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Evidence Based Approach 2 > Lecture Week 2 > Flashcards

Lecture Week 2 Flashcards

1
Q

Eysenck Personality Inventory

A
  • Developed by Hans Eysenck
  • Known as EPI
  • Self-report instrument designed to measure two central dimensions of personality
  • Extraversion and Neuroticism
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2
Q

Neuroticism in Psychology

A
  • Not to be confused with neurosis
  • In this context refers to personal stability
  • A person high on neuroticism scale would be considered unstable
  • A person low on euroticism scale would be considered stable
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3
Q

Labile

A
  • A person whose mood fluctuates up and down
  • Is considered unstable
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4
Q

EPI L

A
  • Eysenck’s Lie Scale
  • Questions in EPI designed to eliminate subjects who answer dishoneslty or randomly
  • A high score here creates doubt and questions this participants reliability when answering the other questions
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5
Q

GHQ

A
  • General health Questionaire
  • General measure of psychological and psycho-social wellbeing.
  • High score points towards a problematic state
  • Asks questions about your sleep quality, anxiety depression and psychosomatic conditions
  • Is very general hence the phrase general health questionnaire
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6
Q

Balanf

A
  • Test that measures tough mindedness or authoritarianism
  • Not to be confused with conservatism
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7
Q

Beck Depression Inventory

A
  • Famous tool used to measure depression
  • Used in clinincal and research settings
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8
Q

Marlowe Crowne Social Desirability Scale

A
  • 33-item self-report questionnaire
  • Assesses whether or not respondents are concerned with social approval.
  • or Answering in a way that might seem to be “the right way”
  • Seeking to not answer truthfully, but in some sort of a fixed way to achieve a goal, usually socially acceptable answers
  • Created by Douglas P. Crowne and David Marlowe in 1960
  • Measures social desirability bias
  • Known as MC–SDS
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9
Q

Spielberger State & Trait Anxiety Inventory

A
  • (STAI) is a commonly used measure of trait and state anxiety
  • Developed by Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983
  • Used in clinical settings to diagnose anxiety and to distinguish it from depressive syndromes
  • Often used in research as an indicator of distress
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10
Q

Difference between State & Trait Anxiety

A
  • State Anxiety looks at the level of anxiety at that time
  • Trait Anxiety refers to your level of anxiety in general
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11
Q
  • wakewd
  • wakewe
  • sleepwe
  • sleepwe
A
  • Variables that reported people’s sleep and awake cycles
  • Average time they:
    • woke up - weekday
    • woke up - weekend
    • went to sleep - weekday
    • went to sleep - weekend
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12
Q

Checking for Data Entry Errors

A

It is important to check that our data is error free otherwise we get really crazy results

For nominal variables use the Frequencies function in Analyze Tab

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13
Q

Levels of Measurement

A
  1. Nominal
    • Also called categorical e.g. Gender would be Male/Female/Non Binary
  2. Ordinal
  3. Interval
  4. Ratio
  • Every peice of data can be classified into one of these categories
  • SPSS categorises Interval and Ratio together and calls it Scale
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14
Q

Measure in SPSS

A
  • Found in the variable tab
  • reports Nominal, Variable and Scale
  • Scale refers to both Interval and Ratio levels of measurement together.
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15
Q

Using Frequencies Command in SPSS

A
  • Checks for data entry errors in a nominal variable such as gender
  • Analyze/Descriptive Statistics/Frequencies/Choose variable
  • Analysis appears in SPSS Output Window
    *
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16
Q

“Show Labels” Button

A

Toggles between nominal value and label of value

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17
Q

Checking for errors in a Scale Variable

A
  • Use Explore Function in SPSS
  • Analyze/Descriptive Statistics/Explore
  • Choose the Variables you wish to check for errors
  • Click Statistics
  • Check box for labelled “Outliers” and “Descriptives
  • Press Continue then OK
  • View Output Labelled Extreme Values
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18
Q

What to do if I find data entry errors

A
  • Remove Data
  • Make an educated guess about what was intended.
    • then amend data accordingly
    • be open and transparent about massaging data
  • Go back to source material and try to find the correct data score
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19
Q

