RESEARCH_METHODS.pptx

QUANTITATIVE RESEARCH
CHARACTERISTICS AND TYPES

General Types of Educational Research
• Descriptive — survey, historical, content analysis, qualitative
• Associational — correlational, causal-comparative
• Intervention — experimental, quasi-experimental, action research
(sort of)
• This division is irrespective of the quan/qual divide

QUANTITATIVE RESEARCH
Quantitative research is the collection and analysis
of numerical data to describe, explain, predict,
or control phenomena of interest.

Quantitative Research
This research operates on the philosophical belief or assumption that
We inhabit a relatively stable, uniform, and coherent world
that we can measure, understand, and generalize about.
This view, adopted from the natural
sciences, implies that the world and the laws that
govern it are somewhat predictable and can be
understood by scientific research and examination.
In this quantitative perspective, claims about the
world are not considered meaningful unless they
can be verified through direct observation.

THE QUANTITATIVE PROCESS
At the outset of a study,
quantitative researchers state the hypotheses to be examined
and specify the research procedures
that will be used to carry out the study.
They also maintain control over contextual factors that
may interfere with the data collection and identify
a sample of participants large enough to provide
statistically meaningful data.

Sampling
• The first step in selecting a sample is to define the population to
which one wishes to generalize the results of a study
• The sample is drawn from the population
• -Data is collected from the sample
• -Statistics are used to determine how likely the sample results are
reflective of the population

• A number of different strategies can be used to select a sample.
Each of the strategies has strengths and weaknesses.
• There are times when the research results from the sample cannot
be applied to the population because threats to external validity
exist with the study.
• The most important aspect of sampling is that the sample
represent the population.

SIMPLE RANDOM SAMPLING
• Each subject in the population has an equal chance of being
selected regardless of what other subjects have or will be
selected.
• A random number table or computer program is often employed to
generate a list of random numbers to use.
• A simple procedure is to place the names from the population is a
hat and draw out the number of names one wishes to use for a
sample.

STRATIFIED RANDOM SAMPLING
• – A representative number of subjects from various subgroups is
randomly selected.
• The subgroups are called strata and the sample drawn from each strata
is proportionate to the propsrtions of the strata in the sample
• E.g. if a population has 100 teachers (50 elementary, 30 secondary and 2
tertiary), then in a sample of 10, 5 should be from the elementary
stratum, 3 from secondary stratum and 2 from the tertiary stratum

CLUSTER SAMPLING
In cluster sampling, intact groups, not individuals,
are randomly selected.
Any location within which
we find an intact group of population members
with similar characteristics is a cluster.
Examples
of clusters are classrooms, schools, city blocks, hospitals,
and department stores.

When is it used?
Cluster sampling is done when the researcher is unable to obtain a list
of all members of the population.
It is also convenient when the population is very large or spread over a
wide geographic area.
For example, instead of randomly selecting from all fifth graders in a
large city, you could randomly select fifth-grade classrooms and
include all the students in each classroom. Cluster sampling usually
involves less time and expense and is generally more convenient

An extension of the Cluster Random Sample is the
TWO-STAGE CLUSTERE RANDOM SAMPLE.
In this situation, the clusters (classes in our example) are
randomly selected and then students within those clusters are
randomly selected.

CONVENIENCE SAMPLING
• Subjects are selected because they are easily accessible. This is
one of the weakest sampling procedures.
• An example might be surveying students in one’s class.
• Generalization to a population can seldom be made with this
procedure.

PURPOSIVE SAMPLING
– Subjects are selected because of some characteristic.
Also referred to as judgment sampling and is the process of
selecting a sample that is believed to be representative of a given
population.
Sample selection is based on the researcher’s knowledge and
experience of the group to be sampled using clear criteria to
guide the process.

