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Psychology1012 > Week 3 > Flashcards

Week 3 Flashcards

0
Q

External validity

A

Can results generalise to wider population.

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1
Q

Quasi experiment

A

Non random assignment of participants. I.e. Different professions, age etc

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2
Q

Ecological validity

A

Wether the experiment actually mirrors the real life conditions of phenom men being measured.

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3
Q

Measurement error

A

Assumed discrepancy of data collected and true value of measurement

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4
Q

Validity

A

Measuring what we set out to measure. Must be reliable too

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5
Q

Reliability

A

Consistency of results. Does not have to be valid. Must measure in same way each time.

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6
Q

Face validity

A

Weak type of validity. What the test taker thinks of the test. I.e personality test with weird questions.

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7
Q

Content validity

A

Are the items a representative sample of all possible items. Ie test measuring only week 1/2 of 10 weeks.

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8
Q

Criterion-related validity

A

Extent to which a score indicates a level if performance on an criterion against which it is compared. Predictive/concurrent.
I.e. GPa and honours entry

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9
Q

Construct validity

A

Intelligence measurement and how do we know they are measuring intelligence as a construct.

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10
Q

Convergent validity (construct validity)

A

Should correlate with questionnaires that measure
Same construct
Related constructs

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11
Q

Discriminant validity

A

Should not correlate with questionnaires that measure
Different constructs
Unrelated constructs

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12
Q

Experimenter effect (reactivity of measures)

A

What happens when bias of experimenter is known and impacts subjects performance. Hawthorne experiment with lights.

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13
Q

Reliability 3 types

A

Test-retest
Internal consistency
Interrater reliability

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14
Q

Test-retest

A

Measure same individuals at two points in time

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15
Q

Internal consistency

A

Uses responses at only one time and focuses on consistency of items (all measuring same things?)

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16
Q

Interrater reliability

A

Evidence of reliability when multiple taters agree in observations of the same thing

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17
Q

Operationally defined variables

A

Must be defined how going to capture/measure the variable each time. To turn it into numbers…

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18
Q

Levels of measurement

A

Relationship between what is being measured and the numbers that represent what is being measured.

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19
Q

Categorical variable

A

Names distinct entities I.e. Binary (2 groups)

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20
Q

Continuous variables

A

Can take on any value on the measurement scale

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21
Q

4 levels of measurement:

A

Nominal
Ordinal
Interval
Ratio

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22
Q

Nominal variable

A

Two or more things are equivalent in some way are given same number. Numbers have no meaning…just used as labels (categorical)

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23
Q

Linear model

A

Based on a straight line

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24
Q

Variables

A

Measured constructs that vary across entities in the sample

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25
Q

Parameters

A

Estimated from the data usually constants representing fundamental truth about the relations between variables I.e. Mean median

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26
Q

Coefficients (b)

A

Estimate relationship btwn 2 variables

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27
Q

Sum of squares

A

Same process as sum of squares except deviance = outcome - model (asseses fit) total deviance of scores from the mean

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28
Q

Sum of squares as a good measurement

A

Relies on amount of data
More data points
Higher SS

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29
Q

Average error

A

Divide SS (total error) by number of values (N)

30
Q

Mean error

A

Divide not number of scores but the degrees of freedom (df)

31
Q

Degrees of freedom (df)

A

Number of scores used to compute the total adjusted

32
Q

Lack of fit

A

Large values relative to the model

33
Q

Method of least squares

A

Principle of minimising the sum of squares

36
Q

Standard error of the mean (SE)

A

SD of sample means. Acknowledges we can’t take 1000s of samples. SD divided by the square root of the sample size. How well sample fits overall population.

37
Q

Central limit theorem

A

Applied if sample is large (above 30) use this to calculate SE. Sample distribution always normally distributed. The mean of all samples from 1 pop will be the same as the population mean

38
Q

Confidence interval

A

Limits constructed such that for a certain % of samples (95% or 99%) the true values of pop mean will fall within these limits. Boundaries in which we believe true value of mean will fall.

