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Psychology Bsc (Hons) > Research methods and statistics 1 (year one) > Flashcards

Research methods and statistics 1 (year one) Flashcards

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1
Q

What is a hard science?

A

a science that is objective and measurable e.g chemistry

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2
Q

What must a scientifically sound experiment consist of?

A
  1. operational definitions
  2. suitable sample size
  3. control
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3
Q

What is the empirical approach?

A

science, evidence-based

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4
Q

When did Wilhelm Wundt open the first psychology lab?

A

1879

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5
Q

What is introspection?

A

Paying attention to and analysing your own thought processes

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6
Q

What is the order of the scientific method?

A
  • observation-theory-hypothesis-research-research data
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7
Q

What is a theory and what must it consist of?

A
  • general principals for outlining or understanding
  • must include empirical investigation, prediction, explanation
  • must be falsifiable
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8
Q

Who promoted falsification?

A
  • karl popper (1934)
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9
Q

What does a good theory consist of?

A
  • testable hypothesis
  • guiding research and organising empirical evidence
  • be supported or refuted
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10
Q

What is a hypothesis?

A
  • a theory based prediction
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11
Q

To be scientifically testable what must a hypothesis be?

A
  • clearly defined
  • non-circular
  • deal with observable phenomena
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12
Q

What are examples of famous studies that are not scientifically sound?

A

Asch (1951), Zimbardo (1971)

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13
Q

What are some of the methodological flaws of the Stanford prison experiment?

A
  • researcher bias, small sample size, not representative sample, most guards were not violent, worst guard based behaviour on “cool hand luke”
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14
Q

What do we need to infer causation?

A
  • correlation/co-variation
    - time-order relationship (cause has to come before effect)
    - eliminate other possible causes
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15
Q
  • What is the independent groups design?
A

Groups that are made up of different people

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16
Q
  • What do independent groups look at?
A

difference in performance between subjects

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17
Q

What is an independent variable?

A
  • IV = variable we manipulate
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18
Q

What is a dependent variable?

A
  • DV = variable we measure
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19
Q

What are some advantages to independent groups?

A
  • no fatigue or boredom
  • ## no carry over learning
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20
Q

What is a natural groups design

A
  • IV not manipulated as it is already naturally occuring
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21
Q

What does the within groups design measure?

A
  • repeatedly measure the same people on the same DV
  • controls for individual differences
  • ppts may do all conditions at the same time or different times
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22
Q

What does power refer to?

A
  • the probability that you will find a statistically significant difference when it actually exists
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23
Q

What is error variance?

A
  • variation caused by individual differences

- reducing error variance makes a significant result more likely

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24
Q

What are advantages of within-subjects designs?

A
  • individual differences not a problem
  • more powerful
  • fewer ppts
  • more convenient
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25
Q

What are the 4 levels of data?

A
  1. nominal
  2. ordinal
  3. ratio
  4. interval
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26
Q

What is nominal data?

A

names, categories

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27
Q

What is ordinal data?

A

data is ordered (e.g on a likert scale)

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28
Q

What is interval data?

A

data points have a similar interval between them (e.g height)

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29
Q

What is ratio data?

A

same as interval, but x has a zero value

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30
Q

What is qualitative research?

A

no set hypothesis
explores opinions, experiences etc
just from asking people e.g interviews

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31
Q

What is quantitative research?

A

numerical data analysed with maths based methods

via surveys, tasks etc

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32
Q

Give examples for each data level

A 
- Nominal examples
male/ female, smoker/non-smoker
- Ordinal examples
shoe size, position in race, subjective opinion (likert scales)
- Interval examples
voltage, temperature
- Ratio examples (CANNOT go below 0)
Height, weight, test scores
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33
Q

What are descriptive statistics?

A
  • describing data
  • see what data “looks like”
  • looks at central tendancy and measures of dispersion
  • only tells us about our sample, not population
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34
Q

What are inferential statistics?

A
  • use sample to make inferences about the population

- help us reach conclusions beyond our data

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35
Q
  1. What does the statistic you use depend on?

2. What are the two main types of descriptive stats?

A
    • data level
    • distribution of data
    • measures of central tendancy
      - measures of dispersion
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36
Q

What are measures of central tendancy?