SYSTEMATIC SAMPLING
• – Systematic sampling is an easier procedure than random sampling when you
have a large population and the names of the targeted population are available.
• Systematic sampling involves selection of every nth (i.e., 5th) subject in the
population to be in the sample.
• Suppose you had a list of 10,000 voters in your area and you wished to sample 400
voters for research
• We divide the number in the population (10,000) by the size of the sample we
wish to use (400) and we get the interval we need to use when selecting subjects
(25).
• In order to select 400 subjects, we need to select every 25th person on the list.

Ethics of Research
Researchers are bound by a code of ethics that includes the following
protections for subjects
• Protected from physical or psychological harm (including loss of dignity,
loss of autonomy, and loss of self-esteem)
• Protection of privacy and confidentiality
• Protection against unjustifiable deception
• The subject must give voluntary informed consent to participate in
research. Guardians must give consent for minors to participate. In
addition to guardian consent, minors over age 7 (the age may vary) must
also give their consent to participate.

Informed Consent
• All research participants must give their permission to be part of a study
and they must be given pertinent information to make an “informed”
consent to participate.
• This means you have provided your research participants with everything
they need to know about the study to make an “informed” decision about
participating in your research. Researchers must obtain a subject’s (and
parents’ if the subject is a minor) permission before interacting with the
subject or if the subject is the focus of the study.
• Generally, this permission is given in writing; however, there are cases
where the research participant’s completion of a task (such as a survey)
constitutes giving informed consent.

INSTRUMENT
• Instrument is the generic term that researchers use for a
measurement device (survey, test, questionnaire, etc.).
• To help distinguish between instrument and instrumentation,
consider that the instrument is the device and instrumentation is
the course of action (the process of developing, testing, and using
the device).

• Instruments fall into two broad categories,
• researcher-completed and subject-completed,
• distinguished by those instruments that researchers administer
versus those that are completed by participants. Researchers
chose which type of instrument, or instruments, to use based on
the research question.

Usability
• Usability refers to the ease with which an instrument can be administered,
interpreted by the participant, and scored/interpreted by the researcher.
• Example usability problems include:
• Students are asked to rate a lesson immediately after class, but there are
only a few minutes before the next class begins (problem with
administration).
• Students are asked to keep self-checklists of their after school activities,
but the directions are complicated and the item descriptions confusing
(problem with interpretation).

VALIDITY
• Validity is the extent to which an instrument measures what it is
supposed to measure and performs as it is designed to perform.
• It is rare, if nearly impossible, that an instrument be 100% valid,
so validity is generally measured in degrees.
• There are numerous statistical tests and measures to assess the
validity of quantitative instruments, which generally involves pilot
testing.

• External validity is the extent to which the results of a study can be
generalized from a sample to a population. Establishing eternal validity
for an instrument, depends directly on sampling.
• An instrument that is externally valid helps obtain population
generalizability, or the degree to which a sample represents the
population.
• Content validity refers to the appropriateness of the content of an
instrument. In other words, do the measures (questions, observation
logs, etc.) accurately assess what you want to know?
• This is particularly important with achievement tests.

RELIABILITY
• A test is reliable to the extent that whatever it measures, it measures it
consistently.
• Does the instrument consistently measure what it is intended to measure?
• There are 4 estimators to gauge reliability:
• Inter-Rater/Observer Reliability: The degree to which different raters/observers
give consistent answers or estimates.
• Test-Retest Reliability: The consistency of a measure evaluated over time.
• Parallel-Forms Reliability: The reliability of two tests constructed the same way,
from the same content.
• Internal Consistency Reliability: The consistency of results across items.

NOW LET’S STUDY DIFFERENT QUANTITATIVE
METHODS

Descriptive Research/Survey Research

Definition:
 Descriptive research involves collecting data to test hypotheses or to answer questions about people’s
opinions on some topic or issue.
 Survey is in an instrument to collect data that describes one or more characteristics of a specific
population.
 Survey data are collected by asking members of population a set of questions via a Questionnaire or
an Interview.
 Descriptive research requires attention to the selection of an adequate sample and an appropriate
instrument.

