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STATISTICS QUALI (ASSUMPTIONS) Flashcards

1
Q

T-TEST FOR SINGLE SAMPLE ASSUMPTIONS
( to be able to use the One sample T-TEST you should have a population mean)

  3. 4.
A
  1. Dependent variable must be continous (interval/ratio)
  2. Independence of observation
  3. Normally distributed
  4. Not contain any outliers
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2
Q

ASSUMPTIONS FOR T-TEST INDEPENDENT SAMPLES

  4. 5.
A
  1. Dependent variable must be Continous (interval or ratio)
  2. Independent variable should consist of two categorical “related groups” or “matched pairs”
  3. The observation within each treatment condition must be independent
  4. No significant outliers in the difference between two related groups
  5. Approximately normally distributed
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3
Q

What do you mean by robustness?

A

• A particularly hypothesis testing procedure is reasonably accurate even when its assumption are violated
• Test can still produce a valid result even if the normality assumption is not met

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4
Q

WILCOXON-SIGNED RANK TEST
• Non-parametric test equivalent to the paired T-test
• Does not assume normality in the data, it can be used when this assumption has been violated and the use of paired T-test is appropriate

ASSUMPTIONS?
1.
2.
3.

A
  1. Dependent variable measured at Ordinal or continuous level
  2. consist of two categorical “related groups” or “matched pairs”
  3. Distribution is not normal
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5
Q

ASSUMPTIONS FOR THE T-TEST FOR INDEPENDENT SAMPLES
1.
2.
3.
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5.
6.

A
  1. Dependent variable measured on a continuous scale (interval/ratio)
  2. Independent variable should consist of two categorical, independent groups
  3. There should be an independence of observation
  4. no outliers
  5. Normally distributed for each group of the independent variable
  6. There Needs to be homogeneity of variance
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6
Q

ASSUMPTIONS OF MANN-WHITNEY U TEST
• Used to compare difference between two independent groups when the dependent variable is either ordinal or continous, but not normally distributed
ASSUMPTIONS?
1.
2.
3.
4

A
  1. Dependent variable should be measured at the Ordinal or continuous level
  2. The independent variable should consist of two categorical, independent groups
  3. Have independence of observation
  4. Two variables are not normally distributed
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7
Q

ASSUMPTIONS OF ANOVA
1.
2.
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5.
6.

A
  1. Measured interval/ratio (continuous)
  2. Independent variable consist of two or more categorical, independent groups
  3. Independence of observation
  4. No outliers
  5. The residuals of dependent variable is approximately normally distributed
  6. Needs to be homogeneity of variance
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8
Q

POST HOC COMPARISON (Done after ANOVA)
TURKEY’S HONESTLY SIGNIFICANT DIFFERENCE (HSD TEST)
• A single step multiple comparisons procedure and statistical test. Can be used on a raw data or in conjunction with an ANOVA to find means that are significantly different from each other
1. Use?

GAMES HOWELL
• Non-parametric approach in comparing combination of groups or treatments
2. Used?
SCHEFFÉ S TEST
• method of figuring the significance of post hoc comparison that takes into account all possible comparisons that could be made.
3. Used?

BONFERRONI PROCEDURE
• A multiple comparisons procedure in which? ___ so that each is tested at a more stringent significance level

A
  1. Used for equal sample sizes
  2. It does not assume equal variances and sample sizes
  3. Used for unequal sample sizes
  4. Total alpha percentage divided Among the set of comparison
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9
Q

ASSUMPTIONS OF KRUSKAL WALLIS H TEST
• “one way ANOVA” on ranks
• A rank based parametric test that can be used to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or independent variable
ASSUMPTIONS:
1.
2.
3.
4.

