likert scales considered at the interval level. To examine relationships among these variables, the researchers probably used Pearson correlation coefficients, which assess the linear relationship between continuous variables at the interval level (Cohen et al., 2013).

Statistical Technique in Hersh et al. (2018)

Hersh et al. conducted an RCT comparing stress levels before and after the intervention between two independent groups. An independent samples t-test would be appropriate to compare mean stress scores if assumptions are met; otherwise, a Mann-Whitney U test would serve as an alternative (Pallant, 2016).

Conclusion

Selecting appropriate statistical methods requires careful consideration of data type, distribution, and study design. Understanding the distinctions among parametric and nonparametric techniques and their application ensures valid, reliable analysis and meaningful research conclusions.

References

• Field, A. (2013). Discovering statistics using IBM SPSS Statistics. Sage publications.
• Fox, J. (2016). Applied regression analysis and generalized linear models. Sage Publications.
• Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric statistical inference. CRC press.
• Grove, S. K., & Cipher, D. (2020). Understanding statistical procedures: Using the algorithm in Figure 12-1. Elsevier.
• Pallant, J. (2016). SPSS survival manual. McGraw-Hill Education.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Pearson.
• Zimmerman, D. W. (2012). A note on nonparametric statistical tests in educational research. Journal of Experimental Education, 80(3), 386-402.
• Lee, H., Nguyen, H. Q., Jarrett, M. E., Mitchell, P. H., Pike, K. C., & Fan, V. S. (2018). Effect of symptoms on physical performance in COPD. Heart & Lung, 47(2), 149–156.
• Smith, M. J., Theeke, L., Culp, S., Clark, K., & Pinto, S. (2014). Psychosocial variables and self-rated health in young adult obese women. Applied Nursing Research, 27(1), 67–71.
• Hersh, R. K., Cook, R. F., Deitz, D. K., Kaplan, S., Hughes, D., Friesen, M. A., & Vezina, M. (2016). Reducing nurses’ stress: A randomized controlled trial. Applied Nursing Research, 32(1), 18–25.