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EBP :/ > Module 7 - Slides > Flashcards

Module 7 - Slides Flashcards

1
Q

Data Analysis / Reduction

A

Reduces large data sets into more compact, manageable and interpretable information

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2
Q

What does data analysis involve?

A

Organizing and Interpreting quantitative data following systematic rules, then followed by statistical analysis

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3
Q

Two main types of statistical analysis for quantitative data

A
  1. Descriptive statistics
  2. Inferential statistics
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4
Q

Descriptive Satistics

A

Used to characterize the SHAPE, CENTRAL TENDENCY, and VARIABILITY within a set of data, called a DISTRIBUTION

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5
Q

Parameters

A

Measures of population characteristics

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6
Q

Statistic

A

A descriptive index computed from sample data

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7
Q

Distribution

A

The total set of scores for a particular variable

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8
Q

Distribution of Scores -Coin Rotation Test (CRT)

A

Frequency distribution

Cumulative percent

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9
Q

Methods to Display Frequency Distributions

A

A table of rank ordered scores that shows the number of times each value occurred, or its frequency (f)
(Ex: histogram)

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10
Q

Graphing Frequency Distributions -Histogram

A

A type of bar graph, composed of a series of columns, each representing one score or group interval

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11
Q

Measures of Central Tendency (3Ms)

A

Mean (average)
Median (middle score)
Mode (the score that occurs most frequently in a distribution)

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12
Q

Variability

A

A measure of the spread of scores within a distribution, and expressed in different ways:
Range, Variance, Standard deviation (SD)

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13
Q

Range

A

From minimum to maximum

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14
Q

Variance

A

Expressed as sum of square (SS)

-Should be small if scores are close together, and large if they are spread out

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15
Q

Standard deviation (SD)

A

Square root of the variance (SS)

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16
Q

Normal Distribution

A

Known as a bell-shaped distribution or Gaussian distribution

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17
Q

Inferential Statistics

A

Used to make INFERENCES or draw conclusions about a POPULATION based on findings in a SAMPLE (involving a decision-making process)

18
Q

Fundamental Concepts of Statistics - Statistical Basics

A

▪Alpha (α) level and Probability (p) value
▪Confidence interval (CI)
▪Hypothesis testing (Null vs. Alternative
hypothesis)
▪Errors in hypothesis testing – type I and II
▪Statistical Power
▪Effect size

19
Q

Alpha Level

A

Level of significance
The amount of chance researchers are willing to tolerate
Specified before the analysis is conducted

20
Q

P value (Probability)

A

The likelihood that any one event will occur, given all the possible outcomes
Implies uncertainty - what is likely to happen
Is a product of data analysis

21
Q

When is there a statistically significant
difference?

A

If p < a ; otherwise, no significant difference
is found

22
Q

Confidence Intervals (CI)

A

A range of scores with specific boundaries or confidence limits; represents a specified
probability (e.g., 95% a traditional value) that the true population value is within the range

23
Q

Statistical Hypothesis Testing

A
  • Null hypothesis – H0: 1 = 2
    ➢There is NO difference between the groups or
    interventions”
  • Alternative hypothesis – H1: 1 ≠ 2
  • There is a difference.
  • Statistical conclusion: “Disproving” the null
    hypothesis
  • either Reject (H0) or
  • Do not reject (H0)
24
Q

Errors in Hypothesis Testing

A
  • Decision is either correct or not correct
  • Potential errors in statistical decision making
    ✓Type I – mistakenly finding a difference (false-positive +); probability is α
    ✓Type II – mistakenly finding no difference (false-negative -); probability is β
25
Q

Statistical Power

A

Power is the probability of attaining statistical significance; can be thought of sensitivity; or probability that a test will lead to rejection of the null hypothesis (H0)

26
Q

Power analysis involves 4 interdependent concepts: PANE

A

✓ P = power (1 – β; β = probability to commit Type II error)
✓ A = alpha (level of significance; 0.05 (default) or 0.01)
✓ N = sample size, its influence on power is critical
✓ E = effect size, the size of the effect of IV also influences the power

27
Q

Effect Size (ES)

A

A measure of the degree to which H0 is
false, or the size of the effect of the independent variable (IV)

28
Q

Assumptions that must be met for Parametric statistics

A
  1. Samples are randomly drawn from a parent population with a normal distribution
  2. Variances in the samples being compared are roughly
    equal
  3. Data should be measured on the interval or ratio scales
29
Q

Nonparametric statistics

A

Can be used when either or all of the assumptions is/are not met

30
Q

Parametric statistic - t-Test

A

Compares two means (of data samples)

31
Q

Parametric statistic - Analysis of Variance (ANOVA)

A

Compares more than two means

32
Q

Independent t-Test (unpaired)

A

Test difference between two independent groups or samples

33
Q

Levene’s test

A

Used to determine homogeneity of variance. If the test
is not significant, variances are assumed to be equal

34
Q

Paired t-Test

A
  • Used in repeated measures designs (see
    previous module)
  • Used when subjects exposed to both conditions
35
Q

Inappropriate Use of Multiple t-Tests

A

Use of multiple t-tests will increase the chance of making a Type
I error

36
Q

Analysis of Variance (ANOVA)

A
  • Differences between more than two means (3 or 4 …)
  • Uses the F statistic (counterpart to t statistic for t test)
  • Named for Sir Ronald Fisher
  • Based on parametric assumptions of
    ✓ normal distribution
    ✓ interval or ratio level of measurement
    ✓ equal variance within groups (Levene’s test*)
37
Q

One-Way ANOVA

A

Appropriate for one-way design with one IV with three or more levels (groups)
If comparing only two modalities, ANOVA is equivalent to the t-Test

38
Q

Two-Way ANOVA for factorial design

A

Two-way indicates two independent variables (IVs)
Accounts for the main effects of all the independent variables respectively, and the interaction effect
between the two IVs

39
Q

Mixed ANOVA for Mixed Designs

A

Between groups, within-group mixed design
Two independent variables
✓One (timing) repeated across all subjects (pretest & posttest) – within-subjects
✓The other randomized to independent groups (Tx) – between-subjects

40
Q

Format of the mixed ANOVA

A

A combination of
between-subjects (independent factors) and within-subjects
(repeated factors) analyses.
✓The independent factor is analyzed as it would be in a regular one-way ANOVA
✓The repeated factor is analyzed using techniques for a repeated measures
analysis.

EBP :/ (11 decks)

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