Statistical Analysis and Results Section Flashcards

1
Q

Results section should contain

A

Narrative description of statistical outcomes

Tables and figures that summarize findings

Statements of support of the hypotheses or rejection of the hypotheses

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2
Q

Interval or ratio measurement data may be described in 4 ways:

A

Central tendency
Variability
Skewness
Kurtosis

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3
Q

Average score of a group

A

Central tendency

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4
Q

3 measures of central tendency

A

Mean, median, mode

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5
Q

How much the scores vary from the average

A

Variability

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6
Q

3 measures of variability

A

Range, variance, standard deviation

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7
Q

The lack of symmetry of the distribution of scores

A

Skewness

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8
Q

the general shape of the distribution of scores

A

Kurtosis

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9
Q

When does normal distribution happen

A

when the middle scores occur most often and the lower and higher scores do not occur often.

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10
Q

If the data is not normally distributed then….

A

nonparametric statistical procedures are used

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11
Q

more powerful than nonparametric statistical procedures.

A

Parametric statistics

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12
Q

4 Parametric statistical procedures:

A

Normal distribution of the data
Interval or ratio level of measurement
If 2 or more data distributions are analyzed, their variances should be similar
Large sample size

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13
Q

When are Nonparametric statistics used

A

when one or more of these are not met

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14
Q

Statistical significance testing involves

A

testing the null hypothesis in the context of the data.

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15
Q

likelihood that one event will occur, given all the possible outcomes

A

Probability

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16
Q

probability of the findings

A

P value

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17
Q

p < .05 means

A

NULL HYPOTHESIS IS REJECTED

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18
Q

p > .05 means

A

NULL HYPOTHESIS IS NOT REJECTED

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19
Q

can be correlational or inferential

A

Data analysis

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20
Q

level of significance

A

.05 or less (p < .05)

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21
Q

evaluate relationships among data

A

Correlational statistics

22
Q

often described using correlation coefficients

A

RELATIONSHIPS W/ IN DATA

23
Q

A perfect positive relationship between two variables is indicated by

A

1.0

24
Q

A perfect negative relationship is indicated by

A

-1.0

25
Q

The absence of a relationship is indicated by

A

ZERO

26
Q

A small number indicates a

A

weak relationship between two variables

27
Q

A large number indicates a

A

strong relationship between two variables

28
Q

The square of the correlation coefficient is used to assess

A

PRACTICAL MEANING

29
Q

Variables that are correlated can be described as

A

varying together, but there may be no cause-effect relationship

30
Q

Presenting the results of correlational statistics involves 4 types of analysis/tables

A

Regression analysis
Bivariate analysis
Multivariate analysis
Contingency table

31
Q
A

Regression analysis

32
Q
A

Bivariate analysis

33
Q
A

Multivariate analysis

34
Q

Chi square

A

Contingency table

35
Q

nonparametric test applied to nominal data, comparing observed frequencies within categories to frequencies expected by chance

A

Chi-square

36
Q

evaluate differences among data, either between-subjects or within-subjects

A

Inferential statistics

37
Q

used to compare two different groups

A

Independent t-test

38
Q

used for within group comparisons

A

Dependent t-test

39
Q

simultaneous comparison of several means

A

ANOVA- analysis of variance

40
Q

only one independent variable

A

One-way ANOVA

41
Q

two independent variables

A

Two-way ANOVA

42
Q

between subjects comparison (ordinal data)

A

Kruskal-Wallis one-way ANOVA

43
Q

from related samples (nominal data)

A

Cochran Q test

44
Q

within-subjects comparison (ordinal data)

A

Friedman two-way ANOVA

45
Q

from independent samples (nominal data)

A

Chi-square

46
Q

multivariate analysis of variance

A

MANOVA

47
Q

analysis of covariance

A

ANCOVA

48
Q

Bonferroni correction

A

Multiple t-test

49
Q

Effect size is

A

a quantitative measure of the difference between two groups

50
Q

Cohen’s d - effect size estimator used to

A

compare the means of two or more groups