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Research Slides

Research Slides. Carolyn R. Fallahi, Ph. D. Defining Important Terms. Hypotheses Null hypothesis Alternative hypothesis ***Goal: to reject the Null hypothesis. Designing a research study. Ask a question…. Can we answer this question via a research study? Operationalizing the hypothesis

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Research Slides

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  1. Research Slides Carolyn R. Fallahi, Ph. D.

  2. Defining Important Terms • Hypotheses • Null hypothesis • Alternative hypothesis • ***Goal: to reject the Null hypothesis

  3. Designing a research study • Ask a question…. Can we answer this question via a research study? • Operationalizing the hypothesis • Stating the independent variables (IV) • Understanding the dependent variables (DV) • Control variables

  4. Different types of research • Case study: Freud • Naturalistic observation • Problems with observation • Natural setting versus laboratory setting • Cross sectional study versus longitudinal study • Survey and Interview Data

  5. Different Types of Research • Descriptive data • Correlational research • Experimental Research • Hypothesis, IV, DV, CV

  6. Research and Publication • Institutional Approval • Informed Consent to Research • Offering Inducements for Research Participation • Deception in Research • Debriefing • Humane Care and Use of Animals in Research • Plagiarism

  7. Correlation • Correlation measures the relationship or association between two variables. • The value of correlation is from -1 to +1. • -1 and +1 represent perfect negative and positive relationships.

  8. Correlation • Examples: +.70 correlation between IQ and SAT scores. • -.70 correlation between severity of Schizophrenic symptoms and level of socialization.

  9. Correlation • Correlation is measured mathematically Example: Schizerall versus Haldol.

  10. Probability • Probability is something that we hear about and use everyday. • There is a 70% chance of rain! • Probability of flipping a coin and getting Heads = 50%. • Probability is measured between 0 and 1. • 0 = for sure the event won’t happen. • 1 = 100% sure that it will happen.

  11. Probability • Probability will be measured with p-values. • Like correlation, I will give you the p-value to interpret. • P < .50 • P < .05 • P < .01 • For purposes of this class, p < .05 or less, will be statistically significantly different.

  12. Probability • For example, if you were looking at a study that involved proportions: • 70/100 patients improved with drug 1 where 20/100 patients improved with placebo. • We would use a z-test.

  13. Probability • In another scenario, 4 different populations. • Men, women, old, young • Chi Square.

  14. Probability • P-value is the probability or the likelihood of the null hypothesis being true. • If p-value is small, say .05, then it is very unlikely that the null hypothesis is true. • If p-value is .15 or high, there is a high probability that the null hypothesis is true. • In this scenario, we accept the null hypothesis and reject the alternative.

  15. Class Example • Drug study – Improve ADHD. • Comparing new drug versus old drug. • We believe the new drug, Adderall, will be significantly better than the old drug, Ritalin. • Please state the Ho and Ha hypotheses.

  16. Class Example • Ho: Adderall = Ritalin • But we don’t believe that, so: • Ha: Adderall will decrease symptoms of Adhd better than will Ritalin.

  17. Class Example • Interpret the two correlations. • Adderall – rho = -.85 • Ritalin – rho = -.60 • We cannot tell just from looking at the correlations which is more effective, therefore, we need p-values. • P< .04.

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