This document provides information on data presentation and analysis for research studies. It discusses important steps in data analysis including encoding, organizing, and statistically analyzing data according to research questions. It also discusses techniques for data processing such as editing, coding, and tabulation. Finally, it provides examples of interpreting data in tables, bar graphs, line graphs and pie charts as well as using descriptive and inferential statistical techniques like percentages, measures of central tendency, standard deviation, and correlation analysis.

1. DATA PRESENTATION
& ENTERPRETATION
Prepared by: Jennysel O. Lozada

2. DATA PRESENTATION
▪Data presentation and analysis is one of the most
essential part in your research study. An excellent
data presentation can be potential for winning the
hearts of the panelists, clients, or simply the
readers. No matter how good your data, if it is not
well presented, you will not be able to earn the
preferences of those whom you are trying to
persuade. Good data presentation matters.

3. THE FOLLOWING ARE THE SIGNIFICANT STEPS YOU NEED TO TAKE
NOTE IN PREPARING AND WRITING YOUR DATA ANALYSIS AFTER
GATHERING THE DATA:
▪ (1) encode and organize your data for analysis according to the data asked by
your research questions;
▪ (2) use your data for statistical tests you have identified. You may ask assistance
from your statistics and research teacher;
▪ (3) present the result in tabular or graphical form appropriate for your data and
research purpose;
▪ (4) write the interpretation for each table or graph highlighting the significant
results and its implications;
▪ (5) support your findings from relevant literature and studies you have cited in
the Chapter 2 of your research paper; and
▪ (6) edit the grammatical and typographical errors in your interpretation. You may
use www.grammarly.com to edit your work.
▪ (7) Submit your work using the format given to you. Remember the institutional
format of your school.

4. TECHNIQUES IN DATA PROCESSING
▪The data processing involves three actions:
1. editing,
2. coding, and
3. tabulation

5. TECHNIQUES IN DATA PROCESSING
▪Editing detects errors and omissions,
corrects them whatever possible. Editor’s
responsibility is to guarantee that data are –
accurate; consistent with the intent of the
questionnaire; uniformly entered; complete;
and arranged to simplify coding and
tabulation.

6. TECHNIQUES IN DATA PROCESSING
▪Coding refers to the process of assigning numerals or other
symbols to answers so that responses can be put into a
limited number of categories or classes.
▪In quantitative research, coding is done to assign numerical
value to specific indicator especially if it is qualitative in
nature. This numerical value will be useful when you are
going to analyze your data using statistical tool.

7. TECHNIQUES IN DATA PROCESSING
▪Tabulation is a system of processing data or information by
arranging it into a table. With tabulation, numeric data is arrayed
logically and systematically into columns and rows, to aid in their
statistical analysis. The purpose of tabulation is to present a large
mass of complicated information in an orderly fashion and allow
viewers to draw reasonable conclusions and interpretations from
them. In many studies, table is used to do this process.
▪Tabulation can be done manually or electronically using MS
Excel. The following digital tools can be used to tabulate your
data like MS Excel, Minitab, or other digital tools.

8. PRESENTATION AND INTERPRETATION
OF DATA
▪Tables
➢Table helps summarize and categorize data
using columns and rows. It contains headings
that indicate the most important information
about your study.

9. PRESENTATION AND INTERPRETATION OF DATA
▪Sample Interpretation for the Given Table

10. PRESENTATION AND INTERPRETATION OF DATA
▪Sample Interpretation for the Given Table

11. PRESENTATION AND INTERPRETATION OF DATA
▪Graphs
➢Graphs are visual representations which focuses on how a
change in one variable affects to another. They are used to
organize information to show patterns and relationships. A
graph shows this information by representing it as a shape.
Researchers and scientists often use tables and graphs to
report findings from their research. In choosing what type of
graph to use, determine the specific purpose of the
presentation. Line Graph illustrates trends and changes in
data over time, Bar Graph illustrates comparisons of amounts
and quantities, while Pie Graph (Circle Graph) displays the
relationship of parts to a whole.

