INQUIRIES INVESTIGATIONS & IMMERSIONS.pptx

Inquiries, Investigations and
Immersions

 Research is
- A study/investigation
- A scientific investigation
- Is a study on investigation which is done
systematically, empirically, scientifically,
and logically for the purpose of achieving
knowledge and helping solve situational
problems.

 Characteristics of a Research
Process
- Systematic - well defined designs, an
orderly procedure
- Empirical – measurable and observable
things or phenomenon that you can put in
print on the bases of your senses.
- Scientific – can be tested
- Logical – justifiable and acceptable by
reason

Purpose of Research
1. Discover new knowledge
2. Help solve situational problems

 System Framework of research
- INPUT PROCESS OUTPUT
Input Output
Skills and abilities
necessary in
conducting
Research/Scientific
Investigation
(Theories/Principles)
Pure/Basic research
(Idealistic)
Solutions to
problems
(Social
Responsibility

 System Framework of research
Aims at developing a person to be-
• Sensitive to
surroundings
• Systematic
• Critical
• Objective
• Logical
• Rational
• Analytical
ENVIRONMENT
Social
Political
Economic
Educational
Technological
Physical

 Critical Researcher- has the “3rd
eyes”, seeks the truth from what he
reads, does not take them hook-line
and sinker, does not jump into
conclusions. Treat opinions as
opinions

TOPIC
Feasible
Relevant
Significant
10

Brainstorming for Research Topics
1. Scheduling
2. Team teaching
3. Evaluation of
learning,
reporting to
parents
4. Student
regulation
5. Learning styles
6. Peer Tutoring
7. Field trips
8. School facilities
9. Extracurricular
programs
10. Uses of ICT in
Instruction
11. Stress
management
12.Guidance-
counseling
programs

I. Brainstorming for Research
Topics
Key Questions:
a.What do I know about the topic?
b.What should I know about the
topic?
c.What do previous studies say about
my chosen topic?

II. Identifying the Problem
and Asking the Question
Specific Consideration in Choosing
a Problem
 Workability
Is it within the limit and range of your
resource and time constraints?
Will you have access to the number of
samples required?
Is there reason to believe that you can come
up with the answers to the problem?
Is the required methodology manageable and
understandable?

a Problem
 Critical Mass
Is the problem sufficient in magnitude and
scope (are there enough variables and
potential results)?
 Interest
Are you interested in the problem?
Does it relate to your career interest?

a Problem
 Theoretical Value
Does the problem fill a gap in the literature?
Will it contribute to the advancement in your
field?
Does it improve the “state of the art”?

a Problem
 Practical Values
Will the solution to the problem improve
practice?
Are practitioners likely to be interested in the
results?
Will the findings aid the managers in making
sound decisions?
Will the system be changed by the outcome?

Situational Problem – Research Problem
SEE
EXPERIENCE
OBSERVE
HEAR
READ
FEEL
Situation in the
Environment
• Social
• Political
• Physical
• Economic
• Religious/Moral
Meaningful sensation of
the condition in the
environment that bothers
you and which you alone
cannot solve.
RESEARCH PROBLEM
-a scientific
investigation of the
different dimensions
associated with the
situational problem
involving 2 or more
factors or variables
Source of Situational Problem

 Example
Knowledge of child abuse existing
in the environment are problems
that can be derived from this
situation. Problems would be –
a. Do children abused sexually
come from
-broken homes or not
-one-parent homes or not
-poor families or not

 The research problem is just a
part of the whole pie. It
investigates two or more
variables, particularly, how these
variables are related.

Background of the Problem
 It is the presentation of the
concept of the study in a very
effective manner.
1. It must include an assumption
of significance.
2. It must be a loaded statement
that would drive an impact to
emote interest from the reader.
3. It must be simple, clear, specific
and related to the topic.

 This introductory page acquaints
the reader with the problem to be
dealt with. This orientation is
best accomplished by providing
rationale or background.

 The background intends to draw
a clearer picture of what you
want to say. It describes clearly,
colorfully and vividly the problem
situation which serves as the
rationale of the study.

 It presents in details the problem
situation based on what you
SEE AND OBSERVE HEAR READ
Happenings Lectures/Speeches Newspapers
Events Radio and TV
Broadcasting
Journal
Phenomenon Conversations Books
Personal Experience Interviews Reports &
Monographs

Records of Critical
Incidents
Records of
opinions, positions,
values
Records of findings,
figures/statistical
data
SEE AND OBSERVE HEAR READ
Happenings Lectures/Speeches Newspapers
Events Radio and TV
Broadcasting
Journal
Phenomenon Conversations Books
Personal Experience Interviews Reports &
Monographs

1. Background of the Problem
 The purpose of the background is to
highlight the need for the study by
presenting what is happening at
present and what ought to be using
the data that the researcher has
gathered.
 It identifies the area in which the
problem is to be found, and points
out that the problem had not been
fully studied.

2. Conceptual Framework
This deals with the key concepts and
related literature underlying the
framework that guides the study. The
purpose of this is:
1. To expand the context and
background of the study
2. To help further define the problem
3. To provide an empirical basis for the
subsequent
development/formulation of
hypothesis.

The initial step is to identify the
key variables of the study. This refers
to the independent, dependent and
moderator variables to be investigated.

The second step is to look for the
definitions of the variables. For the
dependent variables the following should
be done:
1. Define the variable (universal
definition)
2. Describe its characteristics and
indicators
3. Discuss its importance (how it affects
other variables) and how it is affected
by other variables (independent
variables)

For the independent variable,
define and describe its characteristics
and indicators. Discuss its effect on the
dependent variable on the basis of the
review of related literature and studies.
The same should be done for the
moderator variables.

The discussion should point out
how the previous studies relate to the
present investigation by highlighting
their similarities and differences. More
importantly, it must include some
relevant theories and concepts that
help in the development of the present
study.

Organizing the literature review
section by subheadings makes it easier
for the researcher to follow. To be
meaningful, this subheadings should
reflect the variables and their
relationship.

We should remember that the purpose of
literature review is to provide a basis for the
formulation of hypothesis.
The conceptual framework is
summarized or synthesized into a logical
network of relationship of the key concepts or
variables involved in the study. This is further
simplified by presenting a research paradigm
or hypothetical illustration of the relationship
of variables and their corresponding
indicators.

3. Research Hypothesis
(for quantitative research)
Hypothesis – is a conjectural statement
of the relation between two or more variables.
It is a tentative or temporary answer to a
research problem.

It has the following characteristics:
1. It should conjecture upon a relationship
between two or more variables.
2. It should be stated clearly and
unambiguously in a declarative statement.
3. It should be testable; that is it should be
possible to restate it in an operational form
which can be evaluated based on data.

Example:
I.Q. and achievement test are positively
related.