Rsearch Design
Survey can be
categorized as
Cross-sectional
point in time Collects
data at one
Longitudinal
Collects data at more
than one time to
measure growth or
change

Conducting Descriptive Research
 It requires a collection of standardized quantifiable information from all
members of a population or sample .
• Is a written collection of self report
questions to be answered by
selected group of research
participants.
Questionnaire
• Is an oral ,in-person question and
answer session between a
researcher and individual
respondent.
Interview

Constructing The Questionnaire
 A Questionnaire should be attractive ,brief and easy to respond .
 No item should be included that does not directly relate to the objective
of the study.
 Structured or closed ended should be used.
 Common structured items used in questionnaire are scaled items , ranked
items and checklists.
 In an Unstructured item format respondents have complete freedom of
response but often are difficult to analyze and interpret.

Single concept Clear wording
Terms and meaning should
be defined.
Questionnaire should
begin with general,
Non threatening
questions.
Avoid leading questions
Each question should focus on :

Pilot Testing
 The questionnaire should be tested by respondent who are similar to
those in the sample of the study.
 Pilot testing provides information about deficiencies as well as
suggestions for improvement.
 Omission or unclear or irrelevant items should be revised.
 Pilot testing or review by colleagues can provide a measure of content
validity.

Cover Letter
 Every mailed or Emailed questionnaire must be accompanied by cover
letter that explains What is being asked and why is being asked.
 The cover letter should be brief, neat and addressed to the specific
individual.

Selecting Participants
 Participants should be selected using an appropriate sampling technique.
 The researcher should ensure that the identified participants should have
the desired information and must be willing to share it .

Distributing the Questionnaire
 Questionnaires are usually distributed via one of five
approaches:
mail email telephone
Personal
administration
interview

Tabulating Questionnaire responses
 The simplest way to present the result is to indicate the percentage of
respondents who selected each alternative for each item.
 However analyzing summed items clusters-groups items focused on the
same issues is more useful , meaningful and reliable.
 Comparison can be investigated in the collected data by examining the
responses of different sub-groups in the sample (male/female).

0
2
4
6
Series 1
Series 2
Series 3
Sales
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr

Correlational Research
 Correlational research involves collecting data to determine whether and
to what degree a relation exists between two or more quantitative
variables.
 The degree of relation is expressed as a correlation coefficient.

Purpose
 The purpose of a correlational research may be to determine relations among
variables or to use these relations to make predictions.

Types of correlations
Positive correlation
 Positive correlation between two variables is when an increase in one variable
leads to an increase in the other and a decrease in one leads to a decrease in the
other. For example, the amount of money that a person possesses might correlate
positively with the number of cars he owns.

Negative correlation
 Negative correlation is when an increase in one variable leads to a decrease in
another and vice versa. For example, the level of education might correlate
negatively with crime. This means if by some way the education level is
improved in a country, it can lead to lower crime. Note that this doesn't mean
that a lack of education causes crime. It could be, for example, that both lack of
education and crime have a common reason: poverty.

No correlation
 Two variables are uncorrelated when a change in one doesn't lead
to a change in the other and vice versa. For example, among
millionaires, happiness is found to be uncorrelated to money. This
means an increase in money doesn't lead to happiness

Correlation Coefficient
Measure showing the degree of relation between two variables.

Methods of Studying Correlation
• Scatter Diagram Method
• Karl Pearson’s Coefficient of Correlation
• Spearman rank correlation

Scatter Diagram Method
Scatter Diagram is a graph of observed plotted points where each
points represents the values of X & Y as a coordinate. It portrays
the relationship between these two variables graphically

A perfect positive correlation
Height
Weight
Height
of A
Weight
of A
Height
of B
Weight
of B
A linear
relationship

High Degree of positive correlation
• Positive relationship
Height
Weight
r = +.80

Degree of correlation
• Perfect Negative Correlation
Exam score
TV watching
per
week
r = -1.0

Degree of correlation
• No Correlation (horizontal line)
Height
IQ
r = 0.0

Advantages of Scatter Diagram
•Simple & Non Mathematical method
•Not influenced by the size of extreme item
•First step in investing the relationship between
two variables

Disadvantage of scatter diagram
Can not adopt the an exact degree of
correlation

Karl Pearson’s Coefficient of Correlation
It is also called simple correlation
coefficient.
It measures the nature and strength between two variables
of the quantitative type.

