DATA-ANALYSIS-AND-INTERPRETATION-IN-EDUCATIONAL-RESEARCH-ppt.pptx

DATA-ANALYSIS-AND-INTERPRETATION-IN-EDUCATIONAL-RESEARCH-ppt.pptx

1. DATA ANALYSIS AND INTERPRETATION IN EDUCATIONAL RESEARCH University of Saint Anthony (Dr. Santiago G. Ortega Memorial) City of Iriga 4431, Philippines SCHOOL OF GRADUATE STUDIES AND RESEARCH MICHAEL R. RIVERA Master of Art in Education. Major in Administration and Supervision

2. DATA Data is known to be crude information and not knowledge by itself. The sequence from data to knowledge is: from Data to Information , f rom Information to Facts, and finally, f rom Facts to Knowledge.

3. DATA Data becomes information, when it becomes relevant to your decision problem Information becomes facts, when the data can support it. Facts are what the data reveals. However the decisive instrumental (i.e., applied) knowledge is expressed together with some statistical degree of confidence.

4. ANALYSIS and INTERPRETATION provide answers to the research questions postulated in the study. ANALYSIS means the ordering, manipulating, and summarizing of data to obtain answer to research questions. Its purpose is to reduce data to intelligible and interpretable from so that the relations of research problems can be studied and tested. INTERPREATION gives the results of analysis, makes inferences pertinent to the research relations studied, and draws conclusions about these relations.

5. What is the difference between data analysis and interpretation? DATA collection is the systematic recording of information; data analysis involves working to uncover pattern and trends in datasets; data interpretation involves explaining those patterns and trends.

6. PLANNING FOR DATA ANALYSIS

7. Data Analysis The purpose To answer the research questions and to help determine the trends and relationships among the variables.

8. Steps in Data Analysis Before Data Collection, the researcher should accomplish the following: * Determine the method of data analysis * Determine how to process the data * Consult a statistician * Prepare dummy tables After Data Collection: * Process the data * Prepare tables and graphs * Analyze and interpret findings * Consult again the statistician * Prepare for editing * Prepare for presentation

9. Kinds of Data Analysis Descriptive Analysis Inferential Analysis

10. 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 descriptive sample characteristics. Descriptive Statistics * are numerical values obtained from the sample that gives meaning to the data collected.

11. Classification of Descriptive Analysis Frequency Distribution * A systematic arrangement of numeric values from the lowest to the highest or highest to lowest. * Formula: Ef = N * Where: E = sum of f = frequency N = sample size

12. Classification of Descriptive Analysis b. Measure of Central Tendency * A statistical index that describes the average of the set values. Kinds of Averages 1. Mode – an numeric value in a distribution that occurs most frequently. 2. Median – an index of average position in a distribution of numbers. 3. Mean – the point on the score scale that is equal to the sum of the scores divided by the total number of score. Formula: X =_ Σ Where: n X = the mean Σ = the sum of X = each individual raw score n = the number of cases

13. Classification of Descriptive Analysis c. Measure of Variability * Statistics that concern the degree to which the scores in a distribution are different from or similar to each other.

14. Two Commonly Used Measures of Variability Range - the distance between the highest score and the lowest score in a distribution. Example: The range for learning center A 500 (750 – 250) and the range for learning center is about 300 (650 – 350) 2. Standard Deviation - the most commonly used measure of variability that indicates the average to which the scores deviate from the mean.

15. Classification of Descriptive Analysis d. Bivariate Descriptive Statistics * Derived from the simultaneous analysis of two variables to examine the relationships between the variables. Two Commonly Used Bivariate Descriptive Analysis 5. Contingency tables - is essentially a two- dimensional frequency distribution in which the frequencies of two variables are cross-tabulated. Correlation - the most common method of describing the relationship between two measures

16. Kinds of Data Analysis Descriptive Analysis Inferential Analysis

17. 2. Inferential Analysis The use of statistical test, either to test for significant relationships among variables or to find statistical support for the hypotheses. Inferential Statistics * are numerical values that enables the researcher to draw conclusion about a population based on the characteristics of a population sample. * This is based on the laws of probability

18. Level of Significance An important factor in determining the representativeness of the sample population and the degree to which the chance affects the findings The level of significance is a numerical value selected by the researcher before data collection to indicate the probability of erroneous findings being accepted as true. This value is represented typically as 0.01 or 0.05. (Massey, 1991)

19. Uses of Inferential Analysis * Cited some statistical test for inferential analysis. t-test is used to examine the difference between the means of two independent groups. 2. Analysis of Variance (ANOVA)- is used to test the significance of differences between means of two or more groups 3. Chi-square- this is used to test hypotheses about the proportion of elements that fall into various cells of a contingency table.

20. Meaning of Interpretation Interpretation refers to the task of drawing inferences from the collected facts after an analytical and or experimental study. In fact, it is a search for broader meaning of research findings. The task of interpretation has two major aspect viz., the effort to establish continuity in research through linking the results of a given study with those of another, and the establishment of some explanation concepts.

21. “In one sense, interpretation is concerned with relationships within the collected data, partially overlapping analysis. Interpretation also extends beyond the data of the study to inch the results of other research, theory and hypotheses.”

22. Interpenetration is the device Thus, interpenetration is the device through which the factors that seem to explain what has been observed by researcher in the course of the study can be better understood and it also provides a theoretical conception which can serve as a guide for further researches.

23. Why Interpretation? Interpretation is essential for the simple reason that the usefulness and utility of research findings lie in proper interpretation It is being considered a basic components of research process because of the following reasons:

24. Through interpretation It is through interpretation that the researcher can well understand the abstract principle that works beneath his findings. Through this he can link up his findings with those of other studies, having the same abstract principle, and thereby can predict about the concrete world events. Fresh inquiries can test these predictions later on. This way the continuity in research can be maintained.

25. Interpretation leads to establishment Interp retation leads to the establishment of explanatory concepts that can serve as a guide for future research studies; It opens new avenues of intellectual adventure and stimulates the quest for more knowledge. Researcher can better appreciate only through interpretation why his findings are what they are and can make others to understand the real significance of his research findings.

26. Methods of data interpretation Direct visual observations of raw data After organizing the data in tables After making Graphical representations After calculations using numerical / statistical methods After mathematical modelling

27. Precautions in Interpretation One should always remember that even if the data are properly collected and analyzed, wrong interpretation would lead to inaccurate conclusions. It is, therefore, absolutely essential that the task of, interpretation be accomplished with patience in an impartial manner and also in correct perspective.

28. For correct interpretation Researcher must pay attention to the following points for correct interpretation: ( i ) At the outset, researcher must invariably satisfy himself that (a) the data are appropriate, trustworthy and adequate for drawing inferences; (b) the data reflect good homogeneity; and that (c) proper analysis has been done through statistical methods, (ii) The researcher must remain cautions about the errors that can possibly arise in the process of interpreting results.

29. ‘All meaning, we know, depend on the key of interpretation.’ -George Eliot

30. Thank You Thank you one and all for your patient listening MICHAEL R. RIVERA MAED. ADMIN AND SUPERVISION