The document outlines various types of data analysis, including definitions, objectives, and statistical techniques used for each type. Types discussed include univariate, bivariate, multivariate, normative, status, descriptive, classification, evaluative, comparative, and cost-effective analysis. Each type of analysis is defined alongside the statistical tools commonly applied to achieve specific research objectives.

1. Data analysis

2. DATA ANALYSIS
 may be defined as an
examination of data or fact in
terms of quantity, quality,
attribute, trait, pattern, trend,
relationship among others so as
to answer research questions
which involve statistical
techniques and procedures

3. BASES IN ANALYZING RESEARCH DATA
• Problems/ Objectives
• Hypothesis
• measuring instruments
• Statistical tools

4. CODING & COLLATING
• CODING- is your act of using symbols
like letters or words to represent
arbitrary or subjective data
( emotions, opinions, attitudes) to
ensure secrecy or privacy of the data.
• COLLATING- way of bringing
together the coded data.

5. TYPES OF DATA ANALYSIS
1. UNIVARIATE ANALYSIS
2. BIVARIATE ANAYSIS
3. MULTIVARIATE ANALYSIS
4. NORMATIVE ANALYSIS
5. STATUS ANALYSIS
6. DESCRIPTIVE ANALYSIS
7. CLASSIFICATION ANALYSIS
8. COMPARATIVE ANALYSIS, and
9. COST-EFFECTIVE ANALYSIS

6. • 1. UNIVARIATE ANALYSIS
-tests a single variable to determine whether
the sample is similar to the population from
which it has been drawn
example:
Problem: How effective is the teaching of Mr. Z in teaching
Mathematics in certain elem. School as perceived by Grade 6 pupils?
Null Hypothesis: The teaching of Math by Mr. Z in a certain elem.
School as perceived by Grade 6 pupils is not effective.
Variable: Different Sections of Grade 6 pupils
Statistical Tool: Weighted arithmetic mean is used because the
options are very much effective or much 4;
effective, 3;effective,2; ineffective,1.

7. • 2. BIVARIATE ANALYSIS
- Tests two variables on how they differ with each
other. The common statistical tools to be used are
correlation coefficient, z- test, and t- test.
• 3. MULTIVARIATE ANALYSIS
- Tests three or more independent variables at a
time on the degree of relationship with the dependent
variable. The statistical used in this type are F-test or
analysis of variance (ANOVA), Friedman two-way
ANOVA, and Krustal- Wallis one way ANOVA for
experimental design. Chi-square is used for descriptive
research. Friedman and Krustal-Wallis ANOVA are both
applicable in experimental and descriptive.

8. • 4. NORMATIVE ANALYSIS
- Is a type of data analysis wherein
the results of the study is compared
with the norm or standard. The
statistical tools used in this type are the
arithmetic mean and the standard
deviation.
• 5. STATUS ANALYSIS
- Stresses real facts relating to
current conditions in a group of subjects
chosen for study. The common
statistical tools used in this type are the
arithmetic mean, standard deviation, z-
test, and chi-square.

9. • 6. DESCRIPTIVE ANALYSIS
- Merely describes the
characteristics, composition, structures,
and substructures that occur as units
within the larger structure. The
researcher should consider the forces
that hold together and the strains that
tend to destroy the system apart. He
also analyzes on what makes the system
work and regulate.
- The statistical tools commonly
used in descriptive analysis type are the
arithmetic mean, chi-square, and
Friedman two-way ANOVA.

10. • 7. CLASSIFICATION ANALYSIS
- Usually employed in natural science subjects
such as Botany, Zoology, Phycology, Ichthyology,
Conchology, Mycology, and the like. The specimen
collected are classified from phylum to species.
Taxonomic studies of plants and animals are commonly
used in this study.
• 8. EVALUATIVE ANALYSIS
- Is a type of data analysis that appraises carefully
the worthiness of the current study. The statistical tools
commonly used in this type are weighted arithmetic
mean, percentages, Friedman two-way analysis of
variance, and z-test.

11. • 9. COMPARATIVE ANALYSIS
- The researcher considers at least two entities (not
manipulated) and establishes a formal procedure for
obtaining criterion data on the basis of which he can
compare and conclude one is better than the other.
- The common statistical tools used in this type are
the mean, variances, and t-test.
• 10. COST-EFFECTIVE ANALYSIS
- Is applicable in comparing the cost between two or
more variables, and to determine which of the variables is
most effective.
- The statistical tools commonly used in this type are
the arithmetic mean, variance, t-test, and F-test.