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Psychology > RDA Test 2 > Flashcards

RDA Test 2 Flashcards

1
Q

What is a relationship design?

A

Whether variables share a common relationship (i.e. as one thing increases, the other measurement either increase or decreases in response)

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2
Q

What is a difference design?

A

Whether there are differences between measurements depending on how the participants are measured

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3
Q

What is counterbalancing and why is it used?

A

Where the order of tasks/conditions for participants are altered for each participant

Used to deal with participants improving or exhibiting demand effects

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4
Q

What is a floor effect?

A

Where everyone does very poorly, or near the minimum value

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5
Q

What is a ceiling effect?

A

Where everyone does very well, or near the maximum value

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6
Q

What is the Hawthorne Effect?

A

Where participants modify their normal behaviour due to them being aware that they are being observed

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7
Q

What is the Clever Hans Effect?

A

When researchers behave in a way that can influence the participant to act in a way that is desirable for the study

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8
Q

What is social desirability bias?

A

When participants answer in a way that would be viewed more positively in terms of social norms

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9
Q

What is acquiescence bias?

A

Where a participant answering questions might agree or disagree with statements/questions without the answer being a true reflection of their feelings or opinion

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10
Q

What causes acquiescence bias?

A

Participant motivation, the set of options they can choose, all questions being phrased in the same way, etc

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11
Q

What should a sample be representative of?

A

The population of interest

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12
Q

What is a longitudinal study?

A

Where we study a behaviour or subject of interest over a longer period to time and we usually make measurements at regular intervals

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13
Q

What is the main reason why longitudinal studies are important?

A

Allows us to see how things change over time (e.g. an intervention to change a behaviour) within the same group of participants

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14
Q

What is a cross-sectional design study?

A

Where behaviour is observed over time, but instead of following one group of participants, it uses a cross-section of the population of interest and divides them into time groups

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15
Q

What do descriptive statistics allow us to do?

A
  • Explore and compare our data meaningfully
  • Assess any major differences between conditions/variables
  • Determine the ‘shape’ of our data distributions
  • Check for missing data or unusual data (e.g. an outlier)
  • See how much ‘noise’ there is in our data
  • Check to see whether the data are ‘fit’ to use for further statistical testing
  • Look at measures of central tendency, dispersion and variation
  • Organise and aggregate or disaggregate data in a meaningful way
  • Get a ‘feel’ for any relevant patterns
  • Present data graphically or in a tabular format
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16
Q

What do inferential statistics allow us to do?

A
  • Test whether our data is normally distributed
  • Determine whether differences or relationships are statistically meaningful
  • Express whether we can retain or reject the null hypothesis
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17
Q

What are the 3 main measures of central tendency?

A

Mean, median, mode

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18
Q

What is the mean?

A

The average of a set of numbers, whether integers or decimals

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19
Q

How is the mean worked out?

A

Add all items in a set up, and then divide by the number or items

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20
Q

What is the most common measure of central tendency?

A

Mean

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21
Q

What is the median?

A

The middle of a set of values if you arrange them from smallest to largest

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22
Q

When is the median most useful?

A

When you have a non-normal distribution or with extreme scores, or when using ordinal data

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23
Q

What is the mode?

A

The mode is the most commonly occurring number in a set of data

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24
Q

When is the mode most used?

A

With nominal data

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25
Q

What do measures of variability tell us?

A

About the spread of data and in some instances, the amount of ‘noise’ in the data set

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26
Q

What are the 5 common measures of dispersion?

A
  • Range and interquartile range
  • Mean absolute deviation
  • Variance
  • Standard deviation
  • Standard error of the mean (SE)
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27
Q

What is the range?

A

The difference between the smallest and largest value is a distribution

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28
Q

What is the biggest limitation regarding the range? What can be used to remedy this?

A

It is susceptible to an extreme score in a distribution

The interquartile range can be used to remedy this

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29
Q

What is the interquartile range?

A

When we take the centre 50% of values, between the 25th and 75th percentile

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30
Q

How is the interquartile range usually displayed graphically?

A

Box plot

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31
Q

What is the mean absolute deviation?

A

A measure of how much difference or deviation there is from the mean

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32
Q

How is the mean absolute deviation usually worked out?

A

By working out the difference between each value and the mean (ignoring all the negative signs – otherwise they would sum to zero), adding them up, and then dividing by N

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33
Q

What is variance?

A

An indication of the overall amount of variability in a set of data, but in a squared format, is used in many statistical formulae, and is denoted as s2

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34
Q

How is variance worked out?

A

By adding up the squared differences (deviations) between each value in a set of data and the mean, and then dividing by N – 1

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35
Q

What is standard deviation?

A

The square root of the variance

Measures in the original units of measurement and relates to the standard normal distribution, so we can get a much better sense of the distribution of scores in our data

36
Q

What does the standard error of the mean measure?

A

The standard deviation of the population mean

37
Q

What is kurtosis?

A

A measure of the tailedness of a distribution (how “pointy” a distribution is)

38
Q

What is skewness?

A

Degree of asymmetry, can vary in severity, and can be either positive (a positive skewness value), negative (a negative skewness value) or neither (a skewness value of 0)

39
Q

What do scatterplots show?

A

The relationship between variables

40
Q

What are the features of a scatterplot?

A
  • The plot normally has the predictor variable on the x-axis, and the outcome variable on the y-axis
  • We can also include a line of best fit (i.e. the best linear statistical model for the data)
  • This can also include some measure of ‘noise’ around the best fitting model (e.g. standard error of the mean, confidence interval, etc.)
41
Q

What do boxplots look at?

