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PR2 QUANTI VS QUALI
Practical Research
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PRACTICAL RESEARCH 2
QUALI VS. QUANTI
QUALI - Researcher relies on the views of the participants.
- interviews and observations
QUANTI - Researcher looks into the pattern of numeric
data.
- focused-group discussions
PURPOSE OF QUALI - understand and interpret social
interactions
PURPOSE OF QUANTI - test hypothesis, look at cause and
effect, make predictions
SAMPLE IN QUALI - small and not randomly selected
SAMPLE IN QUANTI - large and randomly selected
VARIABLES IN QUALI - study of whole, not variables
VARIABLES IN QUANTI - specific variables are studied
DATA IN QUALI - words, images, or objects
DATA IN QUANTI - numbers and statistics
DATA COLLECTED IN QUALI - open-ended responses, field
notes, participant observations, reflections
DATA COLLECTED IN QUANTI - precise measurements using
validated and structured instruments
FINAL REPORT IN QUALI - contextual description and direct
quotations from research
FINAL REPORT IN QUANTI - statistical report with correlations
of means & statistical significance of findings
ANALYSIS IN QUALI - explore, explain, understand; narrative;
mainly inductive reasoning
ANALYSIS IN QUANTI - describe, measure, predict; mainly
deductive reasoning
CHARACTERISTICS OF QUANTITATIVE RESEARCH
1. The data is gathered using structured research instruments.
2. The results are based on larger sample sizes that are
representative of the population.
3. The data is gathered using structured research instruments.
4. The results are based on larger sample sizes that are
representative of the population.
5. All aspects of the study are carefully designed before data
is collected.
6. Data are in the form of numbers and statistics, often
arranged in tables, charts, figures, or other non-textual forms.
7. It can be used to generalize concepts more widely, predict
future results, or investigate causal relationships.
8. Researcher uses tools such as questionnaires or computer
software to collect numerical data.
STRENGTHS OF QUANTITATIVE RESEARCH
1. Allows for a broader study, involving a greater number
of subjects, and enhancing the generalization of the
results.
2. Allows for greater objectivity and accuracy of results.
Quantitative methods are designed to provide
summaries of data that support generalizations about
the phenomenon under study. It usually involves few
variables and many cases and employs prescribed
procedures to ensure validity and reliability.
3. Applying well-established standards means that the
research can be replicated, and then analyzed and
compared with similar studies.
- QUASI-EXPERIMENTAL
- PRE- EXPERIMENTAL
NON-EXPERIMENTAL RESEARCH
- DESCRIPTIVE RESEARCH
- CORRELATIONAL RESEARCH
- SURVEY RESEARCH
An experimental design systematically manipulates one or
more variables in order to evaluate how this manipulation
impacts an outcome/s of interest.
When individuals are randomly assigned to groups, the
procedure is called a true experiment. The investigator uses
control and experimental groups.
Example: Independent Video Learning Tools: Its Effect on
Academic Performance of SHS students
Designs in which a researcher has only partial (or no) control
over randomly assigning participants to levels of a
manipulated variable of interest are called quasi-experiments.
The investigator uses control and experimental groups.
Example: The Effect of Remedial Program to Beginners
With pre-experimental designs, the researcher studies a single
group and implements an intervention during the experiment.
This design does not have a control group to compare with the
experimental group.
In a non-experimental kind of design, the researcher observes
the phenomena as they occur naturally, and no external
variables are introduced. The variables are not manipulated
nor is the setting controlled. Researchers collect data without
making changes or introducing treatments.
Descriptive research describes the characteristics and
components of the population or phenomenon.
Survey Research is used to gather information from groups of
people by selecting and studying samples chosen from a
population. The objective of the study is to see a general
picture of the population under investigation towards a certain
phenomenon.
Investigators use correlational statistics to describe and
measure the degree or association (or relationship) between
two or more variables or sets of scores.
a. Positive Correlation
b. Negative Correlation
c. No Correlation
Example: The Relationship Between Playing Online Games
and the Grade-point Average
PRACTICAL RESEARCH 2
Lesson 2 ■ Importance of Quantitative Research in Teaching Profession Research studies are gaining unprecedented focus and attention. Then, only the faculty members in higher education have so much interest and conduct research and devote time and effort conducting research to improve educational practices that may lead to more quality learning for the students. Researchers also validate, test, and challenge existing educational practices that may lead to reforms that are more contextualized, localized, and responsive to the needs of the learners. ■ Importance of Quantitative Research Across Fields In Education, Quantitative Research can be used in measuring the level of performance of students as well as the teachers. It can also be used to assess the effectiveness of the methods used. The different programs conducted, and the satisfaction of all stakeholders in the educational sector including students, faculty, parents, administrators, the community, the government, and the non-governmental organizations. Through this research method, the interest of these groups can be advanced or enhanced by implementing quantifiable best practices.
■ A changing quantity or measure of any factor, trait, or condition that can exist in differing amounts or types. ■ A logical set of attributes, characteristics, numbers, or quantities that can be measured or counted. Types of Variables ■ Independent variables ■ Dependent variables ■ Control variables ■ Predictor variables ■ Criterion variables ■ Extraneous variables
■ Independent variables ■ are those that cause changes in the subject. ■ independent variable should not be used when writing about nonexperimental designs. ■ Dependent variables ■ are those that bear or manifest the effects caused by the independent variables. Example: The effects of classroom temperature in the academic performance of the students. Independent: classroom temperature Dependent: academic performance of the students
Example: Does behavior modification reduce aggression in autistic children?
Independent: behavior modification Dependent: level of aggression in autistic children ■ Control variables
- The factors or conditions that are kept the same (unchanged) in an experiment.
- do not undergo any changes during an experiment. Example: Does changing the temperature of a ball affect the height the ball will bounce? Control: same ball dropped from the same height dropped onto the same surface. Example: Does the amount of light affect the retention of information? Control: type of information age environment ■ Extraneous variables
- Extraneous variables are any variables that you are not intentionally studying in your experiment or test.
- These are undesirable variables that might influence the outcome of an experiment.
- These variables are to be controlled by the experimenter. But if they do not give in to your control, they become confounding variables that can strongly influence your study.
Criterion: students’ behavior ■ How to control extraneous variables? - Random sampling - Pre-test Levels of measurement of variables 1. Nominal data Used for labelling variables, without any quantitative value. These data are considered as the simplest level. Examples: Gender – male, female Address 2. Ordinal data These are attributes of variables that can be placed as one is higher than the other. Used for ranking purposes. It is the order of the values is what’s important and significant, but the differences between/among each one are not really known. Examples: Likert scales Educational attainment 3. Interval data These are attributes of variables where the distance from one number to the other has meaning. The order is known, and also the exact differences between the values. But no true zero, and no ratio. Examples: Differences in temperature 4. Ratio data
These data tells the order, the exact differences between values, and has true zero. A true zero means absence of the measured variable. Examples: Income Weight Height Age
PR2 QUANTI VS QUALI
Subject: Practical Research
School: Bataan National High School
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