CHAPTER 4.pptx

Results and Discussion

1. CHAPTER 4 of a research study

2. CHAPTER 4: Presentation, Analysis and Interpretation of Data

3. Presentation of Data - Ms. Rigen Maalam-

4. A. Presentation of data Data presentation involves the use of a variety of different graphical techniques to visually show the reader the relationship between different data sets, to emphasize the nature of a particular aspect of the data.

5. A. Presentation of data Data collected from a particular research study can be presented through: Tables Charts Graphs Scatter Plot

6. A. Presentation of data

7. Presentation of data Why are data presented in tables, charts, graph or scatter plot? to organize data to show comparison of data

8. EXAMPLE: A teacher administered a 50-item diagnostic test in Math 10 to 57 students of section A. The raw scores are presented in the bar graph below.

9. Analysis of Data -Mrs. Jean Balogbog - -Mrs. Angel Rose Saluta -

10. analysis of data Analyzing data is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. It usually requires the use of data analysis software like SPSS. Data is collected and analyzed to answer questions, test hypotheses, or disprove theories.

11. analysis of data A simple example of data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision . This is nothing but analyzing our past or future and making decisions based on it.

12. analysis of data Two Methods for data analysis: Qualitative data analysis techniques Quantitative data analysis techniques These data analysis techniques can be used independently or in combination with the other to help business leaders and decision-makers acquire insights from different data types.

13. analysis of data Qualitative data describes information that is typically non-numerical. The qualitative data analysis approach involves working with unique identifiers, such as labels and properties, and categorical variables, such as statistics, percentages, and measurements.

14. analysis of data Qualitative data analysis techniques includes: content analysis (which measures content changes over time and across media); and discourse analysis (which explores conversations in their social context).

15. EXAMPLE: Observing that a reaction is creating gas that is bubbling out of solution or observing that a reaction results in a color change. Qualitative analysis is not as reliable as quantitative analysis but is often far easier, faster and cheaper to perform.

16. analysis of data Quantitative data describes information that is typically numerical. Quantitative data analysis involves working with numerical variables — including statistics, percentages, calculations, measurements, and other data — as the nature of quantitative data is numerical.

17. analysis of data Quantitative data analysis techniques includes: working with algorithms, mathematical analysis tools, and software to manipulate data and uncover insights that reveal the business and other values

18. EXAMPLE: A teacher administered a 50-item diagnostic test in Math 10 to 57 students of section A. We can use MPS to analyze the data. Mean Percentage Score (MPS) indicates the ratio between the number of correctly answered items and the total number of test questions or the percentage of correctly answered items in a test. To compute for the MPS, use this equation, MPS = (No. of learners who got the correct answer/Total no. of students) x 100%

19. What is the purpose of MPS in DepEd? MPS is not for compilation only. It must be used for  decision making in lesson delivery enhancements, learning resources utilization and school improvement plan and adjustment . The design used was descriptive research

20. Interpretation of Data -Mrs. Meriam S. Ramillete -

21. INTERPRETATION of data Data interpretation is the process of using diverse analytical methods to review data and arrive at relevant conclusions. The interpretation of data helps researchers to categorize, manipulate, and summarize the information in order to answer critical questions.

22. INTERPRETATION of data Data analysis tends to be extremely subjective. That is to say, the nature and goal of interpretation will vary from business to business, likely correlating to the type of data being analyzed.

23. EXAMPLE: A teacher administered a 50-item diagnostic test in Math 10 to 57 students of section A. The raw scores are presented in the bar graph below.

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