12. PRESENTATION AND INTERPRETATION OF DATA
▪ Sample Interpretation of a Bar Graph

13. PRESENTATION AND INTERPRETATION OF DATA
▪ Sample Interpretation of a Line Graph

14. PRESENTATION AND INTERPRETATION OF DATA
▪ Sample Interpretation of a Pie Graph

15. ACTIVITY 3: PRESENT ME!

16. ACTIVITY 4: LOOK AND EXPLAIN ME!
DIRECTIONS: INTERPRET EACH FIGURE GIVEN BELOW. FOLLOW THE GUIDELINES IN INTERPRETING THE
GRAPH.WRITE A BRIEF INTERPRETATION OF THE DATA ON THE SPACE PROVIDED FOR EACH FIGURE.

17. ACTIVITY 4: LOOK AND EXPLAIN ME!
DIRECTIONS: INTERPRET EACH FIGURE GIVEN BELOW. FOLLOW THE GUIDELINES IN INTERPRETING THE
GRAPH.WRITE A BRIEF INTERPRETATION OF THE DATA ON THE SPACE PROVIDED FOR EACH FIGURE.

18. ACTIVITY 4: LOOK AND EXPLAIN ME!
DIRECTIONS: INTERPRET EACH FIGURE GIVEN BELOW. FOLLOW THE GUIDELINES IN INTERPRETING THE
GRAPH.WRITE A BRIEF INTERPRETATION OF THE DATA ON THE SPACE PROVIDED FOR EACH FIGURE.

19. ACTIVITY 5: INTERPRET ME!

20. USING STATISTICAL
TECHNIQUES TO
ANALYZE DATA

21. DATA ANALYSIS STRATEGIES
1. Descriptive Analysis
➢ describe or summarize data in a meaningful way leading to a simple
interpretation of data
1. Percentage
2. Measures of Central Tendency (Mean, Median, Mode)
3. Measures of Dispersion (Range, Average Mean Deviation, Standard
Deviation
2. Inferential Data Analysis
➢ used to test hypotheses about a set of data to reach conclusions and
generalizations
1. Test of significance of difference (T-test, Z-Test, ANOVA)
2. Test of relationship (Pearson r or Correlation, Spearman rho, regression,
Chi-square test)

22. DESCRIPTIVE ANALYSIS
▪ 1. Percentage is any proportion from the whole.

23. DESCRIPTIVE ANALYSIS
▪ 2. Mean or average is the middlemost value of your list of values, and this can be
obtained by adding all the values and divide the obtained sum to the number of values.

24. DESCRIPTIVE ANALYSIS
▪ 3. Standard Deviation shows the spread of data around the mean.

25. DESCRIPTIVE ANALYSIS
▪ 3. Standard Deviation shows the spread of data around the mean.

26. DATA ANALYSIS STRATEGIES
Inferential Data Analysis
➢ used to determine if there is a relationship between an
intervention and an outcome as well as the strength of that
relationship.
➢ allow us to use samples to make inference and
generalizations about the population from which the
samples were drawn.

27. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r) is a
statistical method used to estimate the strength
of relationship between two quantitative
variables
▪Correlation Coefficient r
➢ A number that represents the strength and
direction of a relationship existing between
two variables

28. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)

29. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)

30. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)

31. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)
➢ Strength of Correlation
▪ If the relationship is strong one, the number
will be closer to +1.00 or to -1.00.
▪ A correlation of +0.89 is equally strong as -
0.89.
▪ A correlation of -0.90 is stronger than 0.45.

32. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)
➢ Strength of Correlation

33. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)

34. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)

35. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)

36. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)

37. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)

38. INFERENTIAL DATA ANALYSIS
▪Correlation Analysis (Pearson’s r)

39. ACTIVITY 6: WHAT’S MY PERCENTAGE?
▪ Directions: Here’s a data gathered by Purok A City High School administration regarding
the number of Grade 7 parents who opted to receive printed copies of the learning
modules. Fill out the boxes for total and percentage.Then write a brief interpretation of
the table.

40. ACTIVITY 7: WHAT’S MY MEAN AND STANDARD DEVIATION?
▪ Directions: Here’s the data gathered from the survey on Study Habits conducted by the
Grade 12 students to the 150 Grade 7 students of Purok A City High School.

41. ACTIVITY 8: WHAT’S MY RELATIONSHIP?
▪ Directions: Here’s the data about the Math Pretest and Posttest scores of ten (10) Grade 12
students of Purok A City High School. Is there a significant relationship between the
pretest and posttest scores in Math?