Correlational Design (kinds of Quantitative Research)
-relationship between variables.
-data is collected by observation since it does not
consider the cause and effect.
Quantitative Research
-close ended
-numerical
-uses scientifically collected and statistically analyzed
data to investigate observable phenomenon.
-phenomenon is any existing or observable fact or
situation that we want to unearth further or understand.
Descriptive Design (kinds of Quantitative Research)
-describe a particular phenomenon by observing it as it
occurs in nature.
-no experimental manipulation, and the researcher does
not start with a hypothesis
-goal of descriptive research is only to describe the
person or object of the study
Ratio (two levels measurements of Numerical Data)
-absolute zero
-type of data is similar to interval
-The zero point in this scale indicates the absence of the
quantity being measured. Examples are age, height,
weight, and distance.
Variables
-words being use to establish title
A Quasi-Experimental Design (kinds of Quantitative
Research)
-random selection and assignment of subject
-is used to establish the cause-and-effect relationship of
variables
Continuous (quantitative variables)
-non whole number
-take fractional (non-whole number) values that can
either be a positive or a negative. Example: height,
temperature.
TWO TYPES OF VARIABLES
1. Independent variables
2. Dependent variables
Quantitative
-present by graphs
8 Characteristics of Quantitative Research
1. Large Sample
2. Objectivity
3. Concise Visual Presentation
4. Faster Data Analysis
5. Generalized Data
6. Fast and Easy Data Collection
7. Reliable Data
8. High Replicability
TRUE OR FALSE
(example questions)
1. Quantitative data can be presented using tables and
graphs.
-TRUE (Quantitative data is numerical, and tables and
graphs are standard tools used to organize and visually
represent this kind of data, making it easier to interpret.)
2. The results of quantitative research can be used to
generalize and predict.
-TRUE (Since quantitative research involves large sample
sizes and statistical analysis, the results can often be
generalized to a larger population and used to make
predictions.)
3. Quantitative research is flexible so at any stage, the
study may change.
-FALSE (Quantitative research is typically structured and
less flexible once the methodology is set.)
4. Quantitative data are more credible, reliable, and
useful than qualitative data.
-FALSE (Both types of data have their value; credibility
depends on the context of the study.)
5. The research study cannot be replicated or repeated
because it is unique in every case.
-FALSE (One of the strengths of quantitative research is
its replicability.)
6. Data are in the form of numbers and analyzed
statistically.
-TRUE (Quantitative research deals with numerical data,
and statistical tools are used to analyze this data to draw
conclusions.)
7. Data analysis is an on-going process. It can be done at
any stage of the process.
-FALSE (In quantitative research, data analysis typically
happens after data collection.)
8. The behavior of the participants is observed and is
critical to the analysis of results.
-FALSE (This is more characteristic of qualitative
research.)
9. Analysis of data is less time-consuming.
-TRUE (Quantitative data can often be analyzed faster,
especially with software.)
10. In quantitative research, the researcher participates
and engages the participants in the study
-FALSE (Quantitative research is usually more detached
and objective, with less direct interaction.)
QUANTITATIVE RESEARCH
• uses scientifically collected and statistically analyzed
data to investigate observable phenomenon.
• phenomenon is any existing or observable fact or
situation that we want to unearth further or understand
KINDS OF QUANTITATIVE RESEARCH
DESCRIPTIVE DESIGN
• is used to describe a particular phenomenon by
observing it as it occurs in nature.
• no experimental manipulation, and the researcher does
not start with a hypothesis.
• goal of descriptive research is only to describe the
person or object of the study.
THE CORRELATIONAL DESIGN
• identifies the relationship between variables.
• data is collected by observation since it does not
consider the cause and effect.
EX POST FACTO DESIGN
• is used to investigate a possible relationship between
previous events and present conditions.
• “Ex post facto” which means after the fact, looks at the
possible causes of an already occurring phenomenon.
A QUASI-EXPERIMENTAL DESIGN
• is used to establish the cause-and-effect relationship of
variables.
• lesser validity due to the absence of random selection
and assignment of subjects.
• independent variable is identified but not manipulated.
The researcher does not modify pre-existing groups of
subjects. The group exposed to treatment (experimental)
is compared to the group unexposed to treatment
(control)
EXPERIMENTAL DESIGN
• like quasi- experimental is used to establish the cause-
and-effect relationship of two or more variables.
• provides a more conclusive result because it uses
random assignment of subjects and experimental
manipulations.
QUANTITATIVE VARIABLES
- also called numerical variables, are the type of variables
used in quantitative research because they are numeric
and can be measured. Under this category are discrete
and continuous variables.
QUANTITATIVE VARIABLES
1. Discrete
-accountable whole numbers.
-It does not take negative values or values between fixed
points.
-Discrete variables take specific, whole number values,
without any fractions or decimals.
-Discrete variables are those that take distinct, separate
values and cannot be subdivided into smaller parts.
Example:
(number of students in a class)- (e.g., 25 students, 30
students).
(group size)
(frequency)
(number of books on shelf)- (You can have 12 or 18
books, but not 12.5 books.)
(number of siblings)- (e.g., 2.5 siblings).
2. Continuous
-non whole number
-values that can either be a positive or a negative.
-Continuous variables are those that can be measured in
infinitely small increments, unlike discrete variables
which have a finite number of values.
Example:
(height)- (eg., 165.3 cm or 170.45 cm)
(temperature)- (eg.,23.6°C or 37.54°C.)
NUMERICAL DATA HAVE TWO LEVELS OF MEASUREMENT
1. Intervals
• Point-to-point
• intervals don’t have a true zero point, meaning zero
doesn’t represent the total absence of the quantity.
• Example: Temperature
2. Ratio
• Zero Value
• Examples are age, height, weight, and distance.
Experimental research provides conclusive result because
of random selection
Quasi experimental- it provides LESSER VALIDITY DUE TO
THE ABSENCE OF RANDOM SELECTIONS AND
ASSIGNMENTS
