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6030 - Introduction to Educational Research > Chapter 9 > Flashcards

Flashcards in Chapter 9 Deck (33)
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1
Q

Inferential Statistics

A

Statistical procedures designed to determine if differences and relationships found in sample data are sufficiently large that they can be assumed to be true at the population level

2
Q

Degree of Uncertainty

A

All inferences from samples to populations involve a degree of uncertainty. The amount of uncertainty is determined by a number of factors, including sampling error and measurement error.

3
Q

Measurement Error

A

This is differences in estimates of a population parameter based upon different testings of the same sample using the same instrument. These differences are the result of unreliability in our measurement instrument.

4
Q

Sampling Error

A

This is the differences in estimates of a population parameter based upon different samples drawn from the same population. If the samples were randomly drawn and all of the same size, the amount of error (at a certain probability level) can be calculated.

5
Q

Null Hypothesis

A

There is no difference between the groups (or the difference is in the unpredicted direction)

6
Q

Alternative Hypothesis

A

There is a difference between the groups (or the difference is in the predicted direction)

7
Q

level of significance

A

also known as a level

8
Q

If the _____________ would have generated the sample statistic less often by chance than the a level, the researcher rejects the ______ and affirms the alternative.

A

null hypothesis, null

9
Q

Type I Error

A

Rejecting the null when it is in fact true

10
Q

Type II Error

A

Failing to reject the null when it is in fact false

11
Q

Difference between group means

A

larger differences make it easier to achieve significance, all other things being equal

12
Q

Sample size (N)

A

larger sample sizes reduce sampling error and make it easier to achieve significance, all other things being equal

13
Q

Reliability of the instruments

A

more reliable instruments reduce measurement error, and make it easier to achieve significance, all other things being equal

14
Q

Confidence Intervals

A

An interval within which we believe the true population parameter falls, at a given level of confidence (usually 95% or 99%)

15
Q

Effect Size

A

Statistical significance answers the question, “Are the results likely to be due to sampling error?”

16
Q

Sample size does not play a role in ______________.

A

effect size

17
Q

Cohen’s d =

A

Mean One – Mean Two/Standard Deviation (Either Pooled or for Control)

18
Q

Coefficient of Determination =

A

r squared (for correlations)

19
Q

Eta Squared =

A

Sum of Squared Deviations Between/Sum of Squared Deviations Total

20
Q

Parametric Statistics

A

Inferential statistical tests that make a number of assumptions about the data (normality, equality of group variances, and interval/ratio level of measurement)

21
Q

Non-Parametric Statistics

A

Inferential statistical tests that make relatively few assumption about the data

22
Q

Parametric statistics

A

are more “powerful” than non-parametric statistics; that is, they are more likely to reject the null hypothesis when it is in fact false

23
Q

The t-test

A

Tests for differences between the means of two groups.

24
Q

Independent samples t-test

A

Used to test differences in means of two different groups of subjects

25
Q

Paired dependent samples t-test

A

Used to test differences in means of two groups of subjects who are either the same or matched on one or more variables

26
Q

T-test for correlations

A

Tests whether a sample correlation is large enough to indicate a relationship different from zero in the population

27
Q

Simple Analysis of Variance (One-Way ANOVA)

A

Tests for differences in the means of three or more groups representing levels of a single IV factor

28
Q

Factorial Analysis of Variance (N-way ANOVA)

A

Allows testing of multiple IVs, each of which may have many levels.

Allows determination of the significance of each IV

Allows determination of the significance of changes in an IV as a function of changes in other IVs

29
Q

Univariate

A

techniques have a single DV

30
Q

Multivariate techniques

A

have multiple DVs

account for the correlations among the DVs (which would not happen if you ran multiple univariate tests)

31
Q

Chi-Square

A

Used to analyze relationships among categorical variables

Appropriate when the DV is frequency of occurrence data (how many cases fall into each category or combination of categories)

32
Q

Chi-square

A

is a non-parametric statistical test

33
Q

Criteria for Evaluating Inferential Statistics

A

Basic descriptive statistics are needed to evaluate the results of inferential statistics
Inferential analyses refer to statistical, not practical significance

Inferential analyses do not indicate external validity (unless appropriate sampling was used in the design phase)

Inferential analyses do not indicate internal validity

The results of inferential tests depend on the number of subjects

The appropriate statistical test should be used

The level of significance should be interpreted correctly

Be wary of statistical tests with small numbers of subjects in one or more groups or categories