Data

Collection
Procedures
LESSON 1
Today's 1 know the different
ways on how to analyze
Agenda data

2 select appropriate data

At the end of this session,
analysis procedure for
you will be able to:
the research study.
STEM_RS12-IIa-c-6
Overview:
Data Collection
Procedures
 Overview

Research methodology is
commonly acknowledged as
the most important part of any
research project that needs to
be established before the actual
data collection program begins.
It is like a road map.
Overview:
Importance of
Data Collection
Procedures
 The data collection provides the basis for
reliability estimations. Thus, a good data
collection procedure is crucial to ensure that
the reliability estimate is trustworthy. A
prediction is never better than the data on
which it is based.
- Wohlin, C., Höst, M., Runeson, P., & Wesslén, A. , 2023
Encyclopedia of Physical Science and Technology (Third Edition)
Quality of 1 Collection Consistency

good data
2 Completeness
collection
procedure: 3
Measurement System
Consistency
1. Collection Consistency
Data shall be collected and reported
in the same way all the time, for
example, the time for failure
occurrence has to be reported with
enough accuracy.
 2. Completeness

All data has to be collected, for

example, even failures for which the
tester corrects the causing fault.
3. Measurement System
Consistency
The measurement system itself must
as a whole be consistent, for example,
faults shall not be counted as failures,
since they are different attributes.
3. Measurement System
Consistency
A fault typically implies a defect or error in
a system or process, whereas a failure
denotes a situation where the system or
process does not meet its intended or
expected outcome.
3. Measurement System
Consistency
For instance, if a product has a certain
number of faults, they should not be
counted as failures unless they result
in a failure to meet the required
standards or specifications.
3. Measurement System
Consistency
The point being made is that when
measuring or assessing performance,
it is crucial to differentiate between
these attributes.
 Experimental
Research: What
it is + Types of
designs
A Review
Session
 Any research conducted under scientifically
acceptable conditions uses experimental
methods. The success of experimental studies
hinges on researchers confirming the change of
a variable is based solely on the manipulation of
the independent variable.
- Bhat (2023)
 What is Experimental
Research?
Is a scientific approach to research, where one or
more independent variables are manipulated and
applied to one or more dependent variables to
measure their effect on the latter.
 What is Experimental
Research?
Mostly related to a laboratory test procedure,
experimental research designs involve collecting
quantitative data and performing statistical
analysis on them during research. Therefore,
making it an example of quantitative research
method.
 Types of
Experimental
Research
Designs
A Review
Session
 1 Pre-Experimental Design
Types of
Experimental Quasi-experimental
2
Research Research Design

Designs 3 True Experimental

Research Design
1. Pre-Experimental
either a group or various dependent groups
are observed for the effect of the application
of an independent variable which is presumed
to cause change. It is the simplest form of
experimental research design and is treated
with no control group
1. One-shot Case Study Research Design

Focus: Only one dependent group or variable is

examined.

Timing: Conducted after administering a treatment.

Nature: Posttest study, where the treatment is

presumed to cause a change in the dependent variable.
2. One-group Pretest-posttest Research
Design
Structure: Combines both pretest and posttest
evaluations.

Timing: Pretest is conducted before administering the

treatment, and the posttest is carried out after the
treatment.

Sequence: Initial assessment at the beginning of

treatment (pretest) and subsequent assessment at the
end of treatment (posttest).
 3. Static-group Comparison

Groups: Two or more groups are observed.

Treatment: Only one group receives treatment; others are held

static (do not receive treatment).

Timing: Post-testing is conducted for all groups after the

treatment is administered to the treated group
.
Assumption: Observed differences between groups are
attributed to the treatment administered to the treated group.
 2. Quasi-Experimental
A quasi-experimental design is similar to an
experimental one, but it is not the same. The
difference between the two is the assignment
of a control group. Quasi-research is used in
field settings where random assignment is
either irrelevant or not required.
 3. True Experimental
The true experimental research design relies
on statistical analysis to approve or
disprove a hypothesis. It is the most
accurate type of experimental design and
may be carried out with or without a pretest
on at least 2 randomly assigned dependent
subjects.
 3. True Experimental

The true experimental research design must

contain a control group, a variable that can be
manipulated by the researcher, and the
distribution must be random.
1. The posttest-only Control Group Design
Subject selection: Subjects are randomly chosen and assigned
to two groups (control and experimental).