There are two approaches for developing
hypothesis:
Deduction – starts from generalization or
theory by logical deduction.
Induction – starts from observation, opinions
to generalizations.

General Classification of Hypothesis
RESEARCH/
ALTERNATIVE (H1)
This temporarily
asserts the
relationship of
variables
NULL/TEST (Ho) Denies the relationship
of variables

4. Statement of the Problem
The advantages of stating the
statement of the problem are:
1. It provides the reader with an
immediate basis from which to
interpret subsequent statements
2. It makes it possible to quickly
determine the purpose of the study.
The reader will not have to search
for the introduction and background
to discover the problem being
examined.

A problem statement must have the
following characteristics:
1. It should ask about a relationship
between two or more variables.
2. It should be stated clearly,
unambiguously and usually in
question form.
3. It should be possible to collect data
to answer the question asked.
4. It should not represent a moral or
ethical position.

One or two sentences will normally
suffice to state the problem. Often the
statement begins as follows:
The purpose of this study is to examine
the relationship between…….(state the
variables, locale and time as the case
maybe).

Specifically, it seeks answers to the
following questions:
1. What is the relation between I.Q.
and achievement?
2. Is there a relationship between
economic background and dropout
rate?

5. Definition of Terms
The definition is based on the
observable characteristics of that which
is being defined.
What is important is the nature of these
observations upon which definitions
are based.

There are 3 approaches or types of
constructing definitions. These are
arbitrarily labelled as A, B, and C by Bruce
W. Tuckman.
A type A definition can be constructed in
terms of the operations that must be
performed to cause the phenomenon or
state being defined to occur.
An intelligent child can be defined
operationally as the child produced by the
marriage of above average, intelligent
couples.

A type B definition can be constructed in
terms of how the particular object or thing
defined operates, that is what it does or
what constitute its dynamic properties.
Thus an intelligent student can be
operationally defined as a person who gets
high grades in school or a person who
demonstrates capability for solving
complicated mathematical problems.

A type C definition can be constructed
in terms of what the object or
phenomenon being defined looks like
that is what constitutes its static
properties. Thus, an intelligent student
can be defined for instance as a person
who has a good memory, large
vocabulary, good reasoning ability,
good mathematical skills, etc.

Ideally, the operational definition
should contain three parts. The first
part is its universal meaning. The
second part is how it is being used in
the study. The third is how it is being
measured.

6. Importance of the Study
It is at this point that the researcher
described who will benefit and what benefits
can be derived from the findings of the
study. The writer, under this section, tries
to sell its importance to the panel or to the
funding agency.

7. Scope and limitations of the Study
This tells the specific boundaries of
the study by describing the place or venue
of the study, the population,
subjects/respondents, time frame, the
variables and their indicators.
Any weakness of the study such as
failure to use a more precise data
gathering or measuring instrument or
failure to execute an important procedure
due to certain circumstances beyond the
researcher’s control form part of the
study’s limitations.

Learner’s Output:
List of Related Literature

LITERATURE REVIEW
A literature review is a re-
view of something that has
already been written

STEP 1a: Literature Review: The Research
Powerhouse
• Generativity is one of the hallmarks of
scholarship (Shulman, 1999). It is the
ability to build on the scholarship and
research of those who have come before
us.
A literature review is an account of what
has been published on a topic by
accredited scholars and researchers
51

 A literature review can be a precursor in
the introduction of a research paper
 A literature review is a critical and in
depth evaluation of previous research. It
is a summary and synopsis of a particular
area of research, allowing anybody
reading the paper to establish why you
are pursuing this particular research
project.
Why do a literature review?

Tips for Searching for Resources
on the Internet
 Finding related research articles
typically requires competence on
the internet.
 Search through databases that
have indexed information on
thousands of research articles
that have been conducted

on the Internet
 List the major or key variables/concepts in the study
 List synonyms for each variable
 Outline the major points to be made in the literature
review
 Do not limit your search to only studies that examine all of
the same variables as your study.
 Put key phrases in quotation marks
 When searching online, use the limit function to reduce
searches that have too many results.
 Limit your use of Google
 Do not cite wikipedia as a source. Like Google, anybody
can edit articles on wikipedia. Therefore, wikipedia
should never be used as a source for an academic paper.
 Use the resources you have to find additional resources.

on the Internet
Boolean logic is the way to
put terms together in a
search by using AND, OR,
NOT

Using AND
 When you use AND you will be
looking for articles containing two or
more words within each article.
For example, employee AND
motivation would retrieve articles
with both words in the article.
Use AND when you are searching for
concepts and want to be more
specific in your search (to narrow it
down).

Using OR
When you use OR you will be looking
for articles containing either one
word or the other word.
For Example, employee OR
personnel OR staff. You would use
OR for similar concepts and
alternative words or synonyms (to
broaden out your search).

Using NOT
When you use NOT you will be
looking for one term but not the
other.
For example, you might search for
broadband NOT wireless. You would
use NOT to exclude irrelevant results
(to narrow down your search).

Table 1 Writing styles – opening sentence
Good opening style Opening style to avoid
Early work by Thomas (1996)
shows that …
Another study on the topic by
Brown (2000) asserts that …
The latest research (Smith,
2003) show …
Thomas (1996) said …
Brown said (2000) …
Smith (2003) wrote ….

Table 2 Verbs and synonyms, to use in writing about text and making an
argument
Account for Clarify Describe Exemplify Investigate Recognize
Analyze
Argues
Assess
Assert
Assume
Claim
Compare &
contrast
Conclude
Criticize
Debate
Defend
Define
Demonstrat
e
Determine
Discuss
Distinguish
Differentiate
Evaluate
Emphasize
Examine
Expand
Explain
Exhibit
Identify
Illustrate
Imply
Indicate
Judge
Justify
Narrate
Outline
Persuade
Propose
Question
Reflect
Refer to
Relate to
Report
Review
Suggest
Summarize

Table 3. Forming critical sentences using signaling words
As a consequence of x then y
Consequently, …
Hence …
Therefore, …
Thus …
In short …
In effect …/ It follows that …
This indicates that …
This suggests that …
This points to the conclusion that …
This most obvious explanation is …
This means that …
Finally, …
Source: Brown and Keeley (2004)

Rule 1: State the theory
Suggested Sentence Stems
The theoretical basis of this paper is
This paper is theoretically anchored on
This paper is premised on
The theory of ______ underpins this study
We draw on ___________ to (state the objective of the
paper)
Rule 2: Explain the theory
Rule 3: Contextualize the theory
Writing the Theoretical Background
(The SEC Approach)
LITERATURE REVIEW

Rule 1: Synoptic Dimension
 Defining what the construct is all about
 Stating what has been said about the
variable (relationship, effect, difference) or it
historical development
Rule 2: Argumentative Dimension
 Build arguments either through sentence of
problematising (SOP) or the need for the
study (NFS)

