quantitative research

1. Quantitative Research
Experimental

2.  Cause and effect relationships are established by
manipulating the INDEPENDENT variable(s) and
observing the effect on the DEPENDENT variable.
 Research design must control for the possible effects of
extraneous variables that could mask, enhance, or in
some way alter the effect of the independent variable on
the dependent variable.
Experimental Research

3. Example:
General study description: Recruited
obese participants will spend 3 weeks in
a tightly controlled laboratory setting
Dependent Variable:
Weight Loss
Independent variable: food
intake
Independent variable: food
intake
Independent variable:
exercise regimen
Independent variable:
exercise regimen

4.  Internal Validity: determined by the degree to which
the observed effects of the independent variable (IV) are
REAL and not caused by extraneous factors
 Alternative explanationsAlternative explanations for the effect of the
independent variable (IV) on the dependent variable (DV)
threaten internal validity
 KEY: controlling for the possible effects of extraneous
variables
Internal & External Validity

5.  External Validity: determined by the ability to
generalize the study results beyond the study sample
Internal & External Validity

6. Threats to Internal Validity
alternate explanations
 History
 Maturation(children)
 Testing
 Instrumentation
 Selection bias
 Mortality/attrition
 Hawthorne
 Placebo
 blind vs. double blind
 Implementation
 fidelity

7.  Randomly select participants from a well-defined study
population
 Randomly assign selected participants to groups
 Include non-treatment control groups in the research
design
Control Strategies
Threats to Internal Validity

8.  External validity can not exist without internal validity
 If the results of the study are not internally valid, there is
nothing to generalize.
 Researchers should be always be concerned about
ensuring internal validity first.
Final Point on Int/Ext Validity

9.  Identify and use a design that…
 Controls as many extraneous variable as possible
 Will still be practical and feasible to implement
Choosing a Design

10.  X =independent variable (the treatment)
 X2 or Y = additional treatments
 O = measurement of the dependent variable (an
observation)
 Each observation or measurement is numbered indicating
order (O1, O2, O3 )
 R = random assignment
 Hawthorne effect
Experimental Designs

11. Examples of Types of Randomization
(Jacobsen, 2012, figure 13-6)

12.  Survey research designs
 Cross –sectional
 Longitudinal
 Trend studies –track population changes over time
 Youth Risk Behavior Survey (YRBS)
http://www.cdc.gov/HealthyYouth/yrbs/pdf/us_injury_trend_yrbs.pdf
 Cohort study – follow a particular group or subgroup over time
 National Longitudinal Study of Adolescent Health (Add Health)
http://www.cpc.unc.edu/projects/addhealth/design
 Panel study – examine the same group of people over time at the
individual level
 Panel Study of American Religion and Ethnicity (PS-ARE) http://www.ps-
are.org/index.asp
Non-experimental Designs

13. Framework for a
Cohort Study
(Jacobsen, 2012, figure 12-2)

14.  Correlational study
 Identifies relationships and the degree or closeness of those
relationships
 A correlation exits if, when one variable increases another
variable either increases or decreases in a somewhat
predictable way.
 What is the relationship between participation in intramural
sports and BMI among WOU students?
 What is the relationship between religiosity and age of sexual
initiation in seventh grade students?
Non-experimental Designs

15.  Linear relationships
 Positive: both variables move in the same direction (one
variable increases as the other increases)
 Negative: one variable moves in the opposite direction of the
other (one variable increases while the other decreases)
 Curvilinear relationships
Types of Relationships

16.  Rough measure = scatter plot
 Statistic = correlation coefficient or r (describes a sample
of paired values from two different variables)
 Measures the closeness with which the pairs of values fit a
straight line
 Range of values for r = +1.0 to -1.0
 When r = 0, there is no correlation
 1.0 = perfect correlation
Assessing correlation

17.  Line of best fit
 http://staff.argyll.epsb.ca/jreed/math9/strand4/scatterPlot.
htm
Interpreting a Scatter Plot

18. Relationships cause & effect
 Correlation of ice cream sales and death by drowning (r
= +.86)
 In the months when ice cream sales go up, so do deaths
by drowning and likewise when ice cream sales go down,
so do deaths by drowning
 A.) Does ice cream consumption cause drowning deaths
to increase? or B.) Do drowning deaths cause surviving
family members and friends to eat more ice cream?