A
  • this is how “most” people behave

- measures : mean, median, mode

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37
Q
  1. What is the mode?
  2. What is the median?
  3. How do you calculate the median?
  4. What is the mean?
A
    • most common value or score
      - mostly used with nominal data
    • central value in a data set ordered from lowest to highest
      - mostly used with ordinal level data/ skewed data
    • add up the two scores in the middle and divide by
      2
    • add up all the scores and divide by the number of values
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38
Q
  1. What does a histogram show?
  2. What is normal distribution?
  3. What is skewed data?
A
    • the frequency of the data
    • scores average around the middle, very few extreme scores
      - bell-shaped curve
      - median
    • mean is not central
      - extreme scores affect the mean
      - not normally distribution
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39
Q
  1. How do we get around skewed data?

2. What is the 5% trimmed mean?

A
    • remove extreme results
    • take off 5% of scores from each end

equation = %required x no. scores
100

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40
Q

What is variance and standard deviation?

A
  • average distance of scores from the mean
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41
Q

What are the measures of dispersion?

A
  • range, interquartile range, variance and standard deviation
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42
Q

What is the range?

A
  • difference between largest and smallest score
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43
Q

What is the IQ range?

A
  • difference between middle 50% of scores
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44
Q

How do we calculate the IQ range?

A
  • want to find the range of the middle 50 % of scores (second and third quartile)
  • % required x no. scores
    100
  • answer rounded gives how many scores to take from top and bottom, which you find the range from
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45
Q

What is variance?

A
  • deviation of scores from the meaan
  • subtract the mean from each score, then find the average
  • add up and square all scores, take them away from sum of scores squared divided by n, divided by n-1
  • n = how many values there are
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46
Q

What is the standard deviation?

A
  • square root of the variance
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47
Q

What descriptive statistics should I consider for each data level?

A

Nominal = frequencies/ %/ mode
Ordinal = median (+range)
Interval/ ratio skewed = median (+range)
ND = mean (+/-SD)

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48
Q

What data is chi-square used for and what does it assess for?

A

nominal data level

- association between categorical variables

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49
Q

what is the equation for expected frequencies?

A

(values taken from observed frequencies) (row total)x(column total)

                                                                                     N
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50
Q

what are p values?

A

evaluate how well the data in your sample supports the null hypothesis

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51
Q

what do high and low p values mean?

A

o High p value : data are likely with a true null

o Low p value : data are unlikely with a true null

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52
Q

at what value is the p-value said to be significant?

A

below .05

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53
Q

what is the alpha level?

A

level at which we accept result to be significant

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54
Q

what is the effect size for 2x2?

A

phi

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55
Q

what do different phi values represent?

A
  • Φ .1 = small
  • Φ .3 = medium
  • Φ .5 = large
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56
Q

what are type 1 and type 2 errors?

A
  • Type 1 : rejection of true null hypothesis (false positive)
  • Type 2 : accepting a null hypothesis (false negative)
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57
Q

what is a Bonferroni correction?

A
  • Change the alpha level to prevent type 1 errors

o Divide alpha level by number of tests that will be conducted

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58
Q

what is p hacking and how is it done?

A

: method of manipulating data to achieve significant results
 Multiple analysis
 Omitting other info
 Controlling for variables
 Analyse part way through then gather more data until a significant result is found
 Changing DV

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59
Q

what is the null hypothesis and alternative hypothesis?

A
  • Null hypothesis = statement of no difference
    o True until there is evidence against it
  • Alternative hypothesis = statement of difference or association
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60
Q

What are one-tailed and two-tailed hypothesis?

A
  • One tailed hypotheses = state which direction the effect will be in (e.g those that subscribe to Zoella will be more likely to choose the unhealthy snack
  • Two tailed hypotheses = no direction stated
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61
Q

what do box plots show?

A

show medians, ranges, IQ ranges, skewness etc
 Range = upper adjacent value – lower adjacent value
 Uneven whiskers = skewness

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62
Q

how do you find the IQ range from a box plot?

A

IQ Range = upper hinge – lower hinge (whole box)

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63
Q

What is positive/negative skew?

A
  • Deviation from symmetry
  • Show a big difference between means, medians, and mode
  • Extreme scores affecting the mean
  • Positively skewed : scores greater than the mean skewing
  • Negatively skewed : scores lower than the mean skewing
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64
Q

what is leptokurtic/paltykurtic distribution?

A
  • Refers to extent to which scores cluster at the tails of the distribution – changes pointiness
  • Positive kurtosis : leptokurtic distribution
  • Negative kurtosis : platykurtic
     Flatter than normal
65
Q

what is the boundary for skewness?

A

more than twice the standard error

66
Q

what is indicated when there is no overlap between two confidence intervals?

