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Stats - Module 1 > Page 8 - Preparing for Analysis > Flashcards

Page 8 - Preparing for Analysis Flashcards

1
Q

Before conducting a statistical analysis you need to check your data for..

A
  1. Accurate of data entry
  2. Missing date
  3. Outliers
  4. Normality
  5. Linearity, homoscedasticity, homogenity of variance
  6. Independence
  7. Multicollineratity and singularity (MANOVA and multiple regression)
  8. Other assumptions
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2
Q

What can you use to check Data entry?

A

SPSS Procedures

or, Frequencies

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3
Q

You must consider the … and … of missing data

A

Amount - pattern

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4
Q

Amount of data missing

A

If more than 5% check patterns

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5
Q

What kind of patterns of missing data?

A

MCAR (missing completely at random)
MAR (Missing at random)
MNAR (Missing not at random)

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6
Q

MAR

A

A pattern of missingness predictable from other variables in the data set

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7
Q

MNAR

A

A pattern of missingness related to the variable itself

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8
Q

Litte’s MCAR test - when is data MCAR?

A

If p value is above 0.05 (non-significant difference from MCAR test/mean)

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9
Q

Missed data can be checked by?

A

List-wise deletion
Mean substitution
Expectation maximisation
Multiple imputation

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10
Q

List-wise used when

A

Few cases missing
Variables not critival to your analysis
Data are missing at random
Missing data on a different variable

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11
Q

Mean substitution

A

Replacing value with the mease of cases across items

Not highly recommended, can skew mean

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12
Q

Expectation maximisation

A

Estimated the shape of the distribution and infering the liklihood the value falling with that distribution
Most simple and reasonable with random missing data

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13
Q

Multiple imputation

A

Used regregression to predict values based on other variables in your dataset
Most respectable, can be used a MNAR MCAR
More difficult

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14
Q

An outlier is…

A

A case with such an extreme value on one variable (univariate) or such a strange combination of score on two or more variables (multivariate) that is distorts statistics
Can lead to type 1 (false positive) and type 2 (false negative) results

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15
Q

When can an outlier occur?

A

Participant interpreted question incorrectly
Experiementer eorror
Participants answer comes from different population
Population of participants has extreme values and is not normally distributed

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16
Q

Checking univariate outliers

A

Frequency distribution in histogram
Box-plots
Normal probability plots
Calculating standariised scrores (Z-scores +- 3.29

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17
Q

Before checking univariate outliers, determine…

A

Ungrouped (Correlations, regression and factor analysis) or,

Split by group (t-tests, ANOVAs, ANCOVAs, MANOVAs, logistic regression and discriminant analysis

18
Q

Checking multivariant outliers

A

Mahalanobis distance (chi squared distribution used x2)
Leverage
Discrepancy
Influence (cooks distance)

19
Q

Five methods of addressing Outliers

A

Ignoring data points
Deleting individual Data points
Running analysis with and without outlier/s
Modification to reduce the bias through winsorizing or tremming (only univariate)
Transforming data for large data sets (univariate, normally too complex for multivariate)

20
Q

Kurtosis

A

Peakedness of distribution
Positive (Leptokurtic) - High
Negative (Platykurtic) - Flat (looks like flat platypus)
https://img.tfd.com/mk/K/X2604-K-11.png

21
Q

What tests used for normality?

A

Kolmogorov-Smitov (out-dated)

Shapiro-Wilk

22
Q

Weaskness of normality tests

A

As sample size, and statistical power increases, tests can appears statistically significant (suggesting skewness) despite data points being normally distributed

23
Q

What does Field recommend when testing for normality?

A

You assess the extent of non-normality in your data using ‘converging evident of multiple techniques
Box-plots, histograms, and/or normality tests

24
Q

Non-normal data is more likely to result in Type 1, or Type 2 errors?

A

Type 1 (False Positive, incorrect rejection of the H0)

25
Q

Is there a normality test for multivarite normality in SPSS

A

No, however, is Shapiro Wilk is non-significant, can assume multivariate normality

26
Q

What is normality of residuals

A

Used for ungrouped data

Difference between observed and expected valiues on a variable should be normally distributed

27
Q

What is linearity

A

Straight line relationship betweem variables

*Seen by bivariate scatterplot, or residuals plots (multiple regression/linearity of residuals)

28
Q

Field (2018) states that linearity is one of the most important assumptions to meet for you analyses, as it underpins the process that you want to model. True or False?

A

True

29
Q

What is Homoscedasticity

A

Used for ungrouped data
Assumption in regression analysis that the residulas on the coninuum of scores for the predictor variable are fairly conistent and have similiar variances
(

30
Q

What is Homogeneity of Variance

A

Same as Homoscedasicity, but for grouped data
‘the assumption that the variance of one cariable is stable at all levels of another variable. Linear relationship, not exponential etc.

31
Q

What is independance of Observations

A

Each participant only participates once in the research + no influence of participants on other participants

32
Q

What is independance of residuals/errors

A

Errors in your model are not related to each other

Durbin-Watson test statistic used to check 1-3 preferred

33
Q

What are multicollinearity and singularity

A

‘Problems with a correlation matric that occurs when variables are too highliy correlated

34
Q

Multicollinearity

A

Variables are very highly correlated (>0.8)

35
Q

Singularity

A

Variables are redundant, one variable is a combination of two or more other variables

36
Q

What is Additivity?

A

The combined effect of individual predictors on an outcome variable is best represented by adding these individual effects together

37
Q

What test is used to check for homegeneity of variance?

A

Levene’s test
Non-significant = homogeneity of variance
Signnificant = Heterogeneity of variance

38
Q

Trimming Data

A

Deletion of cases with extreme values

  • Percentage based
  • Standard deviation based
39
Q

Winsorising

A

Extreme scores are replace with value that is not as extreme

  • Next highest or lowest score
  • Reaplce with next highest/lowest score that is not an outlier
  • Replace with score that is +- 3.29 Standard deviations from the mean
40
Q

Non-parametric statistics

A

Do not rely on normally distributed data - Spearmann correlation

41
Q

Robust methods

A

Trimmed mean/M-Estimator

Bootstrapping (estimates parameters of the same distri ution based on the sample data

Stats - Module 1 (6 decks)

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