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Examen

Social Psy (PSY 3304) Exam 1 TTU Core, Tammy Questions & 100% Correct Answers

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Replicable ~~> when others can repeat it and get the same results falsifiable ~~> must be stated in a way that makes it possible to reject it (can it be disproved?) precise ~~> operational definitions to define the variable ( happiness= smiles per hour) parsimonious ~~> apply the simplest explanation possible to any set of observations Basic research ~~> research for the sake of gaining knowledge (why are people prejudiced?) applied research 2 | P a g e | © copyright 2024/2025 | Grade A+ Master01 | September, 2024/2025 | Latest update ~~> research for the sake of addressing real world problems (how can we decrease prejudice?) True or false: Basic research leads to applied research ~~> true Correlational design ~~> MEASURE (record) two (or +) variables to see if they cooccur/move together. If you know x, you can predict y. positive correlation ~~> x increases, y increases negative correlation ~~> x increases, y decreases strong correlation ~~> (correlational designmagnitude) easier to predict y from x (Sat score and math class grade) perfect correlation 3 | P a g e | © copyright 2024/2025 | Grade A+ Master01 | September, 2024/2025 | Latest update ~~> (correlational design- magnitude) x and y are the same ( SAT and ACT score)r=1 weak correlation ~~> (correlational design- magnitude) x predicts y but not strongly. harder to predict y from x (SAT scores and college art class grade) r closer to 0 No correlation ~~> (correlational design- magnitude) x and y do not "move" together; knowing x does not help you predict y (SAT scores and what car you drive) r=0 Correlations ~~> DOESN'T tell us why x causes y. Only tells us that two variables move together. Good starting point for figuring out problems. Experimental design (Experimental) ~~> one or more manipulated variables and one or more measured variables. Allows us to conclude whether x causes y. 4 | P a g e | © copyright 2024/2025 | Grade A+ Master01 | September, 2024/2025 | Latest update Internal Validity ~~> within the study, was the independent variable and independent variable alone responsible for the effect on our Dependent Variable? Threats to internal validity ~~> Counfound variables: 3rd variables that can account for our results. When something happens in one group but not the other (other than the difference in Independent variable)

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PSY 3304
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Institución
PSY 3304
Grado
PSY 3304

Información del documento

Subido en
13 de septiembre de 2024
Número de páginas
16
Escrito en
2024/2025
Tipo
Examen
Contiene
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1 | P a g e | © copyright 2024/2025 | Grade A+




Social Psy (PSY 3304) Exam 1 TTU Core,
Tammy Questions & 100% Correct Answers
Replicable

✓ ~~> when others can repeat it and get the same results




falsifiable

✓ ~~> must be stated in a way that makes it possible to reject it (can it

be disproved?)




precise

✓ ~~> operational definitions to define the variable ( happiness= smiles

per hour)




parsimonious

✓ ~~> apply the simplest explanation possible to any set of observations




Basic research

✓ ~~> research for the sake of gaining knowledge (why are people

prejudiced?)




applied research

Master01 | September, 2024/2025 | Latest update

, 2 | P a g e | © copyright 2024/2025 | Grade A+



✓ ~~> research for the sake of addressing real world problems (how can

we decrease prejudice?)




True or false: Basic research leads to applied research

✓ ~~> true




Correlational design

✓ ~~> MEASURE (record) two (or +) variables to see if they co-

occur/move together. If you know x, you can predict y.




positive correlation

✓ ~~> x increases, y increases




negative correlation

✓ ~~> x increases, y decreases




strong correlation

✓ ~~> (correlational design-

magnitude) easier to predict y from x (Sat score and math class grade)




perfect correlation


Master01 | September, 2024/2025 | Latest update

, 3 | P a g e | © copyright 2024/2025 | Grade A+



✓ ~~> (correlational design- magnitude) x and y are the same ( SAT and

ACT score)r=1




weak correlation

✓ ~~> (correlational design- magnitude) x predicts y but not strongly.

harder to predict y from x (SAT scores and college art class grade) r

closer to 0




No correlation

✓ ~~> (correlational design- magnitude) x and y do not "move"

together; knowing x does not help you predict y (SAT scores and what

car you drive) r=0




Correlations

✓ ~~> DOESN'T tell us why x causes y. Only tells us that two variables

move together. Good starting point for figuring out problems.




Experimental design (Experimental)

✓ ~~> one or more manipulated variables and one or more measured

variables. Allows us to conclude whether x causes y.




Master01 | September, 2024/2025 | Latest update
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