Research Methods

Question 1

quantitative psychological types of research questions

Answer 1

• Difference: is one group of people DIFFERENT to another in some way?
• Association: is one construct RELATED to another
• Prediction: does one construct INFLUENCE others?

Question 2

goal of psychological research

Answer 2

• Aim: make INFERENCES about a POPULATION.
• EG: a study investigating wellbeing in uni students.
  The corresponding population = (ALL uni students)

Question 3

population

Answer 3

everyone of interest to a research question

Question 4

sample

Answer 4

a group of people taken from the population to participate in a study

Question 5

why we take samples from a population

Answer 5

• not possible to recruit all people in a population, – SAMPLE is used
• samples used as INFERENCE about the population based on what happens with MEASUREMENT of sample

Question 6

EG: Are psyc students smarter than the general population

Answer 6

• Test IQ for psychology students and general population
• recruit sample for psych students and compare to the ‘population’
• compare mean IQ of psychology students to general population’s IQ (100)
• based on results of this comparison, we will make an INFERENCE about the population of psychology students and whether or not its diff from general population in intelligence

Question 7

EG: Do firefighters diff from the general population in their experience of anxiety?

Answer 7

• Need to know the typical level of anxiety for
• Firefighters
• People who are not firefighters
• Sample for firefighters population VS sample for non firefighters population
• compare MEAN levels of anxiety for both samples.
• based on comparison, INFER the result of sample mean comparison is likely to be same for the populations

Question 8

construct

Answer 8

• INTANGIBLE, abstract attributes we THEORISE underlies OBSERVABLE BEHAVIOUR.
  NOT DIRECTLY OBSERVABLE / measured
• eg happiness, anxiety, intelligence

Question 9

operational definition (numerical no.)

Answer 9

PROCESS of defining and measuring an UNOBSERVABLE construct indirectly (eg: IQ test for intelligence, questionnaire score for anxiety)

Question 10

EG: intelligence as a construct

Answer 10

• physically intangible
• operational definition of contruct of intelligence = intelligence score
• IQ score allows intelligence to be measured INDIRECTLY

Question 11

Where do research come from?

Answer 11

Literature search & Review (reading past researches and thinking what’s the NEXT STEP)

A question of: (TIPO)
- Theories
- Interest
- practical problems
- Observation

Question 12

Research questions

Answer 12

broad ideas that typically ask about either
ASSOCIATION,
DIFFERENCE,
or CAUSATION.

Question 13

Hypotheses

Answer 13

LOGICAL, SPECIFIC, TESTABLE, REFUTABLE and PREDICTIVE statements about what will happen in a psychological research study.

EG: state anxiety is negatively associated w mentalisation capacity
(hypotheses should NEVER predict that nothing will be observed) –framed qustions, not statements

Question 14

Research Question & Hypothesis

Answer 14

Research Question –> Hypothesis
(informs)

Question 15

Experimental Hypothesis (H1)
(alternative hypothesis)

Answer 15

a statement that PREDICTS an EFFECT (eg: difference / association)

Question 16

Null hypothesis (H0)

Answer 16

predicts NO EFFECT (eg: no difference /no association)

Question 17

Null hypothesis significance testing

Answer 17

–> process by which we can determine if our sample data provides for some sort of diff in terms of whatever being measured

1. Propose null hypothesis that a population parameter (mean) has a particular VALUE
2. ASSUME null hypothesis is TRUE
3. Determine PROBABILITY of sample MEAN occurring IF the null hypothesis is true (eg is sample mean typical or extreme?)
   - Involves statistical test based on a distribution of sample means, normal shape, can calculate the standard error
4. Probability of the sample mean LOW (TYPICAL) = REJECT the null hypothesis.
   Probability is HIGH (EXTREME) = do not reject the null hypothesis.

Question 18

The 2s Rule of Thumb

Answer 18

• In normal distribution, 95% of scores fall within approx 2 sd (s) of the mean
• those scores outside 2 sd (s) of the mean = extreme scores
• They are not expected as they occur infrequently in this distribution
  Applying 2s Rule of Thumb
• M=46.87
• S=4.84
• S=4.84 x 2 = 9.68
  Lower limit = m - 2s = 37.19
  Upper limit = m + 2s = 56.55
• More extreme than lower limit: 2.02%
• More extreme than upper limit: 4.04%
• Within 2s of distribution mean: 93.94%

Question 19

Distribution of data

Answer 19

described according to :
central tendency (m) & variability (s)

Question 20

Normal distribution

Answer 20

a bell-shaped curve, describing the spread of a characteristic throughout a population
- Most of the people are in the MIDDLE - peak of graph
- Reduce in frequency towards tails of graph
- SYMMETRICAL distribution

Question 21

Typical scores

Answer 21

score that occur frequently

Question 22

Extreme scores

Answer 22

• unusual to find extreme scores - ones that are VERY LOW or VERY HIGH
• They occur INFREQUENTLY in this distribution
• They indicate a DIFFERENCE to typical scores in terms of whatever is being measured

Question 23

Distribution of sample means

Answer 23

made up of sample means from ALL of the RANDOM SAMPLES of a certain size (n) that could possibly be obtained from a population

• distribution of sample means = theoretical distribution governed by a mathematical theorem –> CENTRAL LIMIT THEOREM

Question 24

Central limit theorem

Answer 24

• distribution of sample means = population mean
• provides us precise characteristics of the distribution of any distribution of sample means
• as sample size increase, the the distribution of sample means of size n, (randomly selected), APPROACHES a NORMAL distribution. (standard error –> 0)
• For large sample sizes (30 or more), the distribution of sample means will have a normal shape

Question 25

Standard error

Answer 25

Standard deviation of the distribution of sample means - s.d: s/square root of n - As sample size INCREASES, standard error DECREASES ---> 0 * Estimation of population mean becomes MORE PRECISE - Larger the sample, the MORE RELIABLE estimate of population mean

Question 26

How can we decide if individual's scores are typical or extreme?

