Quantitative Research

Experimental

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

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

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 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

Internal & External Validity

Threats to Internal Validity

alternate explanations

History Maturation(children) Testing Instrumentation Selection bias

Mortality/attrition Hawthorne Placebo

blind vs. double blind Implementation

fidelity

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 StrategiesThreats to Internal 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.

Final Point on Int/Ext Validity

Identify and use a design that…

Controls as many extraneous variable as possible

Will still be practical and feasible to implement

Choosing a Design

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

Examples of Types of Randomization

(Jacobsen, 2012, figure 13-6)

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

Framework for a Cohort Study

(Jacobsen, 2012, figure 12-2)

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

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

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

Line of best fit

http://staff.argyll.epsb.ca/jreed/math9/strand4/scatterPlot.htm

Interpreting a Scatter Plot

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?