The sign of r denotes the nature of association
while the value of r denotes the strength of association.

If the sign is +ve this means the relation is direct (an increase
in one variable is associated with an increase in the
other variable and a decrease in one variable is associated
with a
decrease in the other variable).
While if the sign is -ve this means an inverse or indirect
relationship (which means an increase in one variable is
associated with a decrease in the other).

The value of r ranges between ( -1) and ( +1)
The value of r denotes the strength of the association as illustrated
by the following diagram.
-1 1
0
-0.25
-0.75 0.75
0.25
strong strong
intermediate intermediate
weak weak
no relation
perfect negative
correlation
perfect positive
correlation
Direct
indirect

Continue…
If r = Zero this means no association or correlation between the two variables.
If 0 < r < 0.25 = weak correlation.
If 0.25 ≤ r < 0.75 = intermediate correlation.
If 0.75 ≤ r < 1 = strong correlation.
If r = l = perfect correlation.
How to compute simple correlation co-efficient

Advantages of Pearson’s Coefficient
•It summarizes in one value, the degree of
correlation & direction of correlation also.

Limitation of Pearson’s Coefficient
•Always assume linear relationship
•Interpreting the value of r is difficult.
•Value of Correlation Coefficient is affected by
the extreme values.
•Time consuming methods

Spearman’s Rank Coefficient of Correlation
• When statistical series in which the variables under study are not capable of
quantitative measurement but can be arranged in serial order, in such situation
pearson’s correlation coefficient can not be used in such case Spearman Rank
correlation can be used.
• R = 1- (6 ∑D2 ) / N (N2 – 1)
• R = Rank correlation coefficient
• D = Difference of rank between paired item in two series.
• N = Total number of observation.

Merits Spearman’s Rank Correlation
• This method is simpler to understand and easier to apply compared to karl
pearson’s correlation method.
• This method is useful where we can give the ranks and not the actual data.
(qualitative term)
• This method is to use where the initial data in the form of ranks.

Limitation Spearman’s Correlation
• Cannot be used for finding out correlation in a grouped frequency
distribution.
• This method should be applied where N exceeds 30.

Where and Why Correlation are used
1.Prediction
• If two variables are known to be related in a systematic way, then it is
possible to use one variables to make accurate prediction about to other.
For Example Carrots cause good eyesight .But sometime the prediction is
not perfectly accurate .For Example College admissions officers can make
a prediction about the potential success of each applicant.

CONTINUE,,,,,,,,,,,,
2.VALIDITY.Suppose that a psychologist develops a new test for
measuring intelligence. One common techniques for demonstrating
Validity is to use a correlation. If the test actually measures of
intelligence then the score on the test should be related to other
measures of intelligence .FOR EXAMPLE standardized IQ Test,
performance on learning tasks and so on…

Continue,,,,
• 3.Reliability. In addition to evaluating the validity of a
measurement procedure, correlations are used to determine
reliability.
• FOR EXAMPLE If your IQ was measured as 113 last week ,you
would expect to obtain nearly the same score if your IQ was
measured again this week.

Continue,,,,
• 4.THEORY VERIFICATION. Many psychological theories make a
specific predictions about the relationship between two variables.
• FOR EXAMPLE. a developmental theory predict a relationship
between the parent’s IQS and the child’s IQS, a social psychologist
may have a theory predicting a relationship between personality
type and behaviours.

Advantages of Correlation studies
• Show the amount (strength) of relationship present
• Can be used to make predictions about the variables under study.
• Can be used in many places, including natural settings, libraries, etc.
• Easier to collect co relational data

DISADVANTAGES
• Can’t assume that a cause-effect relationship exists
• Little or no control (experimental manipulation) of the variables is
possible
• Relationships may be accidental or due to a third, unmeasured
factor common to the 2 variables that are measured

What is it?
Single-subject research usually involves collecting data
on one subject at a time. Single-subject researchers
generally use line graphs to illustrate the effect of their
intervention.