A

: difference between parameters is significant

67
Q

what are some disadvantages of the between-subjects design?

A
  • Between subjects : independent, looks at performance between subjects/groups
    Disadvantages
     High sample size
     Individual differences
68
Q

what are confounding and situation variables?

A
  • Confounding variable : extraneous variable that influences results
  • Situation variables : variables in condition that could confound, e.g environment, temperature, time of day
69
Q

what are expectancy effects?

A
  • Expectancy effects : expecting an effect can cause that effect e.g expecting a substance rather than a placebo may lead to experiencing some of the effects
70
Q

what are the three types of balancing/matching for between subjects?

A
  • Balancing and matching techniques
    1. Random allocation
    2. Matched group design
    3. Natural group design
71
Q

explain random allocation design?

A

RANDOM ALLOCATION DESIGN

  • Participants randomly assigned to groups
  • Controls for participant variables
  • Sample size should be larger
72
Q

explain matched group design?

A

MATCHED GROUP DESIGN

- Matches participants based on a certain characteristic (sometimes DV)

73
Q

explain the within-subjects design and its disadvantages

A

WITHIN SUBJECTS DESIGN
- Repeatedly measure the same people on the same DV
Disadvantages
o Boredom/ fatigue
o Order/practice effects
o Individual differences
o Time consuming conditions
o Can’t use for experiments where task cannot be repeated (e.g first impressions)
o Can’t be used if there’s differential transfer (effects of one condition affect performance in some conditions (e.g using cannabis then placebo)

74
Q

what is differential transfer?

A

effects of one condition affect performance in some conditions (e.g using cannabis then placebo)

75
Q

state and explain different order/practice effects

A

 Learning
 Fatigue
 Habituation (leads to reduced response)
 Sensitisation (leads to greater response)
 Contrast (may lead to less effort if initially rewarded
 Adaptation (e.g low light levels, drug effects)

76
Q

what is incomplete within-subjects design

A
  • Each condition given to each participant once
  • Order of administration varied
  • Practice effects balanced
77
Q

what are the main counterbalancing methods for incomplete within subjects?

A

o All possible orders

o Selected orders

78
Q

describe all possible orders counterbalancing

A

 Have to calculate the factorial based on levels of IV (result of multiplying number by all numbers less than it)
 Used on 3-4 conditions or less

79
Q

describe selected orders counterbalancing

A

 Based on Latin square
 Used for more than 3 conditions
 Each condition occurs once in each position
 Each condition precedes/ follows each other condition only once

80
Q

what are the main counterbalancing methods for complete within subjects?

A
  • Each condition administers several times (different orders each time)
  • Practice effects balanced for each participant
  • 2 main counter balancing methods
    o Block randomisation
    o The ABBA design
81
Q

describe block randomisation and ABBA design

A
  1. BLOCK RANDOMISATION
     Consists of all conditions
     Participants complete the conditions several times, each time in a different order
  2. ABBA DESIGN
     Presents one random sequence of conditions, then the opposite sequence
82
Q

explain observation without intervention and advantages/diadvantages

A 
o	Naturalistic observation
o	Behaviour occurs naturally, experimenter is a passive recorder
ADVANTAGES
	High external validity
	Can investigate complex social situations
	Useful for developing theories
                      DISADVANTAGES 
	Time consuming/ expensive
	Description, not causation
	Not useful for specific hypotheses
83
Q

explain participant observation and advantages/disadvantages

A 
-	PARTICIPANT OBSERVATION
o	Undisguised
•	Researcher part of group
•	In depth interviews/ observations
	Advantages
	 No ethical problems
	 Natural setting
	Openly record data
	Disadvantages
	Behaviour may change due to presence
o	Disguised
•	Those observed are unaware
•	Prevents observer influence
	Advantages 
	Access to particular groups
	Natural setting
	Disadvantages
	Ethical issues
	Problems recording data
	Researcher bias
	Interaction : researcher may change the observeds behaviour
84
Q

explain structured observations

A
  • Cause an event or set up a situation
  • Observe specific behaviour in a particular setting
  • No attempt to control for other variables
  • Uses behavioural checklist or code using mutually exclusive categories
  • Same procedures across other observers
85
Q

explain field experiments

A
  • Well controlled in natural setting

- Manipulate IV to observe effect on behaviour

86
Q

explain interobserver reliability

A
  • Consistency in measuring between observers

- Correlations can be used to check reliability

87
Q

explain observer influence

A
  • Reactivity : participant modifies behaviour when they know they are being observed
     Socially normative behaviour to gain approval
     Demand characteristics : change behaviour depending on what the expected objective of the research is
  • Controlling reactivity : unobtrusive measurement
     Disguised participant observation
     Adaptation : habituation, desensitisation
     Indirect measurement : physical traces, archival data
  • Expectancy effects with observer bias : knowledge of hypothesis/ previous research
     Can be controlled by blind observers
88
Q

explain observation with intervention

A

 Precipitate an uncommon/ difficult to observe event
 Gain access to closed event/situation
 Establish comparison by adding/manipulating IVs
 Control antecedent events/ behaviour
 Vary qualities of a stimulus event
 3 kinds : participant observation, structured observation, field experiments