A

The range of data

42
Q

What are the features of a boxplot?

A
  • The middle 50% of data that is not disturbed by the outliers (extreme scores) (indicated by dots outside the whiskers)
  • Any potential outliers that might be causing skew
  • The median value in the distribution (the black bar in the box)
  • The whiskers represent the top 25% and bottom 25% of values
43
Q

How do we tell if a distribution is roughly normal with boxplots?

A

If the box is proportional to the whiskers

44
Q

What do histograms look at?

A

The distribution of data

45
Q

What are quantile-quantile plots (Q-Q plots) used for? And how do they do this?

A

To assess whether the data is normally distributed

They do this by comparing a theoretical distribution of values (for the mean and standard deviation of our data) on the x-axis, with our observed values plotted on the y-axis

If the data is normally distributed, then we expect the data points to form a straight line across the graph

46
Q

What does it mean if something is statistically significant?

A

The probability of an observation occurring is remote enough

47
Q

What is threshold for statistical significance?

A

p ≤ .05

48
Q

Is p = 0.68 significant?

A

No

49
Q

Where is the critical region on a distribution curve for a one-tailed prediction?

A

The last 5% of the tail (the rejected region)

50
Q

What are non-parametric tests?

A

Tests used if the distribution is not normal (e.g. chi-square)

51
Q

What are parametric tests?

A

Tests used if the distribution is normal (e.g. t-test)

52
Q

What does the Shapiro-Wilk test tell us?

A

Whether our distribution of values is significantly different to normal. That is, whether it’s too asymmetrical.

We use this to see if our distribution is normal or not.

53
Q

What does bivariate correlation focus on?

A

The relationships between samples

54
Q

What does correlational analysis allow us to do?

A
  • Explore whether there is a real relationship between variables which is unlikely to have occurred due to external factors (i.e. unlikely to have occurred due to chance)
  • Also allows us to determine the:
    • Direction of the relationship
    • Strength of this relationship
55
Q

How is correlational analysis usually represented visually?

A

Scatterplot

56
Q

What statistical test should be used for this study:
- Relationship study
- Categorical data
- Parametric

A

Pearson’s r

57
Q

What statistical test should be used for this study:
- Relationship study
- Categorical data
- Non-parametric

A

Spearman’s rho

58
Q

What statistical test should be used for this study:
- Difference study
- Within participant design
- Parametric

A

Paired samples t-test

59
Q

What statistical test should be used for this study:
- Difference study
- Within participant design
- Non-parametric

A

Wilcoxon

60
Q

What statistical test should be used for this study:
- Difference study
- Between participant design
- 2 groups
- Parametric

A

Independent samples t-test

61
Q

What statistical test should be used for this study:
- Difference study
- Between participant design
- 2 groups
- Non-parametric

A

Mann-Whitney

62
Q

Correlation does not allow us to say anything about _________.

Why?

A

Causality

There may be a 3rd variable that we are unaware of

63
Q

On a plot, what axis does the predictor variable go on and which axis does the outcome variable go on?

A

Predictor - X-axis
Outcome - Y-axis

64
Q

What is the correlation coefficient calculated using?

A

Covariance

65
Q

What is covariance?

A

The degree to which scores on two variables deviate from their sample means

66
Q

Why do we need to use standardized units?

A

Because two variables will most likely be two different types of measurement (e.g. number of ice-creams sold vs. temperature) and so we need to convert into a common metric

When we standardize our measures we end up with a value that ranges from -1 > + 1

67
Q

How do we work out the z-score?

A

We use the coefficient (r) and divide by the SE (standard error) of r

68
Q

What does the z-score tell us?

A

Whether our r value deviates from zero enough to be in a critical area of the normal distribution

69
Q

What does a value of 1 mean for correlation coefficient?

A

Perfect positive correlation

70
Q

What does a value of 0 mean for correlation coefficient?

A

No correlation

71
Q

What does a value of -1 mean for correlation coefficient?

A

Perfect negative correlation

72
Q

What does a positive correlation mean?

A

As one variable increases, the other variable increases in response

73
Q

What does a negative correlation mean?

A

As one variable increases, the other variable decreases in response

74
Q

On a scatterplot, how can we tell how strong a relationship is?

A

The more the points adhere to a straight line, the stronger the relationship is, the more scattered the points are, the weaker the relationship is

75
Q

What does high correlation mean for shared variance?

A

Lots of shared variance

76
Q

What does low correlation mean for shared variance?

A

Little shared variance

77
Q

What would we expect when one variable deviates from the mean if the 2 variables are related?

A

Similar changes for the other variable

78
Q

How do you calculate shared variance?

A

r x r (x 100 [for %])

79
Q

What does the spearman’s rho test do?

A

Ranks the data from 2 variables

Each value is assigned a rank (e.g. 1st, 2nd 3rd, etc., plus ranks that are tied in rank)

Pearson’s formula for r is then applied to the ranks to calculate the correlation statistic (rs)

80
Q

Why is it better to use pearson’s r in comparison to spearman’s rho?

A

Spearman’s rho is less likely to find significance due to it not being as robust

81
Q

Describe what descriptive statistics can tell us about data

A

Allows us to look at measures of central tendency, and get a feel for any relevant patterns

82
Q

Describe what inferential statistics can tell us about data

A

Whether our data is normally distributed and determines whether difference or relationships are statistically meaningful

83
Q

What does a positive skew look like?

A

Scores fall to the top end of the distribution

84
Q

What does a negative skew look like?

A

Scores fall the bottom end of the distribution

85
Q
A

Psychology (2 decks)

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