research lecture 5 Flashcards by sophia carrasco

what are parametric statistics used for

to analyze QUANTITATIVE DATA

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what are examples of parametric Statistics that are used to analyze quantitative data

t-test, ANOVA, Pearson correlation, linear regression

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parametric stats are based on which distrcutions so the data needs to be normalized

t-distribution, F-distribution, chi-
square distribution

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non parametric statistics are used to analyze what kind of data

qualitative

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t Spearman rho, Mann-Whitney U, Friedman’s ANOVA, Wilcoxon-signed ranks .. these are examples of what

non parametric statistics that are used to analyze qualitative data

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what is our options when we have =violated assumptions or have nominal or ordinal data

non parametric statistics

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what is a statistical way to look at relationship among variables and it is based on a linear models

regression

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when is a linear regression significant

when the slop is not equal to 0

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what is a T test

see the difference between 2 means and it is significant if the slop does not equal 0

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what are teh parametric assumptions for t-test or one way ANOVA

I/R data
Normality
Homogeneity of Variance
Free of Extreme outliers
Independence of observations

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what is normality

real concern in smaller studies with a n < 30 (bc of the central limit theory)

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what are 3 way to assess normality

-check histogram
-skewness/kurtosis (if >2 or < -2 there is a problem)
- shapiro wilk test (want this to be greater then .05 to be significant)

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what happens to skewness as the sample size increases

it improves

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what is homogeneity of variance looking for and what should be the same

looking for difference , the variances of the outcome variable should be able the same in each group

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how to assess for homogeneity of variance

Levene’s test

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what do u want the levenes test to show

want it not to be significant , u want them to show “no difference”

set alpha at .05

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data must be ___ of observations .. scored must not follow a pattern over time also scores from one participants can’t influence another participants score

independent

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when u are looking at a graph how do u know if there is a problem with independence

if some of the scores are the same or follow a pattern there is a problem , it should all be reandiozned

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what are 3 regression assumptions

linearity
homoscedasticity
outlier testing in regression

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what is the difference between homogeneity of variance and homoscedasticity

HOV is the difference in stats and homoscedasticity is the relationship between stats

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(T/F) In correlational/relationship analyses (ex regression), the variance of the outcome variable must be about the same at all levels of the predictor variable

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if the variance is no evenly distributed what is it called

having hereoscedasticity

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when looking at a graph how can u tell the difference between homoscedasticity and heterosscedasticity

Homoscedasticity the dots are along the line of regression and for the other one the dots kinda look like a funnel it does from skinny to wide

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how are the data points arranged in linearity

in a linear pattern

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what is the easiest way to check for linearity

creat a scatterplot

what kind of residual does the outlier have

a large one

how do u find residual on a linear graph

its the distance from the dot to the actual line

what are scatterplots also used for

to check homoscedasticity and outliers

Scatterplots are also used to check for homoscedasticity and outliers. To do this…we must do what

figure out every participants residual score

to have homoscedasticity all of the ___ should be about the same

residuals

when you violate an assumption what are 2 ways that is good to do to fix it

1. analyze with bootstrapping in SPSS 2. use non parametric statistics

what are 3 bad things to do if u violate the assumption and u want to fix it

1. trim the data 2. windsorizing 3. transform the data

what is a hypothesis test

a statical method that uses the sample data to evaluate a hypothesis about a population

what is the general goal of a hypothesis test

rul out chance (sampling error) as a plausible explanation for the results from a research study

what are teh steps in a hypothesis testing

1. bathing evidence . make a case for ur study 2. state hypothesis 3. select test statistic 4. calculate test statistic 5. statistical descion - reject the HO or do no reject the HO , if reject then alternative HO is true if do not reject then null is true

the purpose of the hypothesis test is to decide between 2 exmplnations

1. the difference between the pre/post samples CNA BE EXPLAINED BY THE SAMPLING ERROR (fail o reject the null 2. he difference between the pre/post samples is too large to be explained by sampling error. (there does appear to be a treatment effect). - reject the null