89
Q

Define correlational analysis

A
  • Assess relationships between variables
90
Q

define correlation coefficients

A
  • tells us about the strength of the association : range from -1 to +1
  • negative values : negative correlation (-1 = positive)
  • positive value : positive correlation (+1 = positive)
91
Q

define positive and negative correlations

A

positive correlation
- as one variable increases so does the other
negative correlation
- as one variable increases the other decreases

92
Q

what are other explanations for correlations?

A
  • other explanations for correlations: 3rd variable, chance
93
Q

what are the two main inferential tests for correlation data?

A
  • spearman rank correlation
    - ordinal level data
    - skewed ratio/ interval level data
    • pearson product-moment correlation
      - normally distributed interval/ratio level data
94
Q

What data levels are spearman rank correlations used for?

A
  • spearman rank correlation
    - ordinal level data
    - skewed ratio/ interval level data
95
Q

what data levels are pearson product-moment correlations used for?

A
  • pearson product-moment correlation

- normally distributed interval/ratio level data

96
Q

explain how spearman’s correlations are determined

A
  • uses the ranks of the data, not the actual data
  • not influenced by skewed data
  • when we have two values that would get the same rank we add together the ranks and divide by how many tied scores there are
97
Q

describe how you would report results for spearman’s correlation

A
  • shows a positive/negative/no correlation
  • spearman’s : rs
  • degrees of freedom = n-2
  • order = rs (degrees of freedom) = correlation coefficient, p
98
Q

explain one tailed and two tailed tests

A
  • one-tailed : direction is stated
    - we can halve the two-tailed p value to find one-tailed
    - alpha values : .025
99
Q

define alpha values

A
  • alpha value : level at which effect is significant

- typically .05, so p values below .05 are significant

100
Q

define degrees of freedom

A
  • degrees of freedom : the number of observations in the data that are free to vary when estimating parameters
101
Q

what is the symbol for pearsons?

A

-pearson’s symbol = r

102
Q

what are correlation matrices?

A
  • present lots of variables in a table - correlation matrix

- APA format

103
Q

explain surveys

A
  • predetermined questions
  • includes questionnaires and structured interviews
  • can be given online, mail etc
104
Q

give advantages/disadvantages of mail surveys

A
  • mail : + convenient

- response rate/bias

105
Q

give advantages/disadvantages of internet surveys

A 
- internet : + efficient/cheap
                   \+ convenient
                   \+ large/diverse sample
                   - representativeness
                   - ethics
106
Q

give advantages/disadvantages of phone surveys

A
  • phone : + some questions easier to ask
    + large, diverse sample
    - sample bias
    - interviewer bias
107
Q

give advantages/disadvantages of group surveys

A
  • group : + captive audience
    + large amount of data quickly
    - privacy/anonymity
    - pressure
108
Q

give advantages/disadvantages of interview surveys

A
  • interview + same questions/order
    + quantitative analysis
    - interview bias/ social context
109
Q

give advantages/disadvantages of personal surveys

A
  • personally : + convenient/ large sample
    + good response rate
    - representativeness/demand characteristics/ questionnaire fatigue
110
Q

explain psychometric tests

A
  • ability/ aptitude (e.g numerical/verbal reasoning

- personal qualities (personality/attitudes)

111
Q

when were surveys first used?

A
  • 605-1905 : Chinese civil service exams used to recruit officials
112
Q

describe the army alpha and beta tests

A
  • 1917 : army alpha and beta tests developed by Robert yerkes
    - evaluated intellectual/ emotional functioning
    - tested verbal/ numerical ability, (e.g following directions)
    - also tested capability of serving, job classification, leadership potential
    - beta test - non verbal equivalent
    - allowed intelligence classification as superior, average, inferior
    - highest to lowest score : white Americans, north/west European immigrants, south/ east European immigrants, black Americans
  • test was very amercio/eurocentric (e.g what is crisco, celebrities) and required cultural knowledge
    - actually measured level of education / acculturation
113
Q

describe the woodworth personal data sheet

A
  • woodworth personal data sheet
    - world war 1 by US army
    - a test of emotional stability (susceptibility to shell shock)
    - first personality test
114
Q

describe the stanford-binet IQ test

A
  • Stanford-binet IQ test
    - used to assess for learning disabilities
    - used today for clinical/ neurological assessment and educational placement
115
Q

what is the procedure for designing a questionnaire?

