Semester 2 Week 1 - review qualitative psychology Flashcards Preview

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Flashcards in Semester 2 Week 1 - review qualitative psychology Deck (23)
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1
Q

What is a variable?

A

Anything that varies, can always be measured or recorded.

2
Q

What are the four types of variables?

A

Nominal, Ordinal, Interval, and Ratio.

3
Q

What are nominal variables?

A

group membership, no ranking, example - gender. usually reported in frequencies.

4
Q

What is ordinal data?

A

Categories that are being ordered. examples include level of spice, or hours studied per week. usually reported in frequencies.

5
Q

What is interval and ratio data?

A

each point on a scale has an equal distance from each other. If the zero is meaningful (such as age) the data is ratio, if the zero does not indicate anything (such as celsius degrees) it is interval.

6
Q

For what types of data would you calculate the mean, standard deviation, or attempt a t-test?

A

Interval and ratio data.

7
Q

Explain validity and reliability in studies.

A

validity is accuracy, reliability is consistency. reliability is necessary for validity, but a study or measure can be reliable without being valid.

8
Q

Explain the three types of validity at the study level.

A

Internal validity - are the conclusions drawn from the study valid?
External validity - can the results from the study be generalised to the population?
Ecological validity - can the results of the study be generalised to the real world?

9
Q

Explain the three types of validity at the measurement level.

A

Convergent validity - does it measure the construct?
Discriminant validity - does it not measure something else?
Content validity - Does it measure the whole construct?

10
Q

What is internal reliability?

A

Does the item measure the same thing?

11
Q

What is test-retest reliability?

A

Do you find consistent results across time?

12
Q

Explain correlation designs.

A

All variables are measured, end up with correlation data (x is associated with Y). Great in that you can study psychological process under natural circumstances, and easy to get a large sample size. Difficult in that causality cannot be established because of the order of variables and third variable problem.

13
Q

What are the characteristics of an experiment?

A

random assignment (at least 2 conditions), manipulation of IV, and controlling confounds.

14
Q

Explain Quasi-experiments.

A

no random assignment to groups, you can still assign your independent variable randomly.

15
Q

Explain Between vs within-participant design.

A

In between-participant designs, participants are only assigned to one condition, the IV is manipulated between participants. In Within-participant designs, participants are assigned to all conditions, the IV is manipulated within a participant, participants will experience the control and experimental condition.

16
Q

Mean, median, and mode are all types of

A

central tendency

17
Q

What is a z-score and why is it important?

A

Z-score is how your compares to the sample mean expressed in SD. This is important because when we measure something, a raw score is usually not very helpful.

18
Q

What is the null hypothesis?

A

The absence of an effect. Every hypothesis comes with a null hypothesis.
ex
H1: women apologise more than men
H0: there is no difference in apologising between men and women.

19
Q

What does a significance test do?

A

Allow you to 1. Reject H0 and accept H1, or 2. not reject H0.

20
Q

When do you reject/do not reject H0?

A

if p is equal to or less than .05 you cannot reject H0, if p is greater than .05 you can reject the H0 and accept the H1.

21
Q

What is a correlation?

A

AN association between two variables. It expresses how much two variables are related to each other.

22
Q

What are the types of correlation?

A

Positive correlation: when one variable goes up, the other variable goes up.
Negative correlation: when one variable goes up, the other variable goes down.
No correlation: When one variable goes up, this gives us no information about what the other variable does.

23
Q

What are the assumptions for Pearson’s r?

A
  1. association is linear. 2. data are interval or ratio level. 3. no outliers.