Variable: Teaching Beliefs
Literature 1
Literature 2
Literature 3
Literature 4
Literature 5
Literature 6
Literature 7
Claim 1
Claim 2
Claim 3
Evidence
Evidence
Evidence
How do the findings
relate?
How do the findings
differ?
From these similarities
and differences, what
can we possibly
claim?
Cite specific studies
from your literature
review that will support
the claims made in
frame 2
Indicate the findings
of each of the
literature reviewed

The Need for
Dendrogramming
Literature 1
Finding 1
Finding 2
Finding 3
Literature 2
Finding 1
Finding 2
Finding 3
Finding 4
Literature 3
Finding 1
Finding 2
Finding 3
Literature 4
Finding 1
Finding 2
Finding 3
Literature 5
Finding 1
Finding 2
Finding 3
Finding 4
Finding 5

Example write-up (CF)
The conceptual framework underlying this study is
anchored on the concepts of research capability,
workload, and research productivity.
Research Capability
Research capability is simply the capability of the
faculty to undertake research. All the resources or
inputs which enable the faculty member to conduct
research are considered as components of research
capability (Deza, 1999; Banaag, 1994). Salazar-
Clemena and Almonte-Acosta (2007) enumerated
indicators of research capability which include budget
for research, the ability to obtain research grants, the
provision of research infrastructure, the ability to
collaborate with and access to research professionals,
and the presence of rules and procedure on the
granting of rewards for research.

 In this study, research capability is described in terms
of technical skills in doing research, skills in
conceptualizing a research problem, knowledge and
skills in designing the research plan, knowledge and
skills on research data processing, and knowledge and
skills in writing the research paper. Technical skills
include written communication (expressing one’s
ideas and arguments using language rules, presenting
and packaging ideas effectively); oral communication
(expressing one’s ideas and arguments using
language rules, presenting and packaging ideas
effectively); critical /analytical thinking (evaluating
ideas, analyzing the arguments of others); problem-
solving; research organization (parts, format of a
research paper); online search , use of electronic
resources, databases & search engines; use of
computer commands/programs/ software; and
acknowledging or citing sources/ cross-referencing.

Determinants of Research Productivity
 Previous foreign and local studies have revealed
that the reasons for low research productivity
among faculty members are poor or lack of
research skills (Anunobi & Emerole, 2008; Iqbal,
2011); lack of research funds (Anunobi &
Emerole, 2008; Iqbal, 2011; Mahilum, 2010);
and heavy workload or teaching overload (Iqbal,
2011; Mahilum, 2010; Mordeno, 2002). Iqbal
(2011) added performance of administrative
duties along with academic duties, nonexistence
of research leave, negative attitude of the faculty
towards research and absence of professional
journals while Anunobi & Emerole (2008)
included time constraints as impediments to
research publication.

Determinants of Research Productivity
 Predictors of research productivity include
teachers training or having research
orientation (Finkelstein, 1984, Banaag, 1994,
Mordeno, 2002); academic rank (Flanigan, et
al.,1988; Banaag, 1994); highest educational
attainment (Finkelstein, 1984; Flanigan, et
al.,1988; Banaag, 1994);and sufficient time
allocated to research (Finkelstein, 1984).

While several studies have been made to investigate
correlates of research productivity, studies on
research capability in terms of specific research
skills of teachers were lacking. In this end, the
researchers were motivated to conduct this research
that explored the levels of proficiency of teachers on
different skills that determine their capability in doing
research and how this capability can be associated
to research productivity. Workload in terms of hours
of work and number of teaching preparations was
also investigated to verify its impact on faculty
productivity in research. In the end, it is aimed that
this research may contribute to the existing
literatures on determinants of research productivity.

Remember!
 Read enough background material to
discuss the research and the theory
giving a reasonably complete account of
our knowledge of the topic
 Present data that are based on data and
theory, including conflicting views of
different researchers.
 Make it easy for the reader to
understand how all of the studies
interrelate.

Writing the Introduction
(The TIOC Approach)
Pointers: The TIOC Approach
• Highlight then trend/s in the field
• Pinpoint the issues underlying the trend/s
• State the overall objective/intent of the
paper in the light of the gap identified
• Discuss the possible contribution of the
research attempt to advancing/improving
disciplinal theory, research, practice and
policy
• (cross-reference to strengthen claims
Source: De Guzamn (2012). Writing for Intl Publication

Some Approaches to Starting the
Introduction
 Make a compelling statement about an
important issue
There is a strong evidence that computer games are hugely
popular. For example, as of 2002, more money was spent on
computer games in the United States- 6.9billion dollars- than
on box-office movies, and approximately 145 million
Americans (or about 60% of the population over age 6)
regularly played computer games (Lee, Park, & Jin, 2006).
Advocates of educational gaming have proposed that
educators should harness the appeal of computer games as a
vehicle for fostering student learning, but reviews of the
research literature have not yielded strong support for the
instructional effectiveness of computer games (Adams,
Mayer, McNamara, Koenig, & Wainess, 2011).

Introduction
 Identifying the Scope of Previous
Research
The literature on suicide and suicide risk factors is extensive.
The research includes clinical reports, intervention strategies,
identification of individual risk factors, demographic patterns
of suicide, and estimates of base rates in different ages and
culture. A subset of this literature has examined suicide in
college students. College students suicide research is
longstanding and an increasing number of articles address
the topic each year (Stephenson, Belesis, & Balliet, 2005)

Introduction
 Presenting a Statistics
Health outcomes are increasingly
recognized as socially patterned, In 2001-
2002, the leading causes of death were
heart disease, cancer and stroke (Jackson,
Kubzansky, & Wright, 2006).

Introduction
 Describing common occurrences
For traditionally male jobs,.. Women are
less likely to be hired than men. They are
also paid less, given less authority, and
promoted less often. ..Conversely, male
applicants are discriminated against for
jobs that are considered feminine (Ulhmann
& Cohen, 2005),

Plagiarism
“Authors do not present the work
of another as if it were their own
work”.
Whether paraphrasing, quoting an author
directly, or describing an idea that
influenced your work, you must credit the
source. To avoid charges of plagiarism,
take careful notes as you research to keep
track of your sources and cite those
sources according to the guidelines.