A
  • need a topic, then draft, then reexamine/ revise, then do pilot study, then edit and specify procedures for administering
116
Q

what should questionnaire questions be?

A
  • questions must be simple
    - dont use double barralled questions
    - avoid using loaded/ guiding questions
    - avoid negative wording (e.g do you think students shouldnt pay tuition fees
117
Q

describe open ended questions

A
  • open ended questions
    + detailed answers
    +/- quick to design, long analysis time
    + participant led
    - subjective interpretation
  • partially open-ended (multiple answers given
118
Q

describe close ended questions

A
  • closed questions (e.g likert scales, true/false)
    - guessing
    - unsubtle
    - complex to design, quick to mark
    - theory led
    - questionnaire fallacy : people will find a box to tick, even if their opinion is not represented
119
Q

describe rating scales

A
  • e.g yes or no, agree or disagree, graphic rating scale
  • likert scale : labelled statements of a varying strength (e.g strongly agree to strongly disagree
    - each measure given a score (positive question : strongly agree = 5, negatie question : strongly agree = 1
  • semantic differential scale : connotative meaning between bipolar adjectives, and rating is placed on a scale inbetween
120
Q

explain order effects in questionnaire bias

A
  • order effects/ priming : detailed questions at first may influecne later general questions
    - thinking about how the answer to one question while answering another
    - counter balance questions and randomising can help
121
Q

explain demand characteristics in questionnaire bias

A
  • demand characteristics : answer in a certain way to sabotage/ give “beneficial” answers/ look more desirable
122
Q

explain acquiescence in questionnaire bias

A

acquiescent : always agreeing/ disagreeing, even if it contradicts previous answers
- use a mix of positive/negative questions to overcome

123
Q

explain extreme/ neutral responses in questionnaire bias

A
  • extreme/ neutral responses : may not be concentrating, sabotaging etc
    - raw data may need to be disregarded
124
Q

explain cultural bias in questionnaire bias

A

cultural bias : language could be misunderstood, multiple interpretations of words, differing opinions between cultures, social desirability differs

125
Q

explain attitude questionnaires

A
  • assumptions : attitudes can be verbally expressed
  • statements will have the same meaning for all participants
  • attitudes can be quantified
  • problems : consistency, social desirability, ambivalence, normative response bias (use a lie scale)
  • implicity : do the statements express what they should clearly or not?
126
Q

explain word association tests

A
  • used in a clinical setting to determine complexes/ deficiencies used to predict things such as drug use
  • advantages : quick/ easy to administer
    - predict prospective drug use
    - self scoring improves validity
  • disadvantages : colloquialism - cant make standardised procedures
    - tests may not be implicit
127
Q

explain implicit tests

A
  • implicit cognitive tasks : infer attitude/ beliefs from performance on different tasks
  • often use reaction times
  • e.g IAT
    - automatic association between concepts, used for attitudes towards age/gender/ race etc
    - now computer based
    - categorised target concepts with an attitude as quickly as possible
    - faster association = stronger correlation
  • disadvantages : cultural values vs beliefs/ attitudes
    - ecological validity
    - may not act that way
128
Q

explain reliability in psychometric tests

A
  • reliability : internal (all items measure the same thing)
    external (consistent across time and setting)
    - test-retest reliability and split-half reliability
129
Q

explain classical test analysis in psychometric tests

A
  • classical test analysis : assumes observed score (X) is made up of true score (T) and random error score (E) : X=T+E
    - random error : reading errors, social desirability bias, mood, tiredness
    - systematic error : characteristic of test e.g “how often do you go to the cinema” would be influenced by factors like wealth
130
Q

explain validity in psychometric tests

A
  • validity : content validity (covers all behaviour/ aspects)
    construct validity (measures theoretical construct)
    criterion- orientated validity (correlates with establishes measure)
131
Q

explain standardisation in psychometric tests

A
  • standardisation: standardised instructions and procedures
132
Q

explain established population norms in psychometric tests

A

established population norms : should be able to compare results to an appropriate established tests/theories

