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Statistics & Data Analysis > Review of essential theory > Flashcards

Review of essential theory Flashcards

1
Q

what choice of statistic?

A
  • scale of measurement
  • research aims
  • experimental design
  • properties of dependent/outcome variable
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2
Q

scales of measurement

A
  • nominal
  • ordinal
  • interval
  • ratio
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3
Q

categorical

A

nominal

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4
Q

discrete or continuous data

A
  • ordinal
  • interval
  • ratio
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5
Q

nominal

A
  • numbers or names serve as labels e.g. gender/religion (numbers = allocating numbers for a category)
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6
Q

ordinal

A
  • data organised by ranks
  • values represent true numerical relationship
  • intervals between values may not be equal
    e.g. race position, likert scale
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7
Q

interval

A
  • true numerical relationships and intervals between value are equal
  • no true zero
  • e.g. temperature, shoe size
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8
Q

ratio

A
  • true numerical relationships
  • true zero
  • most accurate
  • height, distance
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9
Q

research aim: decribe

A
  • summarize a set of sample values
  • typically use just two stats : central tendency, spread
  • e.g. average, spread, shape
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10
Q
  • discrete or continuous data
  • normally distributed
A
  • use mean as measure of CT
  • use standard dveiation as measure of spread
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11
Q

discrete or continuous data not normally distributed

A
  • use range as a measure of spread
  • use median as measure of CT
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12
Q

categorical data

A
  • measure of CT: mode
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13
Q

research aim: infer relationships

A
  • relational research explores relationship between observed behaviors or phenomena nothing is actively manipulated
  • can’t infer causality but can describe relationships
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14
Q

research aim: infer differences

A
  • experimental research (influence of IV on DV)
  • can make claims about causality IF we control confounding variables e.g. counterbalance, random allocation
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15
Q

Independent variable

A
  • hypothesized to influence the DV
  • known as factors
    e.g. drug, age group
  • always measured on a categorical scale
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16
Q

dependent variable

A
  • outcome variable
  • hypothesized to be dependent on IV
  • e.g. test,scores, reaction time
  • measured as discrete or continuous
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17
Q

levels of IV’s

A
  • at least 2 levels

TYPES OF LEVELS
- true-experimental actively manipulated e.g. random allocation possible
- quasi-experimental where IV reflects fixed characteristics e.g. right or left handed

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18
Q

between subjects design

A
  • independent groups
  • each participant only in one level
19
Q

within subjects

A
  • repeated measures
  • all subjects take part in all levels
20
Q

mixed designs

A
  • at least 1 IV is between subjects, at least one IV is within

e.g. handedness (between) and game (within)

21
Q

experimental designs: test of differences: 1 IV - 2 levels

A
  • between Ps: independent t-test
  • within Ps: paired t-test
22
Q

experimental designs: test of differences: 1 IV > 2 levels

A
  • between Ps: 1-way independent ANOVA
  • within Ps: 1-way repeated measured ANOVA
23
Q

experimental designs: test of differences: 2 IVs (factorial designs)

A
  • between Ps: 2-way independent ANOVA
  • within Ps: 2-way repeated measures ANOVA
  • mixed: 2 way-mixed ANOVA
24
Q

properties of normally distributed data

A
  • symmetrical about the mean / no skew
  • bell shaped
  • mesokurtic
25
Q

mesokurtic

A
  • positive kurtosis value
  • sharper peak
26
Q

platykurtic

A
  • negative kurtosis value
  • extreme values considered not normally distributed / parametric tests not appropriate
27
Q

leptokurtic

A
  • small s.d.
  • extreme variation cannot be considered normally distributed/ parametric tests not appropriate
  • positive kurtosis value
28
Q

positive skew

A

falls towards the more positive value

29
Q

bimodal data

A

2 modes in the data

30
Q

uniform data

A

all data equal

31
Q

what are statistics for

A

to draw inference, say something about a population

32
Q

z-scores

A
  • scores from a normally distributed population
  • 95% of values lie within +- 1.96 s.d. of mean
33
Q

mean of sampling distribution of the mean

A

equivalent of population mean

34
Q

SDM (sampling distribution of the mean)

A

plot of all possible sample means
- normally distributed
- SDM mean equivalent to population mean
- standard error = s.d. of SDM

35
Q

SE (standard error)

A

s.d. of sampling distribution of the mean

36
Q

what happens to SE as sample size increases

A

SE decreases

37
Q

ESE (estimated standard error)

A

an estimate of the standard error, based on our sample

38
Q

Confidence Intervals

A

interval estimates of population parameters
- typically 95% CIs

39
Q

Calculating confidence intervals for sampling distribution of the mean (when you don’t know populatoin s.d.)(around a sample mean)

A
  • critical value of t where 2.5% of scores are higher/lower
40
Q

null hypothesis (H0)

A

there is no difference between population means
- always assume is true

41
Q

p-value

A

the probability of measuring a difference of that magnitude if the null hypothesis is true

  • between 0-1
42
Q

alpha (a)

A

threshold of probability where we will be willing to reject null hypothesis

~0.05

if p>a reject null

result not from chance alone

43
Q

Tyep I error a

A
  • rejected null
  • null hypothesis true
44
Q

Type II error β

A
  • fail to reject null hypothesis
  • null hypothesis false

Statistics & Data Analysis (4 decks)

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