The document discusses the process of data analysis and interpretation in educational research, emphasizing the transition from data to knowledge. It outlines the steps involved in data analysis, differentiates between descriptive and inferential analysis, and highlights the importance of interpretation in understanding research findings. The document also details various statistical methods and precautions necessary for accurate interpretation.

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,
 from Information to Facts, and finally,
 from 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.

5. 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.

6. 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.

7. PLANNING FOR DATA ANALYSIS

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

9. 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

10. Kinds of Data Analysis
1.Descriptive Analysis
2.Inferential Analysis

11. 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.

12. Classification of Descriptive Analysis
a. 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

13. 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

14. 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.

15. Two Commonly Used Measures of
Variability
1. 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.

16. 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

17. Kinds of Data Analysis
1.Descriptive Analysis
2.Inferential Analysis

18. 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

19. 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)

20.  Uses of Inferential Analysis
* Cited some statistical test for inferential analysis.
1. 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.

22. 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.

23. “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.”

24. 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.

25. 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:

26. 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.

27. Interpretation leads to establishment
 Interpretation 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.

28. 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

29. 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.

30. 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.

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

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