A
  1. Dependent variable should be measured at the ordinal or continous level
  2. Independent variable should consist of two or more categorical, independent groups
  3. Have independence of observation
  4. Two or more dependent variables are not normally distributed
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10
Q

VARIATIONS OF ANOVA
1. ____ an ANOVA for a repeated measures design, a design with one group of individuals participating in three (3) or more treatment conditions

  1. ___ An ANOVA used for factorial design, with more than one independent variable and one dependent variable
A

Repeated measures ANOVA
Two-way ANOVA

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11
Q

___ analysis of variance that controls for the effect of one or more additional variables
• Covariate - variable controlled for in analysis of variance

A

ANCOVA

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12
Q

___ analysis of variance that controls for the effect of one or more additional variables
• Covariate - variable controlled for in analysis of variance

A

ANCOVA

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13
Q

___ analysis of variance with more than one dependent variable

A

MANOVA

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14
Q

____ analysis of variance with more than one dependent variable

A

Mancova

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15
Q

COEFFICIENT OF DETERMINATION (CORRELATION COEFFICIENT)
ASSUMPTIONS?
1.
2.
3.
4.
5.
6.
7.

A
  1. Measured at interval or ratio level(continous)
  2. Two continous variables should be paired
  3. Independence of cases
  4. A linear relationship between your two continous variables
  5. Both continous variables should follow a bivariate normal distribution
  6. There Shoud be homoscedasticity
  7. No univariate or multivariate outliers
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16
Q

SPEARMAN’S RHO
• A non parametric measure of the strength and direction of association that exists between two variables measured on atleast an ordinal scale
ASSUMPTIONS?
1.
2.
3.

A
  1. Two variables should be measured on a ordinal, interval or ratio scale
  2. Two Variables represents paired observation
  3. There is a monotonic relationship between the two variables
17
Q

KENDAL TAU-B
• a non parametric measure of strength and direction of association that exists between two variables measured on atleast an Ordinal scale

ASSUMPTIONS?
1.
2.

A
  1. Two variables should be measured on an Ordinal or continuous level
  2. There is a monotonic relationship between your two variables
18
Q

PARTIAL CORRELATION
• the amount of association between two variables, over and above the influence of one or more other variables

A
19
Q

SIMPLE LINEAR REGRESSION

• also called bivariate regression, a statistical technique where the prediction of scores on one variable is based on scores of one other variable
• X –> Y

A
20
Q

MULTIPLE REGRESSION

• Procedures for predicting scores on a criterion variable from scores on two or more predictor variables

A
21
Q

SIMPLE LINEAR REGRESSION
• Used when we want to predict the Value of a variable (outcome) based on the value of another variable (predictor)

ASSUMPTIONS?
1.
2.
3.
4.
5.
6.
7.

A
  1. Outcome variable should be measured at continous level (interval/ratio)
  2. Predictor can be continous, dichotomous or odinal
  3. Need to be a linear relationship between the two variables
  4. No outliers
  5. Independence of observation
  6. Data needs to show homoscedasticity
  7. Residuals should be approximately normally distributed
22
Q

MULTIPLE REGRESSION
• An extension of simple linear regression which can be used if we want to predict the Value of a variable (outcome) based on the value of two or more other variables (predictors)
ASSUMPTIONS?
1.
2.
3.
4.
5.
6.
7.
8.

A
  1. At continous level
  2. Predictors can be nominal or continous level
  3. No outliers
  4. Linear relationship between the outcome variable and each predictor
  5. Independence of observation
  6. Residuals are approximately normally distributed
  7. Data needs to show homoscedasticity of residuals
  8. Data must show minimal multicollinearity
23
Q

CHI-SQUARE GOODNESS OF FIT TEST
• It uses a sample data to test hypothesis about proportions for a population distribution
• It is used to determine how well the obtained sample proportions fit the population proportions specified by the null hypothesis
ASSUMPTIONS?
1.
2.
3.
4.

A
  1. One categorical variable (dichotomous, nominal, ordinal)
  2. Independence of observation
  3. Groups of categorical variable must be mutually exclusive
  4. Should be atleast 5 expected frequencies in each group of categorical variables
24
Q

CHI-SQUARE STATISTICS OF INDEPENDENCE
• Hypothesis testing procedure that examines wether the distribution pf frequencies over the categories of one nominal variable is unrelated to the distribution of frequencies over the categories of a second nominal variable
ASSUMPTIONS?
1.
2.
3.

A
  1. Two variable should be measured at an ordinal or nominal level
  2. Two variables consist of two or more categorical, independent groups
  3. Less than 20% of the cells should have an expected count/ frequency of less than 5

FINALS (15 decks)

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