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 Stating the independent variables (IV) Understanding the dependent variables (DV) Control variables

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

Different Types of Research  Descriptive data  Correlational research  Experimental Research Hypothesis, IV, DV, CV

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

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.

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

Correlation  Correlation is measured mathematically Example: Schizerall versus Haldol.

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.

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.

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.

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

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.

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.

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

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.