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Evidence Based Approach 2 > Word Doc Week 2 > Flashcards

Word Doc Week 2 Flashcards

1
Q

What should I do before conducting any data analysis?

A
  • “Screen & Clean” Data
  • SPSS can not tell if data is ridiculous
  • Only the researcher knows this so I need to make sure I screen the data for errors
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2
Q

Six reasons for conducting exploratory data analysis

A
  1. Checking for data entry errors
  2. Obtaining a thorough descriptive analysis of your data.
  3. Examining patterns that are not otherwise obvious
  4. Analysing and dealing with missing data
  5. Checking for outliers
  6. Checking assumptions
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3
Q

EXPLORE command in SPSS

A
  • The explore command is SPSS covers all bases.
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4
Q

Who is John Tukey

A
  • A very practical statistician
  • Language and techniques of EDA developed by him
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5
Q

What does Screening and Cleaning involve

A
  • Computing new variables from existing ones
  • Recoding variables
  • Dealing with missing data
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6
Q

Checking Data Entry Errors

A
  • Use the frequencies command to check for data entry errors in categorical/nominal variables
  • Use the outliers option in the explore command to check for data entry erros in continuous/scale variables
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7
Q

Options for Dealing with Data Entry Errors

A
  1. Remove data
  2. Make ‘educated guesses’ about what was intended
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8
Q

Obtaining a thorough descriptive analysis of data

A
  • The explore command provides more descriptive stats than any other procedure
    • Multiple measures of central tendency
    • Multiple measures of variability
    • Quantitative measures of shape
    • Confidence intervals
    • Percentiles
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9
Q

Examining Patterns that are not otherwise obvious

A
  • Stem and Leaf Plots
  • Box and Whisker Plots
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10
Q

Analysing and dealing with missing data

A

Do you leave it out or do you substitute in the mean?

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11
Q

Outliers

A
  • Are they legitimate, or are they error?
  • Do you keep them? Do you discard them?
  • Balancing act, as with so much in data analysis
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12
Q

Assumptions

A
  • All parametric procedures (e.g., t-tests, ANOVA’s, correlation) operate under certain assumptions
  • Main two:
    • Normality
      • Assumes that your data comes from population that is normally distributed
    • Homogeneity of variance
    • Assumes that, if your data is to be divided into groups, the level of variability in the groups will be approximately equal (i.e., not significantly different).
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13
Q

Normality is tested in four ways:

A
  1. Visual inspection of histograms and stem and leaf plots
  2. Visual inspection of normality and detrended normality plots
  3. Normality tests
  4. Skewness divided by SE skewness
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14
Q

Subjective Normality Tests

A
  1. Visual inspection of histograms and stem and leaf plots
  2. Visual inspection of normality and detrended normality plots
    • not influenced by sample size
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15
Q

Objective Normality Tests

A
  1. Normality tests
  2. Skewness divided by SE skewness
  • Objective but influenced by sample size
  • With a large sample, even trivial deviations from normality will indicate a violation of the assumption
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16
Q

The least important assumption

A
  • Normality
  • Statistical tests are very robust to even gross deviations from normality, particular when you have a large sample
17
Q

Why is normality the LEAST important assumption?

A
  • Statistical tests are based on sampling distributions
  • Sample distrbutions will always be normal regarles of the underlying population distribution
  • they will get more normal as the sample size increases
18
Q

When is normality a problem?

A
  • When the deviation is strong and the sample is small
19
Q

Tests 3 and 4 are sensitive to sample size

A
  • Ironical
  • Indicate a violation of normality with a large sample
  • Even if deviation is trivial
  • Normality is rarely a problem when the sample size is large
20
Q

Transformation

A
  • Applying a simple mathematical operation to the data to deal with violations of assumptions
21
Q

Common Transformations

A
  1. Log 10
  2. Square root
  3. Reciprocal
22
Q

Homogeneity of variance assumption

A
  • More serious particularly when the sample is unbalanced
  • tested using dedicated test: Levene’s Test
  • Dealt with by power transformation
23
Q

Levene’s Test

A
  • Used to test if k samples have equal variances.
  • Equal variances across samples is called homogeneity of variance.
  • Some statistical tests, for example the analysis of variance, assume that variances are equal across groups or samples.
  • The Levene test can be used to verify that assumption.
24
Q

Some general comments about transformations

A
  • They are no “magic bullet”.
  • They can’t cope with zeroes (need to add a constant)
  • They are unpredictable (e.g., a transformation designed to deal with normality can fix up homogeneity of variance and vice versa)
  • Some data is “untransformable”
25
Q

Recoding Data:

A
  • Often need to recode for a whole host of reasons:
    1. Reducing numbers of groups
    2. Reverse scoring

Evidence Based Approach 2 (14 decks)

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