1. ANNOUCEMENTS 9/3/2015 – NO CLASS 11/3/2015 – LECTURE BY PROF.IR.AYOB KATIMON – 2.30 – 4 PM – DKD 5 13/3/2015 – SUBMISSION OF CHAPTER 1,2 & 3

2. DATA ANALYSIS & INTERPRETATION CHAPTER 4 – RESULT & DISCUSSION

3. OBJECTIVES After studying this lesson you are expected to: – Be able to present the result of your collected data. – Make good analysis of the tabulated or graphically presented data. – Make effective interpretation of the data/finding/results, and – Draw implications or inferences and generations from the analysis and interpretation of findings.

4. INTRODUCTION This chapter (CHAPTER 4) presents the findings of the study. – Presentation should be clear and scholarly done and may come in the form of tables, figures or charts. – Analysis refers to the skill of the researcher in describing, delineating similarities and differences, highlighting the significant findings or data and ability to extract information or message out of the presented data. – Interpretation is the explanations or suggestions inferred from the data, their implications but not conclusions.

5. PRESENTATION OF FINDINGS – HOW?? VERBAL – Describes – Explain SYMBOLIC – Graphic – Tables/Graphs – Statistical values

6. The use of tables and graphs – Tables and graphs are both ways to organize and arrange data so that it is more easily understood by the viewer. – Tables and graphs are related in the sense that the information used in tables is frequently also used for the basis of graphs. Note: When designing table, keep format clear and simple. Line up decimal places, note units clearly, use a large enough typeface and construct a clean orderly arrangement of rows and columns.

9. Types of Graphs BAR GRAPHS – Excellent way to show the results that are one time, that are not continuous – especially samplings such as surveys and inventories. – Are used to get an overall idea or trends in responses which categories get, many versus few responses. LINE GRAPHS – Most useful in displaying data or information that change continuously over time.

12. CIRCLE OR PIE GRAPHS – Particularly good illustrations when considering how many parts of a whole are inception. – Each slice should be easily distinguished from the rest and clearly labeled. Note: Graph/diagram should be - simple, easy to understand, save a lot of words, self explanatory, has a clear title indicating its content, fully labeled & the y-axis is usually used for frequency.

13. DATA ANALYSIS The purpose – To answer the research questions and to help determine the trends and relationships among the variables.

14. STEPS IN DATA ANALYSIS BEFORE DATA COLLECTION – Determine the method of data analysis – Determine how to process the data – Prepare dummy tables AFTER DATA COLLECTION – Process the data – Prepare tables and graphs – Analyze and interpret findings – Consult supervisor/plv/etc – Prepare for editing & presentation

15. KINDS OF DATA ANALYSIS 1. Descriptive analysis – Refers to the description of the data from a particular sample; hence the conclusion must refer only to the sample. – In other words, these summarize the data and describe sample characteristics. 2. Inferential analysis – The use of statistical tests, either to test for significant relationships among variables or to find statistical support for the hypotheses.

16. Classification of Descriptive Analysis 1. Frequency Distribution – A systematic arrangement of numeric values from the lowest to the highest or vice versa. 2.Measure of Central Tendency – Average of the set values (mode, median, mean) 3. Measure of Variability – Statistics that concern the degree to which the scores in a distribution are different from or similar to each other. (range, standard deviation)

17. Inferential Analysis The use of statistical tests, either to test for significant relationships among variables or to find statistical support for the hypotheses. Inferential Statistics – ANOVA (significant of differences between means of two or more groups) – T-test Hypothesis – The outcome of the study perhaps may retain, revise or reject the hypothesis and this determines the acceptability of hypotheses and the theory from which it was derived.

18. INTERPRETATION OF FINDINGS/RESULTS, IMPLICATIONS AND INFERENCES Sufficient data should be used to justify your inferences or generalization. The implications suggested by the data should be explained and discussed thoroughly in this portion of your thesis. The data analysis involves comparing values on the dependent measures in statistical cases. In the non statistical approach, these comparisons usually involve visual inspection of data. Evaluation depends on projecting from baseline data what findings would be like in the future if some variables were not experimented.