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Quantitative Research

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.

ethan-ballard
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Quantitative Research

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  1. Quantitative Research Experimental

  2. Experimental Research • Cause and effect relationships are established by manipulating the INDEPENDENT variable(s) and observing the effect on the DEPENDENTvariable. • 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.

  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 & 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

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

  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. Control StrategiesThreats 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

  8. 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.

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

  10. 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

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

  12. Non-experimental Designs • 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

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

  14. 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?

  15. 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

  16. 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

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

  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?

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