Table 6.1. Basic Citation Styles
Type of citation First citation in
text
Subsequent
citations in text
Parenthetical
format, first
citation in text
Parenthetical
format,
subsequent
citations in text
One work by
one author
Walker (2007) Walker (2007) (Walker, 2007) (Walker, 2007)
One work by
two authors
Walker and
Allen (2004)
Walker and
Allen (2004)
(Walker &
Allen, 2004)
(Walker &
Allen, 2004)
One work by
three authors
Bradley,
Ramirez, and
Soo (1999)
Bradley et al.
(1999)
(Bradley,
Ramirez & Soo,
1999)
(Bradley et al.,
1999)
One work by
four authors
Bradley,
Ramirez, Soo
and Walsh
(2006)
Bradley et al.
(2006)
(Bradley,
Ramirez, Soo &
Walsh, 2006)
(Bradley et al.,
2006)

Type of citation First citation in
text
Subsequent
citations in text
Parenthetical
format, first
citation in text
Parenthetical
format,
subsequent
citations in text
One work by
five authors
Walker, Allen,
Bradley,
Ramirez, and
Soo (2008)
Walker et al.
(2008)
(Walker, Allen,
Bradley,
Ramirez, & Soo,
2008)
(Walker et al.,
2008)
One work by six
or more authors
Wasserstein et
al. (2005)
Wasserstein et
al. (2005)
(Wasserstein et
al., 2005)
(Wasserstein et
al., 2005)
Groups (readily
identified
through
abbreviation) as
authors
National
Institute of
Mental Health
(NIMH, 2003)
NIMH (2003) (National
Institute of
Mental Health
[NIMH], 2003)
(NIMH, 2003)
Groups (no
abbreviaton) as
authors
University of
Pittsburgh
(2005)
University of
Pittsburgh
(2005)
(University of
Pittsburgh,
2005)
(University of
Pittsburgh,
2005)

References
 Beins, B.C. APA simplified style: Writing in
psychology, nursing, education, and
sociology. USA: John Wiley & Sons, Inc.
 De Guzman, A.B. Writing for international
publication. Presented in a seminar-
workshop 2012
 explorable.com/what-is-a-literature-
review
 Korb, K. (2015). Conducting educational
research: Search the Research Literature

Understanding Ways to Collect
Data
1. Research Design
A research design is a plan or strategy
in order to answer the research problem
and control (variance) for validity. This is
the over-all plan for the conduct of the
investigation.
Hence, substantially a design is
intended to answer the problem; and,
technically it provides control for validity.

Data
1. Research Design
Essentially, research designs may be
classified only in two (2) categories on the
basis of maximum control for validity:
1. non-design or non-experimental
(descriptive)
2. True Design or experimental design

Data
1. Research Design
EXPERIMENTAL RESEARCH
A. Pre-experimental design (non-design)
Not recommended for use
-designs which do not control adequate
against sources of internal validity
1. One shot case study
2. One-group pre-test-post-test design
B. Quasi-experimental design
C. True Experimental Design

Data
1. Research Design
-this design controls some but not all
sources of internal invalidity due to existing
conditions by which experimental control is
difficult if not impossible.

Data
1. Research Design
1. Expost facto design – This is the study in which
the researcher examine the effects of
naturalistically occurring treatment after that
treatment has occurred rather than creating
the treatment itself. The researcher attempts
to rotate this after the fact.
2. Co-relational standard

Data
1. Research Design
1. Expost facto design
2. Co-relational standard – this involves two or
more sets of data from a group of subjects with
an attempt to determine the subsequent
relation between those sets of data.

Data
1. Research Design
1. Expost facto design
2. Co-relational standard – serve as useful
purpose in determining the relationship among
measures and suggesting possible bases for
causality, while correlation does not necessarily
imply causation.

Data
1. Research Design
C. True Experimental Design – provide
complete adequate controls for all sources of
internal invalidity (only possible for non-human
subjects
1. Post-only control group design
2. Pretest-post test control group design

Experimental Research
 Most powerful design
 Used to establish cause and effect by
manipulating (influencing) an IV
(independent variable, aka
treatment or experimental variable)
to see its effect on a DV (dependent
variable ,aka criterion or outcome
variable)
 Goes beyond description and
prediction

 Comparison of groups (at least two groups of
subjects, called treatment and control groups)
 Manipulation of the IV (experimenter changes
something for the treatment group that’s
different than the control group)
 Randomization (true experiments require
random assignment into treatment/control
conditions…after random selection of subjects to
participate in study)
 Assignment takes place at start of experiment

 Do not use already formed groups
 Groups should be equivalent (any
differences due to chance)
 Randomization eliminates threats
from extraneous variables
 Groups must be sufficiently large to
be equivalent

 All extraneous variables must be controlled
to eliminate threats to validity/rival
hypotheses
 Ensure groups are equivalent to begin using
randomization
 Hold certain variables constant (i.e. age,
IQ) or build them into to the design

 Use matching when necessary
 Use subjects as their own controls
(treat same group first in control
condition then in treatment OR use
pre-test/posttest on same group)
 Use analysis of covariance to
statistically equate unequivalent
groups

(Group Designs)
 Weak Designs(Pre experimental Designs)
 True Experimental Designs
 Quasi Experimental Designs

Pre-Experimental Designs
 Do not adequately control for the problems
associated with loss of external or internal
validity
 Cannot be classified as true experiments
 Often used in exploratory research
 Three Examples of Pre-Experimental Designs
◦ One-Shot Design
◦ One-Group Pretest-Posttest Design
◦ Static Group Design

One-Shot Design
 A.K.A. – after-only design
 A single measure is recorded after the treatment
is administered
 Study lacks any comparison or control of
extraneous influences
 No measure of test units not exposed to the
experimental treatment
 May be the only viable choice in taste tests
 Diagrammed as: X O1

One-Group Pretest-Posttest
Design
 Subjects in the experimental group are
measured before and after the treatment
is administered.
 No control group
 Offers comparison of the same individuals
before and after the treatment (e.g.,
training)
 If time between 1st & 2nd measurements is
extended, may suffer maturation
 Can also suffer from history, mortality, and
testing effects
 Diagrammed as O1 X O2

Static Group Design
 A.K.A., after-only design with control group
 Experimental group is measured after being exposed to the
experimental treatment
 Control group is measured without having been exposed to
the experimental treatment
 No pre-measure is taken
 Major weakness is lack of assurance that the groups were
equal on variables of interest prior to the treatment
 Diagrammed as: Experimental Group X O1
Control Group O2

Pretest-Posttest Control
Group Design
 A.K.A., Before-After with Control
 True experimental design
 Experimental group tested before and after
treatment exposure
 Control group tested at same two times without
exposure to experimental treatment
 Includes random assignment to groups
 Effect of all extraneous variables assumed to be
the same on both groups
 Do run the risk of a testing effect

Pretest-Posttest Control Group
Design
 Diagrammed as
◦ Experimental Group: O1 X O2
◦ Control Group: O3 O4
 Effect of the experimental treatment equals
(O2 – O1) -- (O4 – O3)
R
R

