PEER #1
1. Discuss one thing you learned about Ch 15
Content validity conceptualizes the construct (Lippincott Williams, & Wilkins, 2012). Content validity refers to how well a measure represents the entire range of a certain construct, ensuring that all relevant aspects are adequately covered. It’s about ensuring that the items or questions used to measure something are appropriate and comprehensive for capturing the concept being studied. The term “construct validation” refers to the process of simultaneously validating measures of psychological constructs and the theories of which the constructs are a part (Strauss & Smith, 2009).
2. Discuss one thing you learned about Ch 16.
Frequency distribution imposes order on raw data. Its numeric values are ordered from lowest to highest, accompanied by a count of the number of times each value was obtained (Lippincott Williams, & Wilkins, 2012). A frequency distribution is a way to organize and display data by showing the number of times each value occurs within a dataset. It provides a summary of the distribution of values or scores, allowing researchers to understand the pattern or spread of the data. This can be done through tables, histograms, or other graphical representations. All interprofessional healthcare team members need to be familiar with, if not well-versed in, these statistical analyses so they can read and interpret study data and apply the data implications in their everyday practice (Shreffler & Huecker, 2023).
3. Discuss one thing you learned about Ch 17
Parameter statistics rely on assumptions about the data distribution and are often more powerful but less robust. In contrast, non-parameter statistics are distribution-free and more flexible but may be less powerful in certain situations. The choice between these two types of statistics depends on the specific characteristics of the data and the research question being addressed. Applying parametric analysis of real survey data leads practically in all cases to the same conclusions as those drawn from applying non-parametric analyses (Mircioiu & Atkinson, 2017).
4. Discuss one thing you learned about Ch 18.
Simple Linear Regression makes predictions about the values of one variable based on the values of a second variable. It estimates a straight-line fit to the data that minimizes deviations from the line (Lippincott Williams, & Wilkins, 2012). Simple linear regression is commonly used for prediction and for understanding the direction and strength of the relationship between two variables. It provides insights into how changes in the independent variable are associated with changes in the dependent variable. Simple linear regression is a statistical method used to model the relationship between two continuous variables. It aims to find the best-fitting straight line (the regression line) that describes the relationship between the independent variable (predictor) and the dependent variable (outcome). (Aggarwal, & Ranganathan, 2017).
References
Aggarwal, R., & Ranganathan, P. (2017). Common pitfalls in statistical analysis: Linear regression analysis. Perspectives in clinical research, 8(2), 100–102. https://doi.org/10.4103/2229-3485.203040.
Lippincott Williams, & Wilkins. (2012). Copyright © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.
Mircioiu, C., & Atkinson, J. (2017). A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale. Pharmacy (Basel, Switzerland), 5(2), 26. https://doi.org/10.3390/pharmacy5020026
Polit, D. & Beck, C. (2014). Nursing research: Appraising evidence for nursing practice (8th Edition). Lippincott Williams and Wilkins. Philadelphia
Shreffler J, Huecker MR. (2023) Exploratory Data Analysis: Frequencies, Descriptive Statistics, Histograms, and Boxplots. [Updated 2023 Nov]. In: Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK557570/
Strauss, M. E., & Smith, G. T. (2009). Construct validity: advances in theory and methodology. Annual review of clinical psychology, 5, 1–25. https://doi.org/10.1146/annurev.clinpsy.032408.153639