“Single subject research (also known as single case
experiments) is popular in the fields of special
education and counseling.
This research design is useful when the researcher
is attempting to change the behavior of an individual
or a small group of individuals and wishes to
document that change.

Unlike true experiments where the researcher randomly assigns
participants to a control and treatment group, in single subject
research the participant serves as both the control and treatment
group.
The researcher uses line graphs to show the effects of a particular
intervention or treatment. An important factor of single subject
research is that only one variable is changed at a time.
Single subject research designs are “weak when it comes to external
validity….Studies involving single-subject designs that show a
particular treatment to be effective in changing behavior must rely on
replication–across individuals rather than groups–if such results are
be found worthy of generalization” (Fraenkel & Wallen, 2006, p. 318).

Suppose a researcher wished to investigate the effect of praise on reducing disruptive
behavior
Frequency
of
disruptions
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Baseline Praise

behavior over many days.
Frequency
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behavior over many days. First she would need to establish a baseline of how
frequently the disruptions occurred.
Frequency
of
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frequently the disruptions occurred. She would measure how many disruptions
occurred each day
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occurred each day for several days.
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occurred each day for several days. In the example below, the target student was
disruptive seven times on the first day
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disruptive seven times on the first day, six times on the second day
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disruptive seven times on the first day, six times on the second day, and seven times
on the third day.
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Once a baseline of behavior has been established (when a consistent pattern
emerges with at least three data points), the intervention begins.
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emerges with at least three data points), the intervention begins. The researcher
continues to plot the frequency of behavior
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emerges with at least three data points), the intervention begins. The researcher
continues to plot the frequency of behavior while implementing the intervention of
praise.
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of
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In this example, we can see that the frequency of disruptions decreased once praise
began.
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began. The design in this example is known as an A-B design.
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began. The design in this example is known as an A-B design. The baseline period is
referred to as A
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referred to as A and the intervention period is identified as B.
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referred to as A and the intervention period is identified as B.
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A B

Another design is the A-B-A design. An A-B-A design (also known as a reversal
design) involves discontinuing the intervention and returning to a baseline.
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Another design is the A-B-A design. An A-B-A design (also known as a reversal
design) involves discontinuing the intervention and returning to a baseline.
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A B A

Sometimes an individual’s behavior is so severe that the researcher cannot wait
to establish a baseline and must begin with an intervention. In this case, a B-A-B
design is used. The intervention
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design is used. The intervention is followed by a baseline
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design is used. The intervention is followed by a baseline followed by the
intervention.
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design is used. The intervention is followed by a baseline followed by the
intervention.
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B A B

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Ordinate
Condition change line
Data points
Data path
Abscissa
Measure of time
Unit of time
Dependent measure Condition identifications Independent variable
Regardless of the research design, the line graphs used to illustrate the data
contain a set of common elements.

Causal Comparative
Research
In causal comparative research the researcher attempts to
determine the cause or reason, for existing differences in the
behaviour or status of groups or individuals.

pg2
 Definition and purpose:
Like co relational research causal comparative research is
sometimes treated as a type of descriptive research because
it too describe conditions that already exist.
Causal comparative is thus a unique type of research with its
own research procedures.
Also known as “ex post facto” research. (Latin for “after
the fact”).

pg3
 Although causal comparative research produces limited
cause –effect information. It is an important form of
educational research.
 True cause effect relation can be determined only through
experimental research.

pg4
 Example: the participation in pre school education is the
major factor contributing to differences in social adjustment
of first graders. To examine the hypothesis, the researcher
would select the example of first graders who had
participated in the pre school education and the sample of
first graders who had not, then compare the social
adjustment of two groups.

