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AUD spring 2020 research > week 15: hypo testing III > Flashcards

week 15: hypo testing III Flashcards

1
Q

inferential statistics

A
  • characteristics of a total population based on a sample
  • the validity of the inference depends on the quality of the sample
  • generalization requires a representative sample
  • no sample will be a perfect replica of a population
  • an imperfect sample does not negate the value of a study, but it limits the conclusions that may be drawn from it
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2
Q

what is the formal definition of inferential stats

A

determine the probability that the null hypothesis accurately represents the conditions in the population; if the statistical test is significant, then the researcher will reject the null hypothesis

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3
Q

what does it mean if a statistic is significant at 0.05 level

A

the probability of error is less than 5 in 100

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4
Q

what does it mean if stats are not significant

A

the probability of error in rejecting the null hypothesis is unacceptably high, unusually beaus the chance of error is greater than 5 in 100

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5
Q

type 1 error

A

reject the null hypo when it is correct

  • saying something is significant when it is not
  • could be from sampling issues
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6
Q

type 2 error

A

occurs when a researcher fails to reject the null hypothesis when it is incorrect

  • saying no significant difference when there is
  • -could be from lack of power so want more participants to fix this
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7
Q

how to chose a statistical test

A

depends on the level of measurement (ordinal, interval, or ratio)

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8
Q

parametric statistics

A

recommended for interval or ratio level measurements
*observations that fit certain assumptions about the distribution of data around the mean (normal distribution) and larger sample sizes

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9
Q

nonparametric statistics

A

recommended for ordinal level measurements, situations in which you are uncertain about the distribution of the data, and have smaller sample sizes

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10
Q

t-test or analysis of variance (anova)

A
  • parametric statistics–probability distribution
  • require continuous (interval or ratio) data or suitably transformed data
  • powerrful and convenient for testing Ho
  • student’s t test (leptokurtic)
  • –n is less than 30 and depends on the degrees of freedom
  • z-test is n is igger than 30
  • all assume random sample
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11
Q

chi-aquared test

A

usually the best choice for categorical variables

  • nonparametric stats–a test of independence
  • typically used to analyze data that are too weak to analyze with a t-test or anova
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12
Q

regression

A

evaluate the relationship between variables and allow prediction

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13
Q

test for testing the difference between two samples

A
  • t test is the most common for analyzing the difference between two sets of data when you have interval or ratio level measures
  • –specifically tests the difference between means to determine if the two groups are significantly different
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14
Q

3 factors affecting whether you will find a significant difference with a t test

A
  • magnitude of the difference between means
  • amount of variability of the data
  • sample size
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15
Q

related t test

A

two measures are performed on the same participant

—this is a paired t-test

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16
Q

independent t test

A 
independent samples (between subjects)
*perform an independent t test
17
Q

paired samples designs

A

within the same subject

  • if it is not attributable to chance the Ho is rejected
  • examples of designs: pretest/posttest or same subjects designs with two conditions
  • assumptions:
  • –continuous data
  • –random samples
  • two sets of scores are correlated
  • –the difference scores distributions are normal
  • wilcoxon signed-rank test for paired data is the nonparametric test
18
Q

independent samples design

A

includes 2 groups of subjects

  • –known as between subjects design
  • independent samples t test tells if the observed difference between two groups is attributable to chance
  • t test is samples are above 30 and student’s t is below 30
  • assumptions
  • –continuous data
  • –random samples
  • –2 independent and unrelated groups
  • –normal distribution
  • –groups have equal variance
19
Q

confidence interval

A

confidence interval is establishing the precision of a result and deciding whether to accept or reject the Ho

  • a way of estimating the margin of error associated with the study
  • –larger groups have smaller CI and the more the CI overlap for each group the less definitive the group differences
  • if the CI contains 0, the Ho is accepted
  • the width of the CI indicates the precision of measurement, which depends on the sample size
20
Q

what is the outcome of a t-test and what do you do with it

A
  • outcome is known as the t-ratio using the pooled variance
  • to test the null hypo, the observed value for t is compared to the critical value
  • –if the observed value is greater than the critical value the null hypo is rejected and the alt hypo is accepted
21
Q