Posttest-Only Control Group
Design
 A.K.A., After-Only with Control
 Experimental group tested after treatment exposure
 Control group tested at same time without exposure
to experimental treatment
 Includes random assignment to groups
 Effect of all extraneous variables assumed to be the
same on both groups
 Do not run the risk of a testing effect
 Use in situations when cannot pretest

Posttest-Only Control Group
Design
 Diagrammed as
◦ Experimental Group: X O1
◦ Control Group: O2
 Effect of the experimental treatment equals
(O2 – O1)
 Example
◦ Assume you manufacture an athlete’s foot remedy
◦ Want to demonstrate your product is better than
the competition
◦ Can’t really pretest the effectiveness of the remedy
R
R

Solomon Four-Group Design
 Combines pretest-posttest with control group
design and the posttest-only with control group
design
 Provides means for controlling the interactive
testing effect and other sources of extraneous
variation
 Does include random assignment

Solomon Four-Group Design
 Diagrammed as
◦ Experimental Group 1: O1 X O2
◦ Control Group 1: O3 O4
◦ Experimental Group 2: X O5
◦ Control Group 2: O6
 Effect of independent variable (O2 – O4) & (O5 –
O6)
 Effect of pretesting (O4 – O6)
 Effect of pretesting & measuring (O2 – O5)
 Effect of random assignment (O1 – O3)
R
R
R
R

Quasi-Experimental Designs
 More realistic than true experiments
 Researchers lacks full control over the scheduling of
experimental treatments or
 They are unable to randomize
 Includes
◦ Time Series Design
◦ Multiple Time Series Design
 Same as Time Series Design except that a control
group is added

Time Series Design
 Involves periodic measurements on the dependent variable
for a group of test units
 After multiple measurements, experimental treatment is
administered (or occurs naturally)
 After the treatment, periodic measurements are continued
in order to determine the treatment effect
 Diagrammed as:
O1 O2 O3 O4 X O5 O6 O7 O8

Experimenter Bias Effect
 The intentional or unintentional influence
that an experimenter (researcher) may
exert on a study

Correlation Research
(Predicting Outcomes Through
Association)
 Correlational research involves study of existing
relationships between two variables
 Descriptive in nature
 Often a precursor to experimental research
 Positive correlation is Hi/Hi and Lo/Lo (coeff. +r)
 Negative correlation is Hi/Lo and Lo/Hi (-r)
 Purpose is to explain relationships or to predict
outcomes

(Predicting Outcomes
Through Association)
 Explanatory studies examine relationship to
identify possible cause/effect
 Relationship might or MIGHT NOT mean
causation
 For causation: 1) A before B; 2) A and B
related; 3) Rule out other causes of B (need
experiment)
 Prediction studies identify predictors of
criterions (i.e. HS GPA and College GPA)
 The stronger the correlation the better the
prediction

Association)
 Complex Correlation Techniques, such as multiple
regression allow use of several predictors for one criterion
 Coefficient of multiple correlation (R) gives strength of
correlation between predictors and criterion
 Coefficient of determination (r2) is amount x and y vary
together
 Descriminant function analysis is for non-quantitative
criterion (predict which group someone will be in)
 Other techniques also used (factor analysis, path analysis,
structural modeling)

Association)
 Problem selection – usually it’s are x and y related
or how well does p predict c
 Sample – random selection of at least 30
 Measurement – need quantitative data
 Design/Procedures – need two measures on each
subject
 Data collection – usually both measures close in
time
 Data analysis – correlation coefficient, r, and plot
(r is -1 to +1, and the closer to plus or minus 1, the
stronger the relationship)

 General guidelines:
+.75 to +1.0 Very strong relationship
+.50 to +.75 Moderate strong
relationship
+.25 to +.50 Weak relationship
+.00 to +.25 Low to no relationship
 Need .5 or better for prediction of any
use, and .65 for accurate predictions
 Reliability coefficients should be .7 up
 Validity coefficients should be .5 up

 Remember correlation is not causation
(lurking variables)
 Subject characteristics – may get different
correl w/ different ability levels, gender,
etc. (can control with partial correlation)
 Location – testing conditions can impact
results
 Instrumentation problems – helps to
standardize instrument and data collection
for both groups

 What factors could affect the variables
being studied?
 Does any factor affect BOTH variables? (this
is where threats occur)
 Figure a way to control any lurking
variables

Causal Comparative Research
(Ex Post Facto)
 Determines cause (or effect) that has occurred and
looks for effect (or cause) from it
 Start w/ differences in groups and examine them
 Examples: Difference in math abilities of
male/female students
 No random assignment to treatment (it already
occurred)
 Associational like correlation but primarily
interested in cause/effect
 IV either cannot (ethnicity) or should not
(smoking) be manipulated

Causal Comparative versus
Correlational Research
 Often an alternative to experimental (faster
and cheaper)
 Serious limitation is lack of control over
threats to internal validity
 Need to remember the cause may be the
effect; they may only be related and there is
some other variable that is the cause
(lurker)

Causal Comparative versus
Correlational Research
 Both are associational (looking for relationship)
 Both are often prelude to experiments
 Neither involves manipulation of variables
 Causal Comparative works with different groups;
correlation examines one group on different
variables
 Correlation is measured w/ coefficient while
Causal comparative compares
means/medians/percents of group members

versus Experimental Research
 Both compare group scores of some type
 In experimental the IV is manipulated, but
not in CC (already took place)
 CC does not provide as strong evidence as
experimental for cause and effect

(Steps)
 Problem formation – identify phenomena and look
for causes or consequences of it
 Sometimes several alternate hypotheses
investigated
 Sample – define (operationally) characteristics of
study carefully, then select individuals who possess
 Groups should be homogeneous in regard to several
important variables (to control for them as causes)
then match control/experimental groups on one or
more variables
 Instruments – use any type to compare the groups
 Design – basic CC involves 2 or more groups that
differ on variable of interest (basic design is one
group possesses trait (athlete) other doesn’t
compare DV (GPA)

(Threats to Internal Validity)
 Subject characteristics – since don’t select
subjects and form groups, there may be
unidentified lurking variables
 Can use matching to control for any identified
differences, but limits samples size
 Can find or create homogeneous groups (for
example compare only high GPA students to
other high GPA students) on attitudes toward x
 Statistical matching – adjusts posttest scores
based on some initial difference
 Other threats – location, instrument, history,
maturation, loss of subjects can be concerns
 Need to control as many as possible to eliminate
alternate hypotheses

Survey Research
(Used to describe what people
think/do/believe)
Types
 Cross sectional provide a snapshot in time
 Longitudinal collect data at different points in time
to study changes over time
 Trend study - random sample each year on same
topic
 Cohort study - sample from same cohort members
year after year
 Panel study - same individuals surveyed year after
year (mortality a problem over long time periods)
 Often surveys are the data collection instrument in
correlation (or cc/exp’l) studies

