Term
| What are the 5 steps of the literature review process? |
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Definition
Step 1. Develop a focused research
question based on an area of interest
Step 2. Develop a list of keywords and
construct a search strategy
Step 3. Choose an appropriate database
Step 4. Conduct your search
Step 5. Evaluate results |
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Term
| What does significance at p < .05 mean? |
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Definition
| The probability of the data given the null hypothesis is < .05 |
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Term
Is this statement correct?
The probability of the data given the null hypothesis is < .05 |
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Definition
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Term
Is this statement correct?
The probability that the null hypothesis is true is < .05 |
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Definition
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Term
Is this statement correct?
The probability that a Type I error was made just in rejecting H0 is
< .05 |
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Definition
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Term
Is this statement correct?
The probability that the same result will be found in a replication
study is > .95 |
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Definition
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Term
Is this statement correct?
The probability that the result is due to chance is < .05 |
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Definition
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Term
| the Bigger your correlation and sample size the ________ the t value is likely to be |
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Definition
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Term
| What does a p value tell you? |
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Definition
| Given that the null is true, what is the probability of these (or more extreme) data occurring. |
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Term
| What other statistics can be used other than p values? |
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Definition
| Effect sizes, confidence intervals |
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Term
| why might p values be low? |
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Definition
| either large sample sizes or large effect sizes |
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Term
a small effect can be statistically
significant in a ______ sample |
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Definition
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Term
| a large effect can fail to be statistically significant in a ________ sample |
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Definition
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Term
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Definition
| rejecting the null hypothesis when the null is true |
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Term
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Definition
| failing to reject null hypothesis when the null is false |
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Term
| The likelihood of finding significance is proportionate to the __________. |
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Definition
| effect size (the size of the difference you are looking to detect) |
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Term
| When your effect size is large (a big underlying difference) you need ________ people to detect a difference |
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Definition
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Term
| When can a small effect be statistically significant? |
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Definition
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Term
| When can a large effect be statistically insignificant? |
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Definition
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Term
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Definition
| the probability of getting a statistically significant result when there is a real effect in the population. In other words, when the null hypothesis is false. |
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Term
| What can power range from? |
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Definition
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Term
| What do Higher power values indicate? |
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Definition
high power, higher probability of
finding an effect. |
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Term
| What are the factors that affect power? |
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Definition
–Study design (stronger manipulations have more
of an effect)
–Level of statistical significance (this can be set to
levels other than .05 ahead of time)
–Type of statistical analysis used
–Measurement error
–Sample size
–The actual magnitude of the effect (effect size) |
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Term
| What is the null hypothesis? |
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Definition
The null hypothesis states that the
experimental group and the control group are not different with respect to [a specified property of interest] and that any difference found between their means is due to sampling fluctuation |
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Term
| What is The “Crud Factor” |
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Definition
| Almost all of the variables that we measure are correlated to some extent |
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Term
| What is the Inverse probability error |
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Definition
| This error states that p = likelihood that the null hypothesis is true |
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Term
| What is the Odds against chance fallacy |
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Definition
| This error states that p is the probability that the results happened due to chance |
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Term
| What is the Type I error fallacy |
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Definition
| This error states that If p < .05 then the probability that you are wrong in rejecting the null is less than 5% |
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Term
| What is the Replication fallacy |
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Definition
| This error states that p is the probability of finding the same result in another study. If p < .05 the probability of finding the same results in another study is 95% |
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Term
| What is the Magnitude Fallacy |
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Definition
| This error states that p value is an index of the magnitude of an effect |
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Term
| What is the Meaningfulness (causality fallacy) fallacy |
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Definition
| This error states that Rejection of null hypothesis confirms the research hypothesis behind it |
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Term
| What is the Success/Failures fallacy |
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Definition
| This error states that rejection = success, and failure to reject = failure |
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Term
| What is the Zero (equivalence) Fallacy |
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Definition
| This error states that Failure to reject the null means that the effect is zero |
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Term
| What is the Sanctification fallacy |
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Definition
This error states that thinking that there is a big distinction between .049 .06, .056, .05.
