Psych 3510 Chapter 3 Notes
Psych 3510 Chapter 3 Notes Psych 3510
Popular in Introduction to Research Methods in Psychology
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This 6 page Class Notes was uploaded by Gina Goodson on Wednesday September 7, 2016. The Class Notes belongs to Psych 3510 at Georgia State University taught by Dr. Megan Wilson in Fall 2016. Since its upload, it has received 4 views. For similar materials see Introduction to Research Methods in Psychology in Psychology (PSYC) at Georgia State University.
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Date Created: 09/07/16
Psych 3510: Chapter 3 (Textbook) 1. Variables (pg. 56) a. Variable: Something that varies i. Must have two levels (values) ii. Can have more than two levels/values b. Constant: Something that could vary but only has one level 2. Measured and Manipulated Variables a. Measured (Dependent variable/DV) i. Observed and recorded b. Manipulated (Independent variable/IV) (pg. 57) i. Controlled by the researcher 1. Example: Researcher gives one group of participants 10 mg of a medication and another group 20 mg of the same medication. The IV is the medication. c. Some variables cannot be manipulated i. Examples: Gender, IQ, and certain traits such as suicidality d. Some variables cannot be measured because of ethical reasons i. Example: High quality school conditions vs. low quality school conditions in a study concerning long-term effects of elementary education 3. Conceptual Variable > Operational Definition a. Conceptual i. Abstract concepts, i.e. “shyness” and “intelligence” ii. Can be called a construct iii. Conceptual definitions defined at the theoretical level b. Operational definition i. Turn a concept into a measured or manipulated variable c. Refer to Table 3.1 for examples (pg. 58) 4. Does the independent variable, as manipulated, cause a change in the dependent variable, as measured? (Lecture) 5. Claims (pgs. 60-64) a. Claim: The argument someone is trying to make i. Based on empirical research b. Frequency Claims (pg. 60) i. A particular rate or degree of a single variable ii. Variables always measured, not manipulated 1. Examples: a. 1 in 25 U.S. Teens Attempts Suicide b. 44% of Americans Struggle To Stay Happy c. 58% of Boulder Residents Exercise Frequently iii. Look for percentages, number of people, etc. c. Association Claims (pg. 61) i. 1 level of a variable is likely to be associated with a particular level of another variable (2 variables) ii. Correlate: When 1 variable changes, the other one tends to change as well 1. Can be said to be related to one another iii. Look for links, associations, and correlations iv. 3 Types (pg. 62) 1. Positive (+) association/correlation a. High variable goes with high variable, low goes with low i. Example: High levels of shyness = High levels of ability to read facial expressions 1. Shy people are better at reading facial expressions 2. Negative (-) association/correlation a. High goes with low, low goes with high i. Example: High rates of multitasking = Low skill of multitasking 1. People who multitask the most are the worst at it 3. Zero (0) association/correlation a. All/no levels of 1 variable associated with all/no levels of another i. Example: Screen time is not linked to physical activity in kids v. Scatter plots: Variables presented on x- and y-axis; each dot represents one participant (pg. 63) 1. +, -, 0 describe slope of scatter plot a. Slope: Direction of a line that we would draw through the plotted data i. + Slope line inclines upward ii. - Slope line declines downward iii. 0 No slope, so a horizontal line would be present d. Causal Claims (pg. 64) i. 1 variable is responsible for changing the other (2 variables) ii. 1 manipulated variable, 1 measured variable iii. Based on association claims, very similar (include 3 types of association claims, too) 1. However, causal claims use more aggressive terms a. Example: Eating 500 calories a day causes weight loss iv. Look for cause, change, makes, affects, harms, etc. e. Refer to Table 3.2 for examples of each claim (pg. 60) f. Refer to Table 3.3 for association claim terms vs. causal claim terms (pg. 65) 6. Validities (pg. 66-73) a. Validity: The appropriateness of a conclusion or decision i. Valid claim is: 1. Reasonable 2. Accurate 3. Justifiable 7. Validities of Frequency Claims (pgs. 67-68) a. Construct validity: How well a conceptual variable operationalized i. How well the researchers measured the variables b. External validity: How well the results of a study generalize people or contexts besides those in the study i. Generalizability: How the researchers chose the study’s participants and how well those participants represent the intended population c. Statistical validity: The extent to which a study’s statistical conclusions are accurate and reasonable i. Also called statistical conclusion ii. How well the numbers support the claim iii. Percentages > Margin of error estimate (indicates where the true value in the population probably lies) 8. Validities of Association Claims a. Construct validity: Assess the construct of each variable i. Example: How well the researchers measured the frequency of multitasking and how well they measured the ability to multitask b. External validity: Whether the association claim can generalize other populations c. Statistical validity i. Strength and Significance 1. How strong is the association? 2. Statistically significant association ≠ “Chance connections” in that sample ii. Avoiding Two Mistaken Conclusions 1. Type I error: “False positive”; an association is made between 2 variables when there is no association in the real population 2. Type II error: “Miss”; there is no association when there really is one in the full population 9. Validities of Causal Claims a. Three criteria i. Covariance: Variables must be related ii. Temporal precedence: One variable must come first in time before the other variable iii. Internal validity/Third-variable: A study should be able to eliminate alternative explanations for the associations b. Experiments Can Support Causal Claims i. Experiment: 1 independent variable and 1 dependent variable ii. Temporal precedence and internal validity 1. Independent variable comes first 2. When variable is manipulated, researchers have the potential to control confounds iii. Random assignment (pg. 74) c. Other Validities i. Construct validity 1. Operationalizing manipulated variables need a specific task or situation that will represent each level of the variable ii. External validity 1. Generalization iii. Statistical validity d. Prioritizing Validities (pg. 77) i. It is impossible to satisfy all 4 validities in one study 1. Depends on the researcher’s goals 2. Example: External validity is not always possible to achieve and sometimes is not relevant
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