what hypothesis states there is no change int he population before and after and intervention

null

in the context of an experiment, H0 predicts that the independent variable had no effect on the dependent variable…. what states this

the null

what hypothesis states there is a change in the population following an intervention

alternative hypothesis

what type of error fixes the alpha level determine

the risk for type 1 error

what is it called on a graph where it consists of outcomes that are very unlikely to occur if the null hypothesis is true

critical region

what is calculated as the difference between he pre and post means / the amount of diffference one would expect without any treatment effect

test statistic

if the test statistic is int he critical region not he graph what can we conclude and what do we reject

the difference is significant or that the treatment has a significant effect and we reject the null

 If the test statistic is not in the critical region, what can we conclude

that the evidence from the sample is not sufficient and the descision is fail to reject the null

what is the actual probability that the results occurred just because of sampling error.

the p value

when is the p value significant

when it is smaller (or equal) to the alpha level that is set in advance

if the p value is within the small end of the tails then it is ___

significant

The hypothesis test is influenced not only by the size of the treatment effect and the variability of the sample but also the size of the ____ .

sample

what do we use as a standaridized measure of effect size

cohens D

what does cohens d measure

the size of the mean difference in terms of the standard deviation

whar are 2 ways to increase the ES

1. increase the mean difference 2. decrease the SD

if r (correlation analyses) =.1 , d=.2 what kind of effect size does it have

small

if r (correlation analyses) =.3 , d=.5 what kind of effect size does it have and what does r^2 mean

medium the effect explains 9% of the total variance between the 2 samples

if r (correlation analyses) =.5 , d=.8 what kind of effect size does it have and what does r^2 mean

large the effect explains 25% of the total variance between the two

what does the larger the cohens D mean

the bigger the effect size and power increases

what is the power of the hypothesis test

the probability that the statistical test will reject the null hypothesis when the treatment does not have an effect

how do we increase our power of a hypothesis test

1- increase the ES (increase the difference between the groups or decrease the variability) 2. increase the a sample size 3. increase the alpha (.01 to .05) 4. use a 1-tail test

what has more power More difference between groups or Less difference More variability within groups or Less variability Larger sample size or Smaller sample size Alpha = .01 or Alpha = .05 1 tail test or 2 tail test

more less larger .05 1

what is used to do power before and after research

g power

if u find a significant result then u had enough ___

power

what are the 2 types of t est

independent test and dependent test

what T test Compares two means based on independent data  Ex/ Data from different groups of people

independent

what t-test Compares two means based on related data  Ex/ Data from the same people measured at different times (pre-post testing)  Ex/ Data from ‘matched’ samples or twins

dependent

independent t-tests have one ____ variable and one —— variable

idepdnet and dependent

skewness and kurtosis is okay is it is between what

+2 and -2

do we want the shapiro wilk for testing normality to be above or below .05

greater bc we do not want it to be significant

if our critical t value is 2.191 what does the t value need to be in order to be significant

greater then 2.191 ir smaller then -2.191

what do we want th number for levenes test for equality of variances to be

larger then .05 for assumptions to be met

what is smaller if the HOV is not met

DF is smaller.. less likely to reject the null

when do we use repeated measure TTest

- comparing twins or matched pairs - studies that measure the same participants twice (ex. pre/post)

when will repeated measures T-test be more powerful

if the degress of freedom (number of participants) for the 2 t-test are about the same

what is a independent T-test for a non parametric

mann - whitney U test

what is a repeated measures T-test for a non parametric

wilcoxon signed ranks test

the indepdent t test and dependent t test for non parametric are done on the ___ ___

mean ranking

if you are repeatedly testing the same dataset , you are more likely to commit what type of error

type 1

what is the bonferroni corrections

divides the alpha by # of tests u plan to run