OVERVIEW-OF-DATA-ANALYSIS-IN-RESEARCH.-for-the-class-pptx.pptx

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1. OVERVIEW OF DATA ANALYSIS IN RESEARCH Kalinga State University September 1, 2024 Seminar in Research with Data Analytics Reporter : Doddie Marie L. Duclan

2. Session Objective At the end of the class session, students should be able to Identify the following: A. Key concepts in data analysis B. Steps in data analysis , and C. Considerations in data analysis

3. I. Introduction to Data Analysis Definition:   Data Analysis is the process of systematically applying statistical and/or logical techniques to describe, illustrate, condense, recap, and evaluate data. Importance:  Ensures data integrity and accurate research findings

4. Types of Data Analysis A. Quantitative Data Analysis A.1. Statistical Techniques: -Data Sampling -Central Tendency -Random Variables -Probability Distributions -Statistical Inference A.2 Examples and Applications

5. Types of Data Analysis B. Qualitative Data Analysis B.1. Iterative Process: 1.Creating a prototype or product/service 2.Getting feedback from customers and stakeholders 3. Using the feedback to improve in the next work cycle 4. Repeating this process until the desired outcome is achieved B.2 Examples and Applications

6. Key Concepts in Data Analysis A. Inductive Inference : a reasoning method where general conclusions are drawn from specific observations to help in making sense of data and drawing meaningful conclusions Importance: 1.Pattern Recognition: Helps identify patterns and trends. 2. Prediction: Allows for making predictions about future events. 3. Decision-Making: Aids in forming general rules or principles for decision-making

7. Key Concepts in Data Analysis B. Shamoo and Resnik (2003) Perspective  Signal vs. Noise According to Shamoo and Resnik (2003) various analytic procedures “provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data” 2,3,4,5,2,1,4,3,2,3,1,1,2,5,3,1,2,2,4,5,2,1

8. Data Analysis in Qualitative Research Approaches to Qualitative Analysis: -Field Study -Ethnography -Content Analysis -Oral History -Biography -Unobtrusive Research Forms of Data: -Field Notes -Documents -Audiotape -Videotape

9. Ensuring Data Integrity Importance:  Accurate and appropriate analysis is crucial Consequence of improper analyses:   Improper analyses can distort findings and mislead readers (Shepard, 2002)

10. Data Collection  Definition :   Datasets are collections of information Purpose: used to answer questions, make decisions, or inform reasoning

11. Data Collection Impact of Information Technology Generation of Vast Amounts of Data Types of Data:  Text, pictures, videos, personal information, account data, metadata

12. Data Collection Data Collection in Digital Platforms Apps and Websites Usage Data and User Information

13. Conclusion Data analysis is a critical process in research that involves the systematic application of statistical and logical techniques to describe, summarize, and evaluate data. It enables researchers to draw meaningful inferences and distinguish significant patterns from random noise. Both qualitative and quantitative research benefit from rigorous data analysis, which often involves continuous and iterative examination of data throughout the collection phase. The integrity of research findings heavily relies on accurate and appropriate data analysis, as improper techniques can distort results and mislead readers. With the advent of information technology, the volume and variety of data have increased significantly, necessitating robust methods to ensure the reliability and validity of research outcomes

14. II.Steps in Data Analysis 1.Determine the data requirements or how the data is grouped. 2.Collect the data. 3.Organize the data after it's collected so it can be analyzed. 4.Clean up the data before it is analyzed.

15. III. Considerations/issues in data analysis 1.Having the necessary skills to analyze 

16. III. Considerations/issues in data analysis 2. Concurrently selecting data collection methods and appropriate analysis 

17. III. Considerations/issues in data analysis 3. Drawing unbiased inference 

18. III. Considerations/issues in data analysis 4. Inappropriate subgroup analysis 

19. III. Considerations/issues in data analysis 5. Following acceptable norms for disciplines Every field of study has developed its accepted practices for data analysis. Resnik (2000) states that it is prudent for investigators to follow these accepted norms. Resnik further states that the norms are ‘…based on two factors: (1) the nature of the variables used (i.e., quantitative, comparative, or qualitative), (2) assumptions about the population from which the data are drawn (i.e., random distribution, independence, sample size, etc.).

20. III. Considerations/issues in data analysis 6. Determining statistical significance Researchers suggest that it may also be appropriate to discuss whether attaining statistical significance has a true practical meaning

21. III. Considerations/issues in data analysis 7. Lack of clearly defined and objective outcome measurements 

22. III. Considerations/issues in data analysis 8. Provide honest and accurate analysis 

23. III. Considerations/issues in data analysis 9. Environmental/contextual issues 

24. III. Considerations/issues in data analysis 10. Data recording method Analyses could also be influenced by the method in which data was recorded. For example, research events could be documented by: a. recording audio and/or video and transcribing later b. either a researcher or self-administered survey c. either closed ended survey or open ended survey d. preparing ethnographic field notes from a participant/observer e. requesting that participants themselves take notes, compile and submit them to researchers.

25. III. Considerations/issues in data analysis  11. Reliability and Validity Factors that can affect R&V: A. stability , or the tendency for coders to consistently re-code the same data in the same way over a period of time B. reproducibility , or the tendency for a group of coders to classify categories membership in the same way C, accuracy , or the extent to which the classification of a text corresponds to a standard or norm statistically

26. III. Considerations/issues in data analysis  12. Extent of analysis Whether statistical or non-statistical methods of analyses are used, researchers should be aware of the potential for compromising data integrity.

27. Post Test 1. Why is data analysis important? 2. How should data be analyzed? 3. As researchers, what factors should be considered to avoid erroneous data analysis?

28. References: Gottschalk, L. A. (1995). Content analysis of verbal behavior: New findings and clinical applications. Hillside, NJ: Lawrence Erlbaum Associates, Inc Jeans, M. E. (1992). Clinical significance of research: A growing concern. Canadian Journal of Nursing Research, 24, 1-4. Lefort, S. (1993). The statistical versus clinical significance debate. Image, 25, 57-62. Kendall, P. C., & Grove, W. (1988). Normative comparisons in therapy outcome. Behavioral Assessment, 10, 147-158. Nowak, R. (1994). Problems in clinical trials go far beyond misconduct. Science. 264(5165): 1538-41. Resnik, D. (2000). Statistics, ethics, and research: an agenda for educations and reform. Accountability in Research. 8: 163-88 Schroder, K.E., Carey, M.P., Venable, P.A. (2003). Methodological challenges in research on sexual risk behavior: I. Item content, scaling, and data analytic options. Ann Behav Med, 26(2): 76-103. Shamoo, A.E., Resnik, B.R. (2003). Responsible Conduct of Research. Oxford University Press.

29. References: Shamoo, A.E. (1989). Principles of Research Data Audit. Gordon and Breach, New York. Shepard, R.J. (2002). Ethics in exercise science research. Sports Med, 32 (3): 169-183. Silverman, S., Manson, M. (2003). Research on teaching in physical education doctoral dissertations: a detailed investigation of focus, method, and analysis. Journal of Teaching in Physical Education, 22(3): 280-297. Smeeton, N., Goda, D. (2003). Conducting and presenting social work researchsome basic statistical considerations. Br J Soc Work, 33: 567-573. Thompson, B., Noferi, G. 2002. Statistical, practical, clinical: How many types of significance should be considered in counseling research? Journal of Counseling & Development, 80(4):64-71.