Descriptive Statistics

A
  • Provides measures that give you an informative snapshot of what’s going on with your data
  • Descriptive statistics are brief descriptive coefficients that summarize a given data set
  • Can be either a representation of the entire or a sample of a population.
  • Descriptive statistics are broken down into measures of central tendency and measures of variability
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20
Q

Measures of Central Tendency

A
  • Includes the mean, median, and mode,
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21
Q

Measures of Variability

A
  • Include the standard deviation
  • Variance
  • Minimum and maximum variables
  • Kurtosis and skewness.
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22
Q

Inferential Statistics

A
  • Where we run our classic statistical tests
  • Ways of analysing data using statistical tests
  • Allow the researcher to make conclusions about whether a hypothesis was supported by the results.
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23
Q

Exploratory Data Analysis Procedure

A
  • The EXPLORE function in SPSS
  • Provides a comprehensive series of descriptive outcomes
  • Procedure in SPSS that gives us more information about the data than any other
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24
Q

Confidence Interval

A
  • A 95% confidence interval is a range of values that you can be 95% certain contains the true mean of the population.
  • Estimates what the mean is in the population supposing there is a sampling error
  • Builds in the possibility that there is an error when trying to generalise data across a population
  • We can be 95% confident that the true mean across the population lies within this range
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25
Q

5% Trimmed Mean

A
  • Trims of highest and lowest 5% to eliminate the negative impact of outliers
  • If this figure is close to the mean then we know that there was not a significant outlier score amongst this variable.
26
Q

Standard Deviation

A
  • The word Standard is the old fashioned word for Average
  • Standard Deviation = Average Deviation
  • Average amount by which scores differ from the mean
  • Gold Standard measure of variability
27
Q

Variance

A
  • Standard Deviation squared
  • Directly related to Standard Deviation
  • Measures how far a set of numbers are spread out from their average value.
28
Q

Interquartile Range

A
  • The range between the 25th and 75th percentile
  • Has the Mean as the 50th percentile
  • Known as the IQR
29
Q

Quartiles & Percentiles

A
  • Percentiles divide a data set into 100 equal parts to tell us what percent of the total frequency of a data set was at or below that measure.
30
Q

Upper Quartile

A

Above the 75th Percentile

31
Q

Lower Quartile

A

Below the 25th Percentile

32
Q

Skewness – Positive

A
  • A measure of asymmetry in the probability distribution of a variable
  • A lot of scores located at the low end values
  • Creates a peak in the lower quartiles not in the middle
  • Creates a tail to the right of the distribution pattern
33
Q

Skewness – Negative

A
  • A measure of asymmetry in the probability distribution of a variable
  • A lot of scores located at the high end values
  • Creates a peak in the upper quartiles not in the middle
  • Creates a tail to the right of the distribution pattern
34
Q

Skewness

A
  • SPSS reports a skewness score
  • If score is in the negative then the graph is negatively skewed
  • If score is reported positive then the graph is positively skewed
35
Q

Kurtosis

A
  • Measure that indicates the shape of the curve in a frequency distribution graph
  • Demonstrates if there is a wide or narrow variation from the mean
36
Q

Kurtosis – Leptokurtic graph

A
  • Demonstrated by a bell curve that is tall and narrow
  • Indicates that there is not much dispersion of variability in the values
37
Q

Kurtosis – Platykurtic graph

A
  • Demonstrated by a bell curve that is flat and wide
  • Indicates there is a wide dispersion of variability in the values
38
Q

Stem & Leaf Plots in SPSS

A
  • Explore Command – Analzye/descriptive Statistice/Explore
  • Choose your variables
  • Plots/Select Factor Levels together; Stem and Leaf and Histogram/Continue/OK
39
Q

Missing Values in SPSS

A

Analyze/Descriptive Statistics/Explore/Options/Choose your value

40
Q

Missing Data/Exclude cases listwise

A
  • If SPSS finds missing data in the variable then it will eliminate the whole record
  • This is not considered optimum as the data in the whole record could still be valuable
41
Q

