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: 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 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

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)  X 2 or Y = additional treatments  O = measurement of the dependent variable (an observation)  Each observation or measurement is numbered indicating order (O 1, O 2, O 3 )  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 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 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.asphttp://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 Line of best fit  http://staff.argyll.epsb.ca/jreed/math9/strand4/scatterPlot.h tm http://staff.argyll.epsb.ca/jreed/math9/strand4/scatterPlot.h tm 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?