The document discusses the components of effective research instruments, emphasizing valid and reliable data collection methods such as questionnaires and performance tests. It outlines characteristics of good instruments, ways to develop them, and types of validity and reliability along with experimental design principles. Additionally, it highlights the importance of adhering to research ethics, including informed consent and participant confidentiality.

1. RESEARCH INSTRUMENT,
VALIDITY AND
RELIABILITY

2. Research Instruments
• They are basic tools researchers used to gather data for
specific research problems.
• Common instruments are performance tests,
questionnaires, interviews, and observation checklist.
• The first two instruments are usually used in quantitative
research, while the last two instruments are often in
qualitative research.

3. Characteristics of a Good
Research Instrument
• Concise.
• Have you tried answering a very long test, and because of
its length, you just pick the answer without even reading it?
• A good research instrument is concise in length yet can
elicit the needed data.
• Sequential.
• Questions or items must be arranged well. It is
recommended to arrange it from simplest to the most
complex. In this way, the instrument will be more favorable
to the respondents to answer.

4. Characteristics of a Good
Research Instrument
• Valid and reliable.
• The instrument should pass the tests of validity and reliability to
get more appropriate and accurate information.
• Easily tabulated.
• Since you will be constructing an instrument for quantitative
research, this factor should be considered. Hence, before crafting
the instruments, the researcher makes sure that the variable and
research questions are established. These will be an important
basis for making items in the research instruments.

5. Ways in Developing Research
Instrument
• There are three ways you can consider in developing
the research instrument for your study.
• First is adopting an instrument from the already utilized
instruments from previous related studies.
• The second way is modifying an existing instrument when the
available instruments do not yield the exact data that will answer
the research problem.
• And the third way is when the researcher made his own
instrument that corresponds to the variable and scope of his
current study.

6. Common Scales Used in
Quantitative Research
• Likert Scale. This is the most common scale used in
quantitative research. Respondents were asked to rate or
rank statements according to the scale provided.
• Example: A Likert scale that measures the attitude of
students towards distance learning.

7. Common Scales Used in
Quantitative Research
• Semantic Differential. In this scale, a series of bipolar adjectives
will be rated by the respondents. This scale seems to be more
advantageous since it is more flexible and easier to construct.
• Example: On a description of an active student in school
activities.
Pleasant 5 4 3 2 1 Unpleasant
Enthusiastic 5 4 3 2 1 Not Enthusiastic
Competent 5 4 3 2 1 Incompetent
• Another important consideration in constructing a research instrument is
how to establish its validity and reliability.

8. VALIDITY
• A research instrument is considered valid if it measures
what it supposed to measure.
• When measuring oral communication proficiency level of
students, speech performance using rubric or rating scale is
more valid than students are given multiple choice tests.
• Validity also has several types: face, content, construct,
concurrent, and predictive validity.

9. Types of Validity of Instrument
1. Face Validity. It is also known as “logical validity.” It
calls for an initiative judgment of the instruments as it
“appear.” Just by looking at the instrument, the researcher
decides if it is valid.
2. Content Validity. An instrument that is judged with
content validity meets the objectives of the study. It is
done by checking the statements or questions if this
elicits the needed information. Experts in the field of
interest can also provide specific elements that should be
measured by the instrument.

10. Types of Validity of Instrument
3. Construct Validity. It refers to the validity of instruments
as it corresponds to the theoretical construct of the study. It
is concerning if a specific measure relates to other measures.
4. Concurrent Validity. When the instrument can predict
results similar to those similar tests already validated, it has
concurrent validity.
5. Predictive Validity. When the instrument is able to
produce results similar to those similar tests that will be
employed in the future, it has predictive validity. This is
particularly useful for the aptitude test.

11. RELIABILITY
• RELIABILITY refers to the consistency of the measures or results of the
instrument.
a. Test-retest Reliability. It is achieved by giving the same test to the same
group of respondents twice. The consistency of the two scores will be checked.
b. Equivalent Forms Reliability. It is established by administering two
identical tests except for wordings to the same group of respondents.
c. Internal Consistency Reliability. It determines how well the items measure
the same construct. It is reasonable that when a respondent gets a high score
in one item, he will also get one in similar items. There are three ways to
measure the internal consistency; through the split-half coefficient, Cronbach’s
alpha, and Kuder-Richardson formula.

