Download Quantitative Research Concepts and Strategies

Quantitative Research
Concepts and Strategies
Quantitative research strategies
are driven by two concerns.
Quantitative research strategies
are driven by two concerns.
Quantitative research is interested in the
nature of relationships among variables.
Quantitative research strategies
are driven by two concerns.
Quantitative research is interested in the
nature of relationships among variables.
Quantitative researchers are interested in
whether their discoveries are
generalizable.
Quantitative research is interested
in the nature of relationships
among variables.
Variable A
Variable B
The variables might be
unrelated.
Variable A
Variable B
The variables might be correlated.
Variable A
Variable B
One variable might affect another.
Variable A
Variable B
When one variable affects another,
Variable A
Variable B
When one variable affects another,
they are given specific labels.
When one variable affects another,
they are given specific labels.
Independent
Variable
Dependent
Variable
The term “quantitative” refers to
this research approach because
we wish to quantify these two
concepts:
-The size of the relationships among
variables.
- The probability that the results are
generalizable.
-The size of the relationships among
variables.
- The probability that the results are
generalizable.
-The size of the relationships among
variables.
- The probability that the results are
generalizable.
-The size of the relationships among
variables.
This is quantified using mathematics:
- The probability that the results are
generalizable.
-The size of the relationships among
variables.
This is quantified using mathematics:
The difference in average scores between males
and females on the SAT.
The correlation between scores on an IQ test and
grade point average.
- The probability that the results are
generalizable.
-The size of the relationships among
variables.
This is quantified using mathematics:
The difference in average scores between males
and females on the SAT.
The correlation between scores on an IQ test and
grade point average.
- The probability that the results are
generalizable.
This is quantified using inferential statistics:
-The size of the relationships among
variables.
This is quantified using mathematics:
The difference in average scores between males
and females on the SAT.
The correlation between scores on an IQ test and
grade point average.
- The probability that the results are
generalizable.
This is quantified using inferential statistics:
“There is a statistically significant difference at the
.05 level between males and females on the SAT.”
Inferential statistics procedures
actually provide both quantities of
interest for us- the size of the
relationship and the probability that
the relationship exists in the larger
population the researcher’s sample
is meant to represent.
The particular statistical procedure that is
used depends on two things:
 The number of independent and
dependent variables.
 The level of measurement used for those
variables.
There are four levels of measurement:
There are four levels of measurement:
Nominal
Numerical values are used only as names for different categories.
There are four levels of measurement:
Nominal
Numerical values are used only as names for different categories.
Ordinal
The attributes can be rank-ordered. However, distances between
attributes do not have any meaning.
There are four levels of measurement:
Nominal
Numerical values are used only as names for different categories.
Ordinal
The attributes can be rank-ordered. However, distances between
attributes do not have any meaning.
Interval
The distances between scores have meaning and are treated as equal.
For example, when we measure temperature, the distance from 30-40 is
equal to the distance from 70-80. The interval between values is
interpretable.
There are four levels of measurement:
Nominal
Numerical values are used only as names for different categories.
Ordinal
The attributes can be rank-ordered. However, distances between
attributes do not have any meaning.
Interval
The distances between scores have meaning and are treated as equal.
For example, when we measure temperature, the distance from 30-40 is
equal to the distance from 70-80. The interval between values is
interpretable.
Ratio
There is an absolute zero that is meaningful. In social science research
most "count" variables are ratio, for example, the number of children
eligible for special education services.
There are four levels of measurement:
Nominal
Numerical values are used only as names for different categories.
Ordinal
The attributes can be rank-ordered. However, distances between
attributes do not have any meaning.
Interval
The distances between scores have meaning and are treated as equal.
For example, when we measure temperature, the distance from 30-40 is
equal to the distance from 70-80. The interval between values is
interpretable.
Ratio
There is an absolute zero that is meaningful. In social science research
most "count" variables are ratio, for example, the number of children
eligible for special education services.
Group Designs
Whether you can trust the results of
quantitative research depends on the design
that was used. The use of groups and group
comparisons is a key design element that
supports valid conclusions about the nature
of the relationships among variables and the
generalizability of results.
Group Designs
Whether you can trust the results of
quantitative research depends on the design
that was used. The use of groups and group
comparisons is a key design element that
supports valid conclusions about the nature
of the relationships among variables and the
generalizability of results.
Validity of Quantitative Research Conclusions
Issues of Cause and Effect
Statistical
Conclusion
Validity
Internal
Validity
Issues of Generalizability
Construct
Validity
External
Validity
Statistical
Conclusion
Validity
Internal
Validity
Construct
Validity
External
Validity
Statistical
Conclusion
Validity
Internal
Validity
Construct
Validity
External
Validity
Is there a relationship between A & B?
Statistical
Conclusion
Validity
Is there a relationship between A & B?
Internal
Validity
Is there a cause and effect
relationship between A & B?
Construct
Validity
External
Validity
Statistical
Conclusion
Validity
Is there a relationship between A & B?
Internal
Validity
Is there a cause and effect
relationship between A & B?
Construct
Validity
Is the cause and effect
relationship between A & B?
External
Validity
Statistical
Conclusion
Validity
Is there a relationship between A & B?
Internal
Validity
Is there a cause and effect
relationship between A & B?
Construct
Validity
Is the cause and effect
relationship between A & B?
External
Validity
Is the relationship between
A and B generalizable?
The particular statistical procedure that is
used depends on two things:
 The number of independent and
dependent variables.
 The level of measurement used for those
variables.
The particular statistical procedure that is
used depends on three things:
 The number of independent and
dependent variables.
 The level of measurement used for those
variables.
The particular statistical procedure that is
used depends on three things:
 The number of independent and
dependent variables.
 The number of groups.
 The level of measurement used for those
variables.
For example:
The particular statistical procedure that is
used depends on three things:
 The number of independent and
dependent variables.
 The number of groups.
 The level of measurement used for those
variables.
For example:
If you have 1 independent variable and 1
dependent variable and they are both
measured at the interval level, you look for
a relationship by using a correlation
coefficient.
For example:
If you have 1 independent variable and 1
dependent variable and they are both
measured at the nominal level, you look
for a relationship by using a chi-square.
For example:
If you have 1 independent variable and 1
dependent variable and the independent
variable is at the nominal level and the
dependent variable is at the interval level,
you look for a relationship by using an
independent t test.
For example:
If you have 1 independent variable and 1
dependent variable and the independent
variable is at the nominal level and the
dependent variable is at the interval level,
you look for a relationship by using an
independent t test. But if the independent
variable has more than 2 groups, you use
analysis of variance.
And so on…
What I left out…
The variables must be measured with validity and
reliability.
There are some sampling methods which are
better than others in getting a representative
sample.
Randomly assigning participants to groups solves
a lot of problems.
There are assumptions about how your scores are
distributed which must be true before you can
trust your statistical results.
Quantitative Research
Concepts and Strategies