Steps to conduct Survey
Research
 Define the problem
Needs to be important enough
respondents will invest their time to
complete it
Must be based on clear objectives
 Identify the target population
Defined by sample unit or unit of analysis
Unit can be a person, school, classroom,
district, etc.)
Survey a sample or do a census of the
population

Survey Research
(Steps to conduct survey research)
Methods of data collection
Direct administration to a group (such as at a meeting)
- good response rate, limited generalize.
Mail survey (inexpensive way to get large amount of
data from widespread pop) - lower response rates, not
in-depth info, illiterate missed
Telephone survey (cheap/fast) - response rates higher
due to encouragement (“I’m not selling…”); miss some
pop members, interviewer bias possible
Personal interviews (face-to-face has good response
rate but time and cost high) - lack anonymity,
interviewer bias

Survey Research
 Select the sample (randomly, but check to see
respondents are qualified to answer)
Pilot test can indicate likely response rate and
problems with data collection or sample
 Prepare instrument (questionnaire and
interview schedule)
 Appearance important - look short and easy
 Clarity in questions is essential

Survey Research
 Question types (same questions need to be asked
of all respondents)
 Closed ended (multiple choice) - easier to
complete, score, analyze
 Categories must be all inclusive, mutually
exclusive
 Open ended - easy to write, hard to analyze and
hard on respondents

Population
This describes the population of the study and
the method of getting the representative
sample (of the population). The total
population of interest and the number of the
sample subjects of the study are given and
embodied in a table.

 Sample – any group on which info is obtained
 Population – group that researcher is trying to
represent
 Population must be defined first; more closely
defined, easier to do, but less generalizable
 Study a subset of the population because it is
cheaper, faster, easier, and if done right, get same
results as a census (study of whole population)
 Accessible population – the group you are able to
realistically generalize to…may differ from target
population
Sample and Population

 Random – every population element has an
equal and independent chance to participate
Uses names in a hat or table or random
numbers
Elimination of bias in selecting the sample
is most important (meaning the researcher
does not influence who gets selected)
Ensuring sufficient sample size is second
most important
Random v. Nonrandom Sampling

 Nonrandom/purposive - troubles
with
representativeness/generalizing
Random v. Nonrandom Sampling

Names in a hat or table of random
numbers
Larger samples more likely to
represent population.
Any difference between
population and sample is random
and small (called random sampling
error)
Simple Random Sampling

 Ensures small subgroups (strata)
are represented
 Normally proportional to their part
of population
 Break population into strata, then
randomly select w/in strata
 Multistage sampling
Stratified random sampling

 Select groups as sample units
rather than individuals
 REQUIRES a large number of
groups/clusters
 Multistage sampling
Cluster Random Sampling

 Considered random is list if
randomly ordered or nonrandom if
systematic w/ random starting
point
 Divide population size by sample
size to get N (ps/ss=N)
 Systematic can be nonrandom if
list is ordered
Systematic (Nth) Sampling

 Using group that is
handy/available (or volunteers)
 Avoid, if possible, since tend not
to be representative due to
homogeneity of groups
 Report large number of
demographic factors to see
likeliness of representativeness
Convenience Sampling

 Using personal judgment to select
sample that should be
representative (i.e., this faculty
seems to represent all teachers)
OR selecting those who are known
to have needed info (interested in
talking only to those in power)
 Snowball is a type (used with hard
to identify groups such as addicts)
Purposive Sampling

Sample size affects accuracy of
representation
 Larger sample means less
chance of error
 Minimum is 30; upper limit is
1,000 (see table)
Sampling

Representative sample is required (not
the same thing as variety in a sample)
 High participation rate is needed
 Multiple replications enhance
generalization when nonrandom
sampling is used
 Ecological generalization (generalizable
to other settings/conditions, such as
using a method tested in math for
English class)
Sampling

Data Collection Procedure
This represents the logical procedure in
collecting and treating data to answer the research
question and the hypothesis:
The usual order of presentation of this
section is chronological, for instance:
1) Requesting permission from the concerned
authorities to conduct the investigation and to
administer the research instruments to the
subjects, including its approval thereof attached as
an appendix;

Data Collection Procedure
2) Orientation and actual administration or
mailing of the research instrument;
3) Follow-ups of those who failed to return
the instrument before the deadline set;
4) Gathering of the duly accomplished
research instrument.
This section tells the reader what you did
and how you did it. Any errors or weaknesses in
the procedures that have been discovered during
the conduct of the research should be pointed out,
and any consequent limitations upon the research
should be fully noted.

Instrumentation
(Measurement)
• Data – information researchers obtain
about subjects
◦ Demographic data are
characteristics of subjects such as
age, gender, education level, etc.
◦ Assessment data are scores on
tests, observations, etc. (the device
used to measure these is called the
measurement instrument)

Instrumentation
• Validity – measures what it is supposed to
(accurate)
• Reliability – a measure that consistently
gives same readings (repeatable)

Instrumentation
• Objectivity – absence of subjective
judgments (need to eliminate subjectivity
in measuring)
• Usability of instruments
◦ Consider ease of administration; time
to administer; clarity of directions;
ease of scoring; cost; reliability/validity
data availability

Instrumentation
(Classifying Data Collection
Instruments)
• By the group providing the data
◦ Researcher instruments (researchers
observes student performance and
records)
◦ Subject instruments (subjects record
data about themselves, such as taking
test)
◦ Others/Informants (3rd party reports
about subjects such as teacher rates
students)

• By where instrument came from
◦ Preference is for existing
◦ Can develop your own (requires time,
effort, skill, testing;
• By response type
◦ Written response – preferred – objective
tests, rating checklist
◦ Performance instruments – measure
procedure, product
Instrumentation
(Classifying Data Collection
Instruments)

Instrumentation(Examples of
Data Collection Instruments)
• Researcher Completed Instruments
◦ Rating scales (mark a place on a continuum
for example numeric rating 1=poor to 5=
excellent)
◦ Interview schedules (complete scales as
interview takes place; use precoding; beware
of dishonesty)

Instrumentation(Examples of
Data Collection Instruments)
• Researcher Completed Instruments
◦ Tally sheets (for counting/recording
frequency of behavior, remarks, activities,
etc.)
◦ Flow charts (to record interactions in a room)
◦ Anecdotal records (need to be specific and
factual)
◦ Time/Motion logs (record what took place and
when)

Instrumentation
• Item Formats
◦ Selection items or closed response (T/F;
Yes/No; Right/Wrong; Multiple choice)
◦ Supply items or open ended (short answer;
essay)
◦ Unobtrusive measures (no intrusion into
event… usually direct observation and
recording)