pg5
 If the children who participated in the pre school education
exhibited the higher level of social adjustment .
 Thus the basic causal comparative approach involves starting
with an effect (i.e social adjustment) and seeking possible
causes (i.e did preschool effect it)

pg6
 Design and procedure:
The basic causal comparative involves selecting two groups
differing on some variables of interest and comparing them
on some dependent variable. One group may possess a
characteristic different than the other.
Samples must be representatives of their respective population
and similar with respect to critical variable other then the
grouping variables.

pg7
 Control procedure:
Lack of randomization, manipulation and control all are
sources of weakness in a
causal comparative design.

pg8
 Data analysis and interpretation:
The inferential statistics most commonly used in causal
comparatives studies are the t test which is used to
determine whether the scores of two groups are significantly
different from one another, analysis of variance, used to test
for significant differences among the scores for three or more
groups.
 Interpreting the findings requires considerable caution.

The Three Types
 There are 3 types of causal-comparative research:
 Exploration of Effects
 Exploration of Causes
 Exploration of Consequences

Similarities to correlational research
 Both types of research are examples of associational
research:
 Researchers seek to explore relationships among variables.
 Both attempt to explain phenomena of interest.
 Both seek to identify variables that are worthy of
later exploration
 Often provide guidance for later experimental studies.

Differences
Causal-Comparative
 Typically compare 2 or more
groups of subjects
 Involves at least 1 categorical
variable.
 Analyzes data by comparing
averages or uses crossbreak
tables.
Correlational
 Requires a score on each
variable for each subject.
 Investigate 2 or more
quantitative variables.
 Analyzes data by using
scatterplots and/or correlation
coefficients.

Differences
Causal-comparative
 No manipulation of the
variables.
 Provide weaker evidence for
causation.
 The groups are already formed,
the researcher must find them.
Experimental
 The independent variable is
manipulated.
 Provide stronger evidence for
causation.
 The researcher can sometimes
assign subjects to treatment
groups.

The steps…
 Problem Formulation
 Select the sample of individuals to be studied.
 Instrumentation- achievement tests, questionnaires, interviews, observational
devices, attitudinal measures…there are no limits…

The design
 The basic design is to select a group that has the independent variable and select
another group of subjects that does not have the independent variable.
 The 2 groups are then compared on the dependent variable.

Internal Validity
 Usually 2 weaknesses in the research:
 Lack of randomization
 Inability to manipulate an independent variable
 Threats
 Oftentimes subject bias occurs
 Location
 Instrumentation
 Loss of subjects

Data Analysis
 Construct frequency polygons.
 Means and standard deviations (only if variables are quantitative)
 T-test for differences between means.
 Analysis of covariance

Proceed with caution!!!
 The researcher must remember that demonstrating a relationship between 2
variables (even a very strong relationship) does not “prove” that one variable
actually causes the other to change in a causal-comparative study.

Limitations of Use
 There must be a “pre-existing” independent variable
 Years of study, gender, age, etc.
 There must be active variables- variables which the research
can manipulate
 The length and number of study sessions, instructional techniques,
etc.

Examples
 Exploration of causes of group membership.
 Question: What causes individuals to join a gang?
 Hypothesis: Individuals who are members of gangs have more aggressive personalities
than individuals who are not members of gangs.

References
Fraenkel, J. R., & Wallen, N. E. (2006). How to design and
evaluate research in education (6th ed.). Boston, MA: McGraw
Hill.
Geisler, J. L., Hessler, T., Gardner, R., III, & Lovelace, T. S.
(2009). Differentiated writing interventions for high-achieving
urban African American elementary students. Journal of
Advanced Academics, 20, 214–247.
McKinney, S. (2004). A comparison of urban teacher
characteristics for student interns placed in different
urban school settings. The professional educator, 26(2).
Wasson, B. (2001). Classroom behavior of good and poor
readers. The professional educator, 23(3).
www.mnstate.edu/wasson/ed603/ed603lesson12.htm
www.faculty-staff.ou.edu/B/Nancy.H.Barry-1/cause.html

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