You need to remember that with p values we are dichotomizing a continuum |
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Term
| What is the Reification fallacy |
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Definition
| This error states that Replication success is determined by examining p values. Failure to replicate is evidenced when an effect is significant in one study but not in another. |
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Term
| The null is always assumed to be _____ |
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Definition
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Term
| _____ refers to the probability of the data |
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Definition
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Term
| A standardized variable has been transformed so that its mean is __ and standard deviation is __ |
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Definition
| mean is 0 and standard deviation is 1 |
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Term
A covariance is an example of an _____ estimate.
a) unstandardized
b) standardized |
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Definition
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Term
| What does a pearson correlation estimate? |
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Definition
| the degree of linear association between two continuous variables |
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Term
| what does Multiple correlation squared, R2 tell us? |
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Definition
| the proportion of variance in Y that is accounted for by using X1 and X2 simultaneously |
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Term
| What is the range of pearson r correlation? |
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Definition
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Term
| what does r squared indicate? |
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Definition
| the proportion of variance that is explained by x |
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Term
| Why is range -1 to 1 theoretical for pearson correlation r? |
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Definition
| Range could be restricted due to different factors/conditions. |
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Term
| If the relation between X and Y is nonlinear does it affect rxy (correlation)? |
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Definition
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Term
| If The variance of either variable is narrow (restricted) does it affect rxy (correlation)? |
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Definition
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Term
| If The shapes of the frequency distribution are different (e.g., one is positively skewed and the other negatively skewed) does it affect rxy (correlation)? |
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Definition
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Term
| If The reliability of X or Y scores are low does it affect rxy (correlation)? |
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Definition
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Term
| Do The means of X and Y affect rxy (correlation)? |
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Definition
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Term
| If a constant increase of 10 was added to X would it affect correlation? |
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Definition
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Term
| What are the assumptions of pearson correlation r? |
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Definition
• Residuals are defined as having a mean of 0
• Are independent
• Normally distributed (aka homoscedasticity) |
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Term
| What is partial correlation? |
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Definition
| The technique of a partial correlation takes a third variable into account |
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Term
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Definition
| If the observed relation between X and Y is due to one or more common causes the association between X and Y is said to be spurious |
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Term
The relation between shoe size
and vocabulary among children is .50; however, this isn’t surprising because they are both caused by a third variable: age. What is this third variable called? |
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Definition
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Term
| what does "controlling for w" do |
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Definition
| removes the effect of a third variable W from both X and Y and reestimates their association |
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Term
What do part correlations and semi
partial correlations do? |
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Definition
| they partial out the effect of external variables from either Y or X |
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Term
| what does regression analysis do? |
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Definition
| fits a straight line through such a distribution |
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Term
What does this equation refer to?
Y = B0 + B1X + E |
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Definition
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Term
What does B1 refer to in this equation?
Y = B0 + B1X + E |
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Definition
| slope, It tells us how much of a change in Y is associated with a one-unit change in X |
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Term
What does E refer to in this equation?
Y = B0 + B1X + E |
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Definition
| The error variable is a hypothetical variable representing all aspects of Y that are not predicted by X |
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Term
| What does a slope of 2.3 mean? |
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Definition
| it means that a one-unit change in X is associated with 2.3 units of change in Y |
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Term
Is this equation standardized or unstandardized?
Y = B0 + B1X + E |
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Definition
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Term
Is this equation standardized or unstandardized?
Y = ß0 + ß1X + E |
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Definition
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Term
| The more scatter of data points there is, the _____ the explanation. |
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Definition
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Term
| What is multiple regression? |
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Definition
a regression with more than one
predictor |
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Term
In this equation, what is B1?
Y = B0 + B1X1 + B2X2 + E |
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Definition
| B1 is the effect of X1 on Y controlling for X2 |
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Term
In this equation
Y = B0 + B1X1 + B2X2 + E
if B1 were equal to 1.6 what would
it mean? |
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Definition
| it would mean that one unit change in X1 is associated with a 1.6 unit change in Y, holding X2 constant. |
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Term
| What does multiple correlation, R tell us? |
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Definition
| R tells us how well the optimally weighted sum of the predictors (X and X) explains Y |
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Term
| Where does the value of R lie between? |
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Definition
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Term
| What does Multiple correlation squared, R2 tell us? |
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Definition
| R2 tells us the proportion of variance in Y that is accounted for by using X1 and X2 simultaneously |
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Term
| How can R can be tested for statistical significance |
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Definition
| this is done with an F statistic |
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Term
| In a multiple regression, how are the individual B or Betas tested for statistical significance? |
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Definition
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Term
| what does R squared = .633 mean? |
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Definition
| tells us that X accounted for 63% of the variance in Y |
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Term
| What is a categorical predictor? |
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Definition
| Participants either belong to one group or the other. Example: We code females with the number 0 and males with the number 1 |
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Term
| When a regression is run with a dummy coded variable, what is the B of the dummy coded variable testing |
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Definition
| the B of the dummy coded variable is testing the mean difference between groups |
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Term
| What is the term for when the two (beta and bivariate correlations) have different signs |
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Definition
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Term
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Definition
suppression occurs when the
absolute value of a predictor’s beta weight is
greater than its bivariate correlation with the
criterion. |
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