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23 Cards in this Set
- Front
- Back
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Why would a researcher want to use regression analyses on their data? |
the line which goes through the middle of data (scatter plot) to help determine where data fits among the average. |
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2 categories of regression |
Linear/Simple Regression (only has 1 predictor Variable) Multiple Regression( can have multiple predictor variables) |
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Regression Equation |
an equation that fits the regression line and can be used to find one of the variables |
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Equation for simple linear regression |
Y= Beta(zero)+ Beta(one) (times X) |
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3 types of multiple regression |
1. Hirearchial Multiple Regression 2. Elminating Cofounding 3. Test Mediational |
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2 reasons why a researcher might choose Hierarchal Regression |
Eliminating Confounding Variables To test mediational variables |
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Multiple correlation coefficient of R, tells us? |
the degree of the relationship between the outcome of the variable and the set of predictor variables |
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What is the range of R? |
0.00- 1.00 |
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Is larger or smaller R better? |
The larger the R value the better the job will do to predict the outcome |
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What does R squared give us? |
Indication as to how well the the line fits the data |
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2 methods for testing the directionality of causal hypothesis |
1. Crosslegged panel design 2. structural equations modeling |
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Crosslegged Panel Design |
The cross of variables between two sets of already corresponding variables (Think graph with X scary movies, and violent behavior) |
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Structural Equations Modeling |
given a pattern of correlations among a set of variables, certain relationships among the variables are move logical than others. |
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Fit Index |
Used to determine how well an equation is doing or working to determine the other variables needing to be determined from a set of data |
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Path Analysis |
Path analysis is a straightforward extension of multiple regression. Its aim is to provide estimates of the magnitude and significance of hypothesised causal connections between sets of variables. |
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Latent Variable Modeling |
In statistics, latent variables (from Latin: present participle of lateo (“lie hidden”), as opposed to observable variables), are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured). |
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What doe path analysis and latent variable modeling tells about causality? |
XXX |
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What does it mean for responses to be "nested"? |
YYYY |
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What is the general idea behind multilevel modeling? |
ZZZ |
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What is the purpose of factor analysis? |
AAAA |
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What is a factor matrix? |
BBB |
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What are factor loadings and what do they tell us? |
CCCC |
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What are the three uses of factor analysis? |
DDDDD |