133
Q

describe the two types of questionnaires

A
  • knowledge based : ability, aptitude, achievement e.g intelligence tests, clinical assessment instruments
  • person based : personality, mood, attitude to assess differences between people
134
Q

describe the two types of response references

A
  • normative reference testing : scores compared to norm e.g mean/ median split
  • criterion reference testing : scores compared to pre-determined criteria e.g determine if someone is at risk
    - restrictive : doesnt take in to account individual differences or non-clinical samples
135
Q

define regression

A
  • focuses on predicting variance in an outcome (criterion or response variable) from predictors (IV)
  • creates a statistical model to find out whether model is a good fit for data and find whether there is a significant association/ direction of association
136
Q

give the linear relationship formula

A
  • linear relationship formula = Y = bX + a
    • Y : criterion/response variable
    • b: slope of the line (based on Pearson’s r)
    • X: predictor variable (years of experience)
    • a: constant or intercept
  • calculates line of best fit for the observed data which can be used to make predictions for unobserved values
137
Q

give the regression equation

A
  • Yi = (B0 + B1Xi) + ei
138
Q

explain bivariate linear regression

A
  • two variables
  • X is predictor variable (IV/explanatory variable)
  • Y is criterion variable (response/outcome/criterion/DV)
139
Q

give the assumptions of regression

A
  • normally distributed continuous outcome
  • independent data
  • interval/ ratio predictors
  • nominal predictors with two categories (dichotomous)
140
Q

define R square

A
  • R square/ adjusted R square
    • how close data is to fitted regression line
    • proportion of variance explained by the model
    • presented as a percentage
    • coefficient of determination
141
Q

define ANOVA

A
  • ANOVA

- measure of model fit : tells us how well regression fits the data

142
Q

define beta coefficient

A
  • beta coefficient

- number of SDs the criterion variable will change as a result of one SD change in the predictor variable

143
Q

what do we need to interpret a regression?

A
  • we need to :
    • assess model fit (f value)
    • know how effective model is - R squared value
    • know whether an association is significant and direction - beta value
144
Q

explain how to report regression data

A
  • Example :
    • a bivariate regression was conducted to investigate the association between years of experience and salary. The regression model predicted approximately 70% of variance in salary, adjusted R^2 = .70, F(1,8) = 22.34, P= .001.
      There was a positive association between years of experience and salary 𝜷 = .86, p = .001
145
Q

Explain why multiple regressions are used

A
  • can increase the amount of variance explained by a model by including additional variables
146
Q

Define correlational analysis

A
  • Assess relationships between variables
147
Q

define correlation coefficients

A
  • tells us about the strength of the association : range from -1 to +1
  • negative values : negative correlation (-1 = positive)
  • positive value : positive correlation (+1 = positive)
148
Q

define positive and negative correlations

A

positive correlation
- as one variable increases so does the other
negative correlation
- as one variable increases the other decreases

149
Q

what are other explanations for correlations?

A
  • other explanations for correlations : 3rd variable, chance
150
Q

what are the two main inferential tests for correlation data?

A

pearson product moment correlations

spearman rank

151
Q

What data levels are spearman rank correlations used for?

A
  • spearman rank correlation
    - ordinal level data
    - skewed ratio/ interval level data
152
Q

what data levels are pearson product-moment correlations used for?

A
  • pearson product-moment correlation

- normally distributed interval/ratio level data

153
Q

explain how spearman’s correlations are determined

A
  • uses the ranks of the data, not the actual data
  • not influenced by skewed data
  • when we have two values that would get the same rank we add together the ranks and divide by how many tied scores there are
154
Q

describe how you would report results for spearman’s correlation

A
  • shows a positive/negative/no correlation
  • spearman’s : rs
  • degrees of freedom = n-2
  • order = rs (degrees of freedom) = correlation coefficient, p
155
Q

explain one tailed and two tailed tests

A
  • one-tailed : direction is stated
    - we can halve the two-tailed p value to find one-tailed
    - alpha values : .025
156
Q

define alpha values

A
  • alpha value : level at which effect is significant

- typically .05, so p values below .05 are significant

157
Q

define degrees of freedom

A
  • degrees of freedom : the number of observations in the data that are free to vary when estimating parameters
158
Q

what is the symbol for pearsons?

A

pearson’s symbol = r

159
Q

what are correlation matrices?

A
  • present lots of variables in a table - correlation matrix

- APA format

Psychology Bsc (Hons) (15 decks)

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