Missing Data/Exclude cases pairwise

A
  • This overrides the function that deletes a record if it contains missing data
42
Q

Stem & Leaf Plot

A
  • Every case is represented in the leaf section and if we counted them they would add up to the total number of participants
  • Each persons score gets converted into a 2 digit number
  • A Stem of 0 says the score is between 0-9, 1 says the score is between 10-19 etc
  • In larger data sets each leaf could represent more than 1 person
43
Q

Box & Whisker Plot

A
  • Also developed by Tuckey
  • Tries to capture the essential characteristics of a single variable in one graph
44
Q

Pull apart the Box & Whisker Plot

A
  • The thick black line in the middle represents the Median
  • Top of the box is the 75th Percentile
  • Bottom of the box is the 25th Percentile
  • Width of box is the InterQuartile Range - Middle 50%
  • Upper Whisker the highest score that is not an outlier
  • Lower Whisker the lowest score that is not an outlier
45
Q

Tukey’s Hinges

A
  • Upper Hinge is 75th percentile
  • Lower Hinge is 25th percentile
    *
46
Q

Box & Whisker Plots - Outliers and Extreme values

A
  • Extreme values will be indicated by:
    • circles which represent outliers
    • stars which represent Extreme Values
  • Each star and circle is labelled with a number; this is the number of the subject
47
Q

What is a Z Score?

A
  • A z-score gives you an idea of how far from the mean a data point is.
  • Technically it’s a measure of how many standard deviations below or above the population mean a raw score is.
  • Also called a standard score
  • Can be placed on a normal distribution curve.
  • Z-scores range from -3 standard deviations up to +3 standard deviations
  • In order to use a z-score, you need to know the mean μ and also the population standard deviation σ
48
Q

Symbol for Mean

A

The Mean μ

49
Q

Symbol for Standard Deviation

A
50
Q

Z Score Formula

A

z = (x – μ) / σ

  • x is an individual variable score
  • μ is the mean
  • σ is the Standard Deviation
  • Z Score is:
    • (Variable - Mean)/Stanadard Deviation
51
Q

Why are outliers a problem

A
  • disproportionate effect on results
  • can push up means and affect central tendency
52
Q

What to do with Outliers

A
  • Some say delete Outliers all together
  • Some say keep them, data is data; if its genuine then it is valid
  • Ask “is the score a legitimate ‘real’ value or is it a score of error?”
53
Q

Parametric Procedures

A
  • Tests that makes certain assumptions about your data
  • Main two are:
    • Normality
    • Homogeneity of Variance
54
Q

Assumption of Normality

A
  • Parametrics assumes that our sample population that is normally distributed.
55
Q

Assumption of Homogeneity of Variance

A
  • Parametric Procedure that assumes if we divide our data into groups that the variability in the groups will be the same
  • The level of variability between the groups is not significantly different
56
Q

4 ways to test Normality

A
  1. Looking at Histograms and Stem & Leaf Plots
  2. Looking at Normality and Detrended Normality
  3. Normality Tests
  4. Skewness divided by SE Skewness
  • 1 & 2 are purely subjective
  • 3 & 4 are numerical and objective
57
Q

Normality Q-Q Plot

A
  • Data sitting along the line then the data can be considered normal
58
Q

Detrended Normality Plot

A
  • Data points should appear random and all over the place
59
Q

Normality Test - Degree of Skewness

A
  • In Descriptive Statistics/Explore
  • Take Skewness and divide by Standard Error of Skewness
  • If the result is between -2 to +2 then that is considered normal
  • anything outside this range suggests a problem with Skewness
60
Q

Tests of Normality

A
  1. Kolmogorov-Smirnov
  2. Shapiro-Wilk

If these values are less than .05 then there is a problem with normality

    • < 0.05 = non-normality
  • If the two scores give conflicting results then use your judgment to decide whether to confirm Normality
  • Don’t rely too much on one source of information. Check the other Normality Tests

Evidence Based Approach 2 (14 decks)

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