Treatment: Only the experimental group receives the treatment.

Timing: Post-testing is conducted for both groups after the

treatment is administered to the experimental group.

Conclusion: Conclusions are drawn based on the observed

differences between the control and experimental groups in the
post-test results.
2. The pretest-posttest Control Group
Design
Subject Assignment: Subjects are randomly assigned to two groups
(control and experimental).

Treatment: Only the experimental group is treated.

Observation: Both groups are observed, and both are tested before and
after the treatment.

Measurement of Change: The degree of change in each group is

assessed by comparing the pretest and posttest results.

Conclusion: Conclusions are drawn not only from the post-test

differences between groups but also by considering the degree of
change within each group from pretest to posttest.
 3. Solomon four-group Design

This is the combination of the posttest-only and the pretest-

posttest control groups. In this case, the randomly selected
subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only
method, while the other two are tested using the pretest-
posttest method.
Thank you!
Announcement:
Short Quiz on
Friday
 References
Bhat, A. (2023, November 24). Experimental Research: What it is + Types of
designs. QuestionPro. https://www.questionpro.com/blog/experimental-
research/

Busayo.Longe. (2020, January 23). Experimental Research Designs: Types,

Examples & Methods. Formplus. Retrieved January 6, 2024, from
https://www.formpl.us/blog/experimental-research

Wohlin, C., Höst, M., Runeson, P., & Wesslén, A. (2003). Software reliability. In
Elsevier eBooks (pp. 25–39). https://doi.org/10.1016/b0-12-227410-5/00858-9
 Selecting the
Appropriate
Data Collection
Tool
LESSON 2
 Experiments carried out for the
purpose of collecting data to be
analysed and studied are carried
out in the laboratory, in the field or
using computer numerical models.
LABORATORY
EXPERIMENTS
 STANDARD TESTING
METHODS
commonly used to test materials and systems
to be used in or related to the research or
investigation
EXAMPLES:
ASTM
BSI
APHA
 ASTM
American Society for Testing and Materials

ASTM develops and publishes technical standards for a

wide range of materials, products, systems, and services.
These standards are widely used in various industries to
ensure quality, safety, and performance.
 BSI
British Standards Institution
BSI develops and publishes standards for a wide range of
industries and applications. For example, in the context of
materials and testing, you might find standards related to
the properties, testing methods, and specifications for
various materials.
 APHA
American Public Health Association
The APHA publishes the "Standard Methods for the
Examination of Water and Wastewater," a widely
recognized reference in the field of water quality analysis.
These methods cover various aspects of water quality
testing, including physical, chemical, and microbiological
parameters.
5 GOOD SOFTWARES IN ANALYZING DATA

Excel
SPSS
Jamovi
Python
SAS
EXCEL

Microsoft Excel is a versatile

software extensively used for data
analysis, featuring spreadsheet
functions, graphing tools, and
computational capabilities like
AutoSum. The inclusion of the
Analysis ToolPak further enhances
its analytical capabilities, providing
tools for variance, regression, and
statistical analysis.
SPSS

SPSS (Statistical Package for the

Social Sciences), also known as IBM
SPSS Statistics, is a software
package used for the analysis of
statistical data.
Jamovi

The jamovi project was founded to

develop a free and open statistical
platform which is intuitive to use, and
can provide the latest developments
in statistical methodology. It is used
in statistical research, especially as
a tool for ANOVA (analysis of
variance) and to understand
statistical inference.
Python

Python is used to streamline, model,

visualize, and analyze data using its
built-in data analytics tools. One of
the key features of Python that
appeals to data analytics
professionals is its many libraries,
such as Pandas and Numpy, which
offer a variety of powerful tools for
many analytics needs.
SAS

SAS is a well-known suite of

statistical analysis software
developed by the SAS Institute for
various analytical purposes,
including business intelligence,
advanced analytics, and predictive
analytics.
Analysts use SAS to retrieve, report,
analyze, and visualize data.
Announcement:
Short Quiz 2 on
Wednesday
QUESTIONS?