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Research Methods Flashcards

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1
Q

quantitative psychological types of research questions

A
  • 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?
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2
Q

goal of psychological research

A
  • Aim: make INFERENCES about a POPULATION.
  • EG: a study investigating wellbeing in uni students.
    The corresponding population = (ALL uni students)
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3
Q

population

A

everyone of interest to a research question

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4
Q

sample

A

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

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5
Q

why we take samples from a population

A
  • 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
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6
Q

EG: Are psyc students smarter than the general population

A
  • 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
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7
Q

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

A
  • 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
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8
Q

construct

A
  • INTANGIBLE, abstract attributes we THEORISE underlies OBSERVABLE BEHAVIOUR.
    NOT DIRECTLY OBSERVABLE / measured
  • eg happiness, anxiety, intelligence
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9
Q

operational definition (numerical no.)

A

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

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10
Q

EG: intelligence as a construct

A
  • physically intangible
  • operational definition of contruct of intelligence = intelligence score
  • IQ score allows intelligence to be measured INDIRECTLY
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11
Q

Where do research come from?

A

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

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

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12
Q

Research questions

A

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

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13
Q

Hypotheses

A

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

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14
Q

Research Question & Hypothesis

A

Research Question –> Hypothesis
(informs)

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15
Q

Experimental Hypothesis (H1)
(alternative hypothesis)

A

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

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16
Q

Null hypothesis (H0)

A

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

17
Q

Null hypothesis significance testing

A

–> 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.
18
Q

The 2s Rule of Thumb

A
  • 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%
19
Q

Distribution of data

A

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

20
Q

Normal distribution

A

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

21
Q

Typical scores

A

score that occur frequently

22
Q

Extreme scores

A
  • 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
23
Q

Distribution of sample means

A

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
24
Q

Central limit theorem

A
  • 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
25
Q

Standard error

A

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

26
Q

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

A
  1. by comparing one individual score with a distribution of other individuals’ score
  2. using sample mean and distribution sample means
27
Q

alpha level (level of significance)

A
  • 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
28
Q

Single Sample z-test

A
  • 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
29
Q

EG: Are psychology students smarter

A

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

30
Q

Single sample t-test

A
  • 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
31
Q

independent group design

A

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

32
Q

control group

A

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.

33
Q

JASP

A

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

34
Q

repeated measures research design

A

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

35
Q

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

A

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

35
Q

Repeated measures t-test

A

(paired samples, related samples)

  • To assess whether there is a significant difference in participants scores before and after an event.
36
Q

Correlational research design

A

– > 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

37
Q

Test of correlation: Pearson correlation coefficient (r)

A

–> 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
38
Q

Pearson’s (r) in JASP

A

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.

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