ANOVA

A

analysis of variant and is often used when there are more than two groups or two conditions

  • –gives an f statistic or f ratio
  • assumes
  • –continuous data
  • –random sampling
  • –normal distributions
  • –equal variances
  • analyzed many groups at one time
  • is related to the t statistic f=t squared
22
Q

one way anova

A
  • separates variance into two distinct parts
  • –within group varibality which is the portion of variability that cannot be explained by the research design; this is known as the mean square for error
  • –between group variability is the portion of variance attributable to group membership (effect) this is known as the mean square for effect
  • the two variances are compared in order to test whether the ratio of variances is significantly greater than 1
  • Kruskal-Wallis is nonparametric
23
Q

factorial designs

A
  • investigates two or more independent variables in the same study
  • can use two way anova, 3 way anova, or more but rare with more than 4-5 factors
  • two way anova enables you to determine if there are differences associated with main effects as well as any interactions between the levels
24
Q

two way anova

A
  • a significant interaction means that the outcome for one of the independent variables was different depending on the level of another independent variable
  • –useful to plot the individual means on a line graph to show the interaction between the main effects
  • nonparametric = friedman’s test
  • –does not evaluate interaction effects
25
Q

repeated measures anova

A

available for situations in which researchers obtained several measures from the same participants

  • these kinds of designs involve participants being observed under 3+ experimental conditions
  • could be one way or two way depending on the number of independent variables
26
Q

mixed models anova

A
  • analysis of randomized pretest/posttest design
  • deemed “mixed model” because it has both repeated measure factor and between group factor
  • sources of variance:
  • –participants
  • –between group factor
  • –repeated measures factor
  • –interaction (treatments by time)
  • –error term
27
Q

post-hoc comparisons

A
  • when multifactor anova identifies significant effects, the location of the effect is not always evident
  • –significant main effect simply indicates the presence of at least one significant difference between means
  • –to identify the specific pairs of means that are significantly different, researchers use a post hoc comparison
28
Q

measures of association

A

used when researchers are interested in investigating the relationship between two or more sets of scores

  • gives us info about the strength of the relationships as well as the direction of the relationship
  • commonly used:
  • –correlation coefficient
  • –regression models
  • –chi-squared analysis
  • –contingency coefficients
29
Q

correlation coefficient

A
  • pearson product-moment correlation coefficient
  • spearman rank-ordered correlation coefficient
  • interpretation:
  • –direction of the relationship
  • –magnitude of the correlation
  • –the possibility that the observed relationship between the variables occurred because of random chance
30
Q

regression models

A

allow prediction of values

*simple regressions with one independent and one dependent variable or multiple regression

31
Q

another term for categorical data

A

count data

*used for nominal measures

32
Q

chi squared tests

A
  • analysis of count data
  • –the observed frequencies (counts) differ from the frequencies expected by chance?
  • these test have many limitations and assumptions:
  • –individual observations must be independent of each other
  • –observations for analysis must be count data
  • –the sum of the expected freq must equal the sum of the observed freqs
  • –the categories must be exclusive of one another
  • –the expected values for any one cell must not be too small
33
Q

contentment tables

A

the rows-by-columns tables that are used to organize the data for X2 analysis

34
Q

how is the practical significance of a statistical outcome expressed

A

effect size

  • statistical significance does not equal practical significance
  • effect size is important and should always be reported!
35
Q

simple effect size

A

the raw difference between treatment means

  • useful for easily interpretable units of measurement
  • not good when there is a lot of variability involved in the measurement, and it cannot be compared between studies as it is not a standard unit
36
Q

effect-size correlation

A

the correlation between the independent variable and the individual scores
*r is good for the directionality and size and r2 is the possibility of chance

37
Q

the standardized effect size

A

most commonly used

  • accounts for variability and provides a statistic that can be used to compare with the results of other studies
  • many measures
  • –cohen’s d which is best used for large samples
  • –hedge’s g which is better for small samples
  • –eta squared which is best for anova and is similar to r squared
38
Q

the problem of unequal sample sized

A
  • affects power and efficiency of experimental design
  • to identify the amount of loss on the power and efficiency is done by comparing the arithmetic and harmonic means of the samples

AUD spring 2020 research (7 decks)

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