Instrumentation
• Types of Scores
◦ Raw scores (initial score or count
obtained…w/out context)
◦ Derived scores (raw scores translated to
meaningful usage with standardized process)
 Age/Grade equivalence; Percentile ranks;
Standard scores (how far a score is from a given
reference point, i.e. z and T scores);
 Which to use depends on the purpose; usually
standard scores used

Instrumentation
• Norm Referenced v. Criterion Referenced Tests
• Norm referenced scores give a score relative to
a reference group (the norm group)
◦ Criterion referenced scores determine if a
criterion has been mastered
◦ These are used to improve instruction since
they indicate what students can or cannot do
or do or do not know

Instrumentation
(Measurement Scales)
• Nominal (in name only)
◦ Numbers are only name tags, they have no
mathematical value (gender: 1=male and 2=
female OR race: 1= Blk, 2=Wht, 3=other)
• Ordinal (in name, plus relative order)
◦ Numbers show relative position, but not
quantity (grade level, finishing place in a
race)

Instrumentation
(Measurement Scales)
• Interval (in name w/ order AND equal distance)
◦ Numbers show quantity in equal intervals, but an
arbitrary zero (can have negative numbers;
degrees C or F)
• Ratio (in name, w/ order, eq. distance AND absolute
zero)
◦ Numbers show quantity with base of zero where
zero means the construct is absent
• Higher levels more precise…collect data at highest
level possible; some statistics only work with higher
level data

Instrumentation
(Preparing for Data Analysis)
• Scoring data – use exact same format for
each test and describe scoring method in
text
• Tabulating and Coding – carefully transfer
data from source documents to computer
◦ Give each test an ID number
◦ Any words must be coded with numerical
values
◦ Report codes in text of research report

Measurement Instruments
 Types of instruments
◦ Cognitive – measuring intellectual processes
such as thinking, memorizing, problem
solving, analyzing, or reasoning
◦ Achievement – measuring what students
already know
◦ Aptitude – measuring general mental ability,
usually for predicting future performance

 Types of instruments (continued)
◦ Affective – assessing individuals’ feelings,
values, attitudes, beliefs, etc.
 Typical affective characteristics of interest
◦ Values – deeply held beliefs about ideas, persons, or
objects
◦ Attitudes – dispositions that are favorable or unfavorable
toward things
◦ Interests – inclinations to seek out or participate in
particular activities, objects, ideas, etc.
◦ Personality – characteristics that represent a person’s
typical behaviors

◦ Affective (continued)
 Scales used for responding to items on affective tests
◦ Likert
 Positive or negative statements to which subjects
respond on scales such as strongly disagree, disagree,
neutral, agree, or strongly agree
◦ Semantic differential
 Bipolar adjectives (i.e., two opposite adjectives) with a
scale between each adjective
 Dislike: ___ ___ ___ ___ ___ :Like
◦ Rating scales – rankings based on how a subject would
rate the trait of interest
Obj. 5.1

◦ Affective (continued)
 Scales used for responding to items on affective
tests (continued)
◦ Thurstone – statements related to the trait of interest to
which subjects agree or disagree
◦ Guttman – statements representing a uni-dimensional
trait
Obj. 5.1

 Issues for cognitive, aptitude, or affective
tests
◦ Problems inherent in the use of self-report
measures
 Bias – distortions of a respondent’s performance or
responses based on ethnicity, race, gender, language,
etc.
 Responses to affective test items
◦ Socially acceptable responses
◦ Accuracy of responses
◦ Response sets
◦ Alternatives include the use of projective tests

Finding the Answers to the
Research Question
1. Interpretation of Data

Descriptive Statistics
 For descriptive problems that require
finding out “what is,” as the term implies,
descriptive statistical analysis can be
used to describe the data. The mean,
median, mode and standard deviation are
the main descriptive statistical treatment
applicable. The mean or median is used
to indicate the average while the
standard deviation provides the
variability of the data/scores in the
sample.

Sample of Computer Output
N Min Max Mean SD
TEST
THIRDQ
FOURTHQ
Valid N
(listwise)
56
56
56
56
1.0
2.0
5.0
2.0
46.0
7.0
1.5
21.8
24.3
0.5
17.6
7.4

Sample Frequencies
Frequency Percent Percent
Cum.
Valid
Percent
Female
Male
Total
216
258
474
45.6
54.4
100.0
45.6
54.4
100.0
45.6
100.0

Illustration:
Characteristic Profile
A. Gender
F %
Male
Female
Total
216
258
474
45.6
54.4
100.0

Sample Interpretation
 as to gender, the respondents were
mostly female (since the modal class is
female).

Illustration 2.
Age
F %
 30-32 5 6.25
 27-29 43 53.75
 24-26 29 36.25
 21-23 3 3.75
 Total 80 100

Interpretation
◦ Results on the table show that most of the
respondents were within the age range of
27-39 (43 or 53.75%). However it could be
seen that the combined ranges from 24-26
to 27-39 composed almost 90% of the
respondents.
◦ From this, it could be said that most of the
respondents were young adults.

Descriptive Statistics Used in
Evaluation Studies

EVALUATION OF THE CONTEXTUAL
TEACHING MATERIALS BY
EXPERTS
Contents Mean Verbal Des.
 Concept definition 4.6 Excellent
 Presentation of concepts 4.6 Excellent
 Sufficiency of Problem
scenarios and examples 5.0 Excellent
 Sufficiency of questions to
ignite the critical thinking 4.8 Excellent
 Writing of the topics within
to the level of the student’s
understanding 4.8 Excellent

Interpret results on the
context of the study
 The concepts in the CTL were presented in
real situations that are familiar to the
students (X=4.6). This is the basic principle
strictly adhered to in a contextual teaching
approach, thus, if the materials fail in this
aspect, there is no contextual approach.
Since the experts judged the criterion as
excellent, it only means that the CTL
materials were successful in translating the
concepts to true-to-life experiences.

 Bivariate Analysis
Interval Data Pearson’s r
Ranked Data Spearman rho
Kendall Tau
Nominal data Chi square

 2 Groups T-test of Difference
between means of Independent Data
 2 sets of scores of 1 group (ie
Comparison of Pre & Posttest) T-test of
Difference Between Means of Correlated
Data
Comparison of 3 or more Groups –
Analysis of Variance.

Sample of a Correlation Matrix
# of
Childrn
Age Incom Yrs
inSch
Educ
# of
Childrn
r 1.0 .404 -.018 -.237 -.172
2-t sig . .000 .489 .000 .000
Age r .4041 1 -.047 -.250 .149
2-t sig .000 .070 . .000 .000
Incom r -.018 .047 1 .361 .360
2-t sig .489 .070 . 000 . .000
Yrs inSch r -.237 -.259 .361 1 .864
2-t sig .000 .000 .000 . .000

Interpreting correlation
coefficient
 Positive correlation:
X Y
X Y
 Negative correlation:
X Y
X Y

Illustration
 Subjects being Pearson’s r Significance
Related
Mathvs.MathNEAT 0.77095 significant
Sci vs.Sci(NEAT) 0.79908 significant
Eng vs.Eng(NEAT) 0.69801 significant
HEKASI vs HEKASI 0.23142 not sig.

