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Research methods exam 2

by: Gina Castellano

Research methods exam 2 Psy 3100

Gina Castellano
GPA 3.4

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This study guide contains the terms and their definitions from the guide that Dr. Smith put online.
Research Methods in PSychology
Dr. Smith
Study Guide
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This 3 page Study Guide was uploaded by Gina Castellano on Monday March 28, 2016. The Study Guide belongs to Psy 3100 at Appalachian State University taught by Dr. Smith in Spring 2016. Since its upload, it has received 11 views. For similar materials see Research Methods in PSychology in Psychlogy at Appalachian State University.


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Date Created: 03/28/16
Exam 2 Study Guide  Hypothesis testing: generating a hypothesis, then collecting data to test the hypothesis (actually testing the null hypothesis) o Steps to take  Assume null hypothesis  Collect data  Calculate probability of getting results if the null hypothesis was true (p-value)  Decide whether to reject null hypothesis or fail to reject null hypothesis  Null hypothesis: assumption that there is no relationship between variables  Statistical significance: when the p-value is less than or equal to .05, we reject the null hypothesis o The difference is probably not due to chance o The effect that is found in the sample probably exists in the general population o Not statistically significant: when the p-value is greater than .05, we fail to reject the null hypothesis  P-value: the likelihood we would observe the results if the null hypothesis was true o Ex: a study that compared test and restudy groups was founded to have p = .01  So if the null is true, we would see a difference 1% of the time  Basically, the null is probably not true  T-value:  D-value (Cohen’s d): is the effect size measure for differences between two groups (how large is the effect?) o Allows people to see how much of an effect a factor might have o Allows people to make comparisons across different studies o Not sensitive to sample size either o Measure of effect size  Strong/large: +/- .80  Moderate/medium: +/- .50  Weak/small: +/- .20  R-value (correlation coefficient): Pearson’s r indicates strength and direction of relationship between two variables o Positive values indicate positive relationships o Negative values indicate negative relationships (as one variable increases, the other decreases) o Measure of effect size  Strong/ large: +/- .50  Moderate/medium: +/- .30  Weak/small: +/- .10  Type 1 error: we reject the null hypothesis when it is actually true o We say there is a difference between two variables when there really isn’t one  Type 2 error: we fail to reject the null hypothesis when the null is actually false o We say there is not a difference between restudy and test groups when there really is one  Statistical power: it is related to effect size which is the magnitude of the relationship between variables as well as being related to the sample size. The larger the effect size and larger the sample size, they both result in more power  Effect size: magnitude of the relationship between variables. If effect size is small, larger samples are necessary  Sample size: number of people in study. Larger sample sizes are good, however, they are not necessary if the effect size is large.  Reject the null hypothesis: when p-value < .05. However, this does not mean the null hypothesis is false, only that it should be rejected based on the data collected.  Fail to reject the null hypothesis: when p-value > .05. However, this does not necessarily mean the null hypothesis is true, only that it cannot be rejected on the basis of the data collected.  Experimental designs: Involves manipulating the variable and is followed by a measurement of another variable. It can draw cause/effect relationships.  Conditions (levels) of an IV: groups that are created by the IV. When you are conducting a study, the independent variable will most likely have two levels. EX: One with and one without.  Experimental group: the group that receives some sort of “treatment”  Control group: this is the group that does not receive anything  Comparison group: all experiments do not need to have a control condition; however, all experiments need a comparison condition.  Between subjects design: (independent-groups design) each participant is assigned to only one level of the IV  Within-subjects design: participants are assigned to all levels of the IV. Be aware of order effects  Random assignment: helps internal validity; randomly choose people to put in groups  Random sampling: helps with external validity; when randomly selected from population for study  Matched pairs assignment: participants are measured and equated on some variable before the experiment begins o Matched on a variable that could influence results  Order effects: the order people go through the conditions might influence their results  Carryover: the effects of one level of the manipulation are still present when in another level (practice, fatigue)  Counterbalancing: arranging the order of conditions so each condition occurs equally often in each position  Covariance: was there a difference? Compared to what?  Temporal procedure: was the IV manipulated before the DV? Usually not a problem with experiments, but worth keeping in mind  Internal validity: most important* are there selection effects? o Different types of participants in the different conditions o Random assignment prevents this  Confounds: vary systematically with the IV; random assignment does not help with confounds; they are study design problems  Extraneous variables: vary randomly (unsystematic); examples: mood, attention, motivation, knowledge of participants; random assignment helps with this  Evaluating statistical validity: is the difference statistically significant? T-tests  Experimenter bias: could influence teaching (internal validity) and grading (construct validity)  Selection effects: i.e. - students picked the class and that determines twitter vs. no twitter  Correlational study: two continuous variables are measured; cannot draw causal conclusions from correlations due to it being non-experimental study Relationship Cohen’s d Pearson’s r Strong/larger .80 .50 Moderate/medium .50 .30 Weak/small .20 .10


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