Answer 26

1. by comparing one individual score with a distribution of other individuals' score 2. using sample mean and distribution sample means

Question 27

alpha level (level of significance)

Answer 27

- If sample mean has less than 5% (Alpha level) probability unlikely that H0 is true, if it is more than 5% probability likely that H0 is true - defines which sample means in a distribution of sample means are typical, and which are extreme, if the null hypothesis is true - When comparison distribution is perfectly normal, the critical limits set by the 5% Alpha Level are precisely +/-1.96 standard errors from the mean of the distribution - If our sample mean is inside these limits, the probability is greater than 5% and therefore high. DO not reject the null hypothesis - If our sample mean is outside these limits, the probability is lower than 5% and therefore low. Reject the null hypothesis

Question 28

Single Sample z-test

Answer 28

- involves calculating z score for sample mean - expresses how many standard errors our sample mean is AWAY from H0 - a z-score of z=1.5 indicate: our sample mean is 1.5 standard errors ABOVE the mean of the distribution of sample means - if z score of z=(-)1.5, indicate our sample mean is 1.5 standard errors BELOW the mean of the distribution of sample means Formula: z = (M-μ)/σ z = z-score for sample mean M = our sample mean μ - population mean σ - standard error of the mean Note that the population standard deviation is known - if z-score is < 1.96, do not reject H0 - if z-score is > 1.96, reject H0

Question 29

EG: Are psychology students smarter

Answer 29

σ = 2.19, more extreme than alpha level of 5% (1.96), hence, probability = LOW, so REJECT

Question 30

Single sample t-test

Answer 30

- Used when we dk the population standard deviation - Almost all aspects are SAME as z-test - We still use NHST to assign a value that indicates no effect, we still apply an alpha level of 5%, determine the probability of our mean occurring, result determines whether the null hypothesis is rejected or not - BUT.. alpha level of 5% is NOT fixed - Degrees of freedom (df) - one less than our sample size for a single sample z-test (n-1) - The critical limit varies along with df - If t-score NOT extreme for the degrees of freedom, = do not reject the null hypothesis - If the t-score is extreme for the degrees of freedom, reject the null hypothesis

Question 31

independent group design

Answer 31

appropriate research design for use with an INDEPENDENT sample t-test. - assesses if difference between the two sample means is diff to zero or one sample is < or >than the other

Question 32

control group

Answer 32

Participants in the control group DO NOT receive the treatment / intervention that is being assessed. The control group provides a benchmark to compare the results of the treatment group to.

Question 33

JASP

Answer 33

output gives descriptive, t-score, p value, effect size and more

Question 34

repeated measures research design

Answer 34

(is there a change across time?) T1 - pre intervention Treatment T2 - post intervention

Question 35

What would be the alternative hypothesis for a repeated measures research design using a repeated measures t-test?

Answer 35

The samples will come from DIFFERENT POPULATIONS before and after an event.

Question 35

Repeated measures t-test

Answer 35

(paired samples, related samples) - To assess whether there is a significant difference in participants scores before and after an event.

Question 36

Correlational research design

Answer 36

-- > investigate RELATIONSHIP BETWEEN TWO VARIABLES (eg: age and IQ) Correlation checklist: - examine rs between x and y - each participant provide 2 pieces of data (age and IQ) - no control - examine linear / symmetrical - positive linear ass.; as x increase, y increase - negative linear ass.; as x decrease, y decrease. - correlation CANNOT determination about cause and effect; no IV, DV - Could be a 3rd variable - those who exercise more may engage in another activity that improves their happiness Each participant must provide 2 pieces of data (for each variable eg provide time spent exercising and happiness questionnaire score) Gather evidence about association

Question 37

Test of correlation: Pearson correlation coefficient (r)

Answer 37

--> Pearson's r is. a measure of correlation in a SAMPLE Pearson's R measures the strength of linear rs between x and y - VALUE of r lies between -1 and 1 (closer to 1/-1 means larger effect/stronger, closer to 0 means smaller effect/weaker) - SIZE of r specifies how close the data is to the straight line (ie. how strong is the linear association) - SIGN of r specifies direction of association

Question 38

Pearson's (r) in JASP

Answer 38

Pearson's r procedure in JASP tells us: - STRENGTH of correlation - DIRECTION of correlation - determines if we can INFER from sample to population correlations using standard null hypothesis significant testing (NHST) if there association observed in a sample (r) is ALSO present in the population * if r is large enough, so that it's EXTREME in a distribution of sample correlation coefficient, then we can infer that there IS an association between two variables in a population.