 It is necessary to explore the statistical
significance by using the critical value,
however, it is much better to determine
whether the computed Pearson's r denotes
a high correlation between the variable
concerned because statistical
significance may only be negligible or
too low to consider. Computer
statistical outputs provide the
probability of alpha which may
indicate the percent of occurrence of
the error to reject the null hypothesis
when it is true.

Sample Interpretation
 As shown in the table, math
achievement is significantly related to
the result of the NEAT in mathematics
(r=.77). This means that the NEAT
results in mathematics relate to the
math achievement of the students in
school. If a pupil performs well in
school mathematics, he is likely to get
high in the NEAT.

Test of Difference
Between Groups

The Pretest/Posttest control
group Design
 Experimental grp. R O1 X O2
 Control grp. R O3 O4
where: 01 and 03 are pretests
02 and 04 are posttest

Possible Results of the design
 O2 = O4; The traditional and
experimental approach have the same
results.
 O2 > O4; The experimental group have
better results.
 O2 < O4: The control group have better
results.

Sample of T-test Output
One-Sample Statistics
N Mean Std. Dev Std.
Error
of the
mean
Pre
Post
29
29
6.50
40.20
1.60
4.00
.29
.74
t – Value = 0.8972 (Probability of t = 0.4831)

T Stat continued
Paired
Difference
Std.
Dev
t df Sig. (2-
tailed)
Pair 1
PRE
POST
33.70 2.90 61.05 28 .000

Sample of T-test Output
Independent samples
Group Statistics
N Mean Std. Dev Std. Error
of the
mean
Post
Exp Grp
Control
15
4
40.4
40.0
4.3
3.8
1.14
1.01

T Stat continued
T-TEST
FOR =
of
Means
Std.
Error
t df Sig. (2-
tailed)
Equal
variances
assumed
.232 1.5 .264 27 .794

Sample result for
Experimental
Design and Group
Comparison By T-
test

Difference Between 2 Groups
 Difference Between the Experimental &
Control groups in the pre-test
Statistics Experimental Control Group
 Mean 7.6 7.4
 SD 11.1 6.0
 N 50 50
 t – Value = 0.8972
 (Probability of t = 0.4831)

Interpretation
◦ The computed t-value for the difference
between the pretest scores of the control and
experimental groups shows no significant
difference since the probability of error (.4831)
is more than the target level (.05).
◦ The two groups are equally prepared for the
experimentation as indicated by the very close
means of the control (7.6) and experimental
groups(7.4).

Comparing 3 or
More Groups By
Analysis of
Variance

Illustrating an ANOVA Table
ANOVA Statistics for Weight Difference
of Three Groups of Broilers
Source of Var. df SS MS F Prob.
of F
 Between G 2 0.0932 0.046 2.84
0.0429
 Within G 9 0.1479 0.016
 Total 11 0.2411

Interpretation of
the ANOVA table

Analysis of Variance for the
Three Groups
 The ANOVA table shows that the computed F
is significant at 0.04 level. The difference was
significant among the groups concerned. At
0.05 level, the null hypothesis, which states
that no difference exists among the groups,
was rejected. It means that the three groups
of broilers were significantly different in terms
of feed conversion.
 (It is necessary to show the basis of the
difference, thus, the researcher must present
next the means of the three groups.

Tell the difference by
the means
Groups Mean
 Group A 18.5
 Group B 15.2
 Group C 15.4

Explain the reason
 The difference was explicit on the weight
of the broilers. The broilers mixed fed
with corn were heavier than the rest.
The two groups, those mixed fed with
grass and camote tops had almost similar
mean weights. This shows that corn
mixed in feeds resulted to heavier
chicken because of the high protein
carbohydrate content of corn compared
to those mixed fed with plant products.

Two-Way ANOVA
 To find Difference Among Groups
Mean1=Mean2=Mean3=…=Mean4
 To find Interaction Between Variables
MeanB11=MeanB12=MeanB13…=MeanBij

Illustration 1
Problem: Is constructivism strategy
effective in teaching Analytic Geometry?
One Solution: Test it between groups
 1 group given the constructivist Strategy
 1 group given the traditional approach

 Is there an interaction between method
of teaching and the ability of the
students?
Solution
 Use two-way ANOVA to compare
between groups and determine
interaction between variables.
Illustration 2

 Is Constructivist Strategy In Teaching
Effective?
 Is there an interaction between This
Method and the ability of the students?
Sample Problem

Using and
Interpreting the
Two-Way ANOVA
Results

Performance in Analytic Geometry by
treatment group
& Mathematical Background
Group Mathematical Background
High Average Low Total
T1 18.60 15.20 17.20 51
T2E 20.00 21.70 19.00 60.7
T3 14.50 17.10 15.00 46.6
T4E 19.20 19.60 13.90 52.7
72.30 73.60 65.10 211.0

Analyze mean performances and try to
find out the highest and the lowest.
 Observe that for those with high math
ability group the highest mean was for
the T2 group.
 For the Average and Low Math ability
groups, the highest means were also
recorded for the T2 Group.
 Among the three math ability groups, the
highest recorded performance was for
the average math ability group.

Two-Way ANOVA Statistics
SV SS df X2 F F Prob
 Group 115.70 3 38.56 6.17 0.029
 Math Bck 35.00 2 17.50 2.80 0.115
 Interaction 7.10 6 12.85 2.05 0.045
 Error 150.10 24 6.25
 Total 377.90 35

 To interpret the results, observe the
probability of alpha (p-value). This will
indicate whether the result is significant or
not. Since alpha is the probability of
rejecting the Ho when it is true, its value
must be less than the targeted alpha.
 Thus, the table shows that the interaction
is significant. This will be the basis for
answering the problem. If it is not
significant, it follows that the researcher
should examine the significance of the row
or column differences between the means.

 Since the Interaction effect is significant,
the researcher could pinpoint in the
conclusion the observe differences. The
higher means could be used as basis for
the conclusions.
 Since the highest mean was observed for
the average mathematics ability group, it
could be said that the constructivist
method worked well with them.
 T2 had the higher mean score compared
to T4 which is also an experimental group.
Compared to the control groups, both
experimental groups had high mean
performances.

Conceptualized Framework for
Qualitative Research

Draft Written Research Report
for Oral Presentation

Final Written Research Report for
Submission

INQUIRIES INVESTIGATIONS & IMMERSIONS.pptx

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