39
Q

Large SE

A

Means a lot of variability between means of different samples - may not give accurate representation of population

40
Q

Small SE

A

Most sample means are close to pop mean - sample likely to be accurate reflection

41
Q

95% confidence interval

A

If you collected 100 samples, calculated the mean and then calculated confidence intervals - 95% of CI would contain true value of mean in the pop.

42
Q

Calculating confidence intervals

A

Need to know what SD and mean are.

Z x SD + mean = X

43
Q

Lower boundary confidence interval

A

Mean - zscore x SE

44
Q

Upper boundary of confidence interval

A

Mean + zscore x SE

45
Q

Sampling variation

A

When the sample means vary from other sample means

46
Q

Sampling distribution

A

Frequency distribution of sample means (graph)

47
Q

Oridinal variable

A

Tell us order that things occur. Nothing about ranking etc. I.e. Horse racing (categorical)

48
Q

Interval variable

A

Shows intervals on the scale I.e iq (continuous)

49
Q

Ratio interval

A

Builds on an interval. How many kids in a family? Starts at 0 (continuous)

50
Q

Continuous variables are:

A

Continuous - any measure on scale

Discreet - certain defined values

51
Q

Descriptive statistics

A

Describe essential characteristics of data. Snapshot

52
Q

Frequency distribution

A

Plots how many times each score occurs

53
Q

Histogram

A

Values of observations plotted horizontal axis. Bars of frequency scores on y axis

54
Q

95% confidence interval

A

Z score is 1.96

55
Q

99% confidence interval

A

Z score 2.58

56
Q

90% confidence interval

A

Z score 1.64

57
Q

t-distribution

A

Calculates confidence interval for small sample sizes (instead of z score). Look up degrees of freedom in t-distribution.
Df - (t-score x SD) = lower boundary
Df + (t-score x SD) = upper boundary

58
Q

Calculating confidence interval

A

% of confidence interval - (z/t score x SE) = lower boundary

% of CI + (z/t score x SE) = upper boundary

59
Q

Null hypothesis

A

Effect is absent

60
Q

Fisher’s p-value

A

(Probability) p = .01 strong evidence to back up a hypothesis

61
Q

Alternative/experimental hypothesis

A

Effect will be present

62
Q

Null hypothesis significance testing NHST

A

Designed to tell wether the alternative hypothesis is likely to be true

  1. Assume null is correct
  2. Fit statistical model to our data represents alternative hypothesis
  3. Use p-value to calculate probability
  4. If criterion less than .05 = model fits data and confidence gained in alternative hypothesis
63
Q

One-tailed test

A

Statistical model that tests a directional hypothesis: the more someone reads this book the more they want to kill its author (predicting direction of data)

64
Q

Two-tailed test - most common

A

Drastically model testing a non-directional hypothesis: reading more of this book could increase or decrease the readers desire to kill its author

65
Q

Type I error

A

We believe genuine effect on population when there isn’t p of this error is .05

66
Q

Type II error

A

We believe there is no effect on population and there is! Max probability of this error occurring .2

67
Q

Experimentwise error rate

A

When a larger number of tests are conducted to measure research question. All type I errors for all tests are added and then taken from 1 to give the % probability that a type I error will occur.

68
Q

Bonferroni correction

A

Calculation done to keep family wise error rate controlled and below .05
Divide type I error % by number of tests (comparisons)

69
Q

Standard deviation

A

How well the mean fits the sample

70
Q

Statistical power linked with…

A

Sample size

71
Q

Effect size

A

Standardised measure of the magnitude of the observed effect I.e. Cohens d, pearsons correlation coefficient r

72
Q

Cohens d

A

Effect size for the comparison of two means. Difference btwn 2 means divided by the pooled SD (if 2 SD use control group SD)

73
Q

Cohen d sizes

A 
.2 = small
.5 = med
.8 = large
74
Q

Test statistic

A

How frequently different values occur. Used to test the hypothesis.

75
Q

Statistical power

A

Ability of a test to detect an effect of a particular size.

Psychology1012 (10 decks)

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