12. Key Features of Classic
Experimental Design
•Three Main Features:
• Independent and Dependent Variables
• Experimental and Control Groups
• Pre-Testing and Post-Testing (DeCarlo, 2018)

13. Independent and Dependent Variables
(IV & DV)
• Independent Variable (IV):
• Manipulated by the researcher (treatment/intervention)
• Varies by field:
• Education: Teaching strategy
• Psychology: Counseling method
• Medicine: Vaccine
• Business: Marketing strategy
• Agriculture: Seedlings
• Dependent Variable (DV):
• Measured outcome based on the intervention's effect on the IV

14. Experimental, Control, and Comparison
Groups
• Experimental Group: Receives the
intervention
• Control Group: No intervention, for
baseline comparison
• Comparison Group: Exposed to the
current standard practice
Example:
Experimental group: New teaching
method + test
Comparison group: Traditional
teaching + test
Control group: No lesson + test
• Pre-Testing and Post-
Testing
• Pre-Test: Given before
intervention to establish
baseline
• Post-Test: Given after
intervention to assess
impact
• Purpose: Comparison
of results to evaluate
intervention efficacy

15. Different Types Of Experimental
Design
• Pre-Experimental Research Designs
• Pre-Experimental Design includes the basic steps in experimental research
except it does not have an equivalent control group to compare the results
with.
One Shot Case Study X 2
𝑂
One Group Pre-test Post-test Study 𝑂1 X 𝑂2
Static Group Comparison Study X 2
𝑂
X 2
𝑂
Key:
X = intervention
𝑂1 = pre-test
𝑂2 = post-test

16. Quasi-Experimental Research Designs
• Quasi-Experimental
Design has a non-
equivalent control
group to compare
with, but it still does
not have the
randomization of
participants.
Pre-test Post-test Non-Equivalent
Groups
𝑂1 X 𝑂2
𝑂1 X 𝑂2
Time-Series Designs
𝑂1 𝑂1 X 𝑂2 𝑂2
Non-Equivalent Before-After Design
𝑂1 𝑂1 X 𝑂2 𝑂2
𝑂1 𝑂1 X 𝑂2 𝑂2
Key:
X = intervention
𝑂1 = pre-test
𝑂2 = post-test

17. True Experimental Research Designs
• True Experimental Design
employs equivalent control
group to compare the results
of the study with, and
participants are randomly
assigned to each group.
Post-test Equivalent Groups
𝑅 X 𝑂2
𝑅 X 𝑂2
Pre-test Post-test Equivalent Groups
𝑅 𝑂1 X 𝑂2
𝑅 𝑂1 X 𝑂2
Solomon Four-Group Design
𝑅 𝑂1 X 𝑂2
𝑅 𝑂1 𝑂2
𝑅 X 𝑂2
𝑅 𝑂2
Key:
R = randomization 𝑂1 = pre-test
X = intervention 𝑂2 = post-test

18. PLACEBO EFFECT
• A placebo is a simulated treatment that do not contain the
active ingredients that the experimental group is receiving,
and the placebo effect is the positive effect of such
intervention (Price et al., 2015). In other words, they are
not really receiving any kinds of treatments.
• These positive effects can be attributed to the thinking of
the placebo group that they will get better. The placebo
effect reduces their anxiety, stress, and depression and
can change their perception and even improve the
functioning of their immune system (Price et al., 2008, as
cited in Price et al., 2015).

19. Characteristics of Good Quantitative
Research
1.Reliability: Consistency of measurements or
observations
2.Validity: Measurement represents what it
intends to measure
3.Replicability: Study can be repeated for
verification
4.Generalizability: Applicability of findings to
the broader population

20. Describing the Research
Intervention
•Variation in Independent Variable:
• Wide: Extent of exposure (e.g., single vs.
multiple counseling sessions)
• Describing the Research Intervention
Level of measurement (e.g., categorical or
quantitative)

21. Assignment to Groups:
• Randomized Design: Equal chance of assignment
• Randomized Block Design: Grouping by
characteristics (e.g., age, gender) before
randomization
• Independent vs. Repeated Measure Design:
• Independent: One level of intervention
• Repeated: Multiple levels, but may introduce
carryover effects (practice, fatigue, context effects)

22. Controlling Confounding Variables
•Methods:
• Restriction: Only participants with similar
confounding values
• Matching: Pairing experimental and control
participants with similar values
• Statistical Control: Setting confounders in the
regression model
• Randomization: Balances confounders across
large samples

23. Adhering to Research Ethics
•Informed Consent:
• Explains study purpose, participant role,
benefits, and potential risks
• Ensures participant confidentiality and the
right to withdraw