Dont understand the question and would like some advice on referencing this question as well.Quantitative Research
Experimental
Experimental Research

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.
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:
exercise regimen
Internal & External Validity

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 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

External Validity: determined by the ability to
generalize the study results beyond the study sample
Threats to Internal Validity
alternate





History
Maturation(children)
Testing
Instrumentation
Selection bias
explanations



Mortality/attrition
Hawthorne
Placebo


blind vs. double blind
Implementation

fidelity
Control Strategies
Threats to Internal Validity

Randomly select participants from a well-defined study
population

Randomly assign selected participants to groups

Include non-treatment control groups in the research
design
Final Point on Int/Ext Validity

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.
Choosing a Design

Identify and use a design that…

Controls as many extraneous variable as possible

Will still be practical and feasible to implement
Experimental Designs



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
Examples of Types of Randomization
(Jacobsen, 2012, figure 13-6)
Non-experimental Designs

Survey research designs


Cross –sectional
Longitudinal

Trend studies –track population changes over time


Cohort study – follow a particular group or subgroup over time


Youth Risk Behavior Survey (YRBS)
http://www.cdc.gov/HealthyYouth/yrbs/pdf/us_injury_trend_yrbs.pdf
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.psare.org/index.asp
Framework for a
Cohort Study
(Jacobsen, 2012, figure 12-2)
Non-experimental Designs

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?
Types of Relationships


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
Assessing correlation

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
Interpreting a Scatter Plot

Line of best fit

http://staff.argyll.epsb.ca/jreed/math9/strand4/scatterPlot.h
tm
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?

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