Statistics For Social Research

Front 1

Slope efficient
Correlation coefficient

Back 1

Correlation coefficient represents the direct effect of the associated IV on Y

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Independence

Back 2

Variables independent if unrelated to each other

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Beta weights

Back 3

Standardized partial slopes

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Partial slope coefficient

Back 4

Estimates the difference in DV associated with a one unit difference in an IV when controlling for the effects of the other IVs

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Example
Ŷ = 5078 + 732 X
(bivariate regression equation)

Back 5

The slope estimate (which in this case is a value of 732) indicates the AVERAGE change in Y associated with a UNIT change in X
For our example: if we had a person who had 0 years of formal education, their income would be, on average, = to $5078.00

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where our slope coefficient = 732

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that mean, on average, for each additional year of education, the person will see an increase in their income of $732

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So if we have NO relationship, what should our slope coefficient be?

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ZERO…meaning no effect on the DV at all.H0:X is NOT associated with Y (therefore the slope = 0 in the population)
H1:X IS associated with Y, therefore the slope is NOT zero in the population

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If we CAN reject the null,

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what we are saying is that the slope estimate IS significantly different from 0 at a .05 level (95% level of confidence)

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Purpose of one way ANOVA and how it’s executed (logic)

Back 9

Designed to be used with interval ratio-level dependent variables.
The test compares the amount of variation between categories with the amount of variation w/i categories. The greater the differences between categories (means) relative to the differences w/i categories (sd)the more likely the null hypothesis of no difference is false and ca be rejected.

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Intercept (slope)

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the point where the regression line “intercepts” or intersects the Y-axis
-- It estimates the average value of Y when X is equal to zero

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R – square
(Coefficient of determination

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accounts for the proportion of variation in the dependent variable “explained” by the independent variable
The range of R2 values: 0.0 to +1.0

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R – square
(Coefficient of determination)
In our example, R2 = .56

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We can say: our model, where education is the independent variable, accounts for 56% of the variation in salary, the dependent variable

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What does model fit mean in regression analyses?

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used to model the relationships between a response variable and one or more predictor variables,

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What is the value of measure of association when assessing variable relationships?

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Measure of association help us trace causal relationships among variables, and they are our most important and powerful statistical tools for documenting, measuring, and analyzing cause-and effect relationships

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What are the strengths and weaknesses of multiple regression analysis

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Muliple regression and correlation are powerful tools for analyzing the interrelationship among three or more variables.

They are not cheap. They assume that each independent variable has a linear relationship with the dependent variable. They assume there is no interaction among the variables in the equation. They assume the independent variables are uncorrelated with each other.

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What is the difference between a true experiment and a quasi experiment?

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True- uses randomized choice, selecting subjects and methods in a way that prevents bias in results Quasi-doesn’t use proper random assignment, recruitment of people can cause bias.

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What are the strengths and weaknesses of a cross sectional research design approach?

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association by itself is not proof that a causal relationship exists. Causation and association are two different things.

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Within group variance

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Pattern of variation within each category

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Between group variance

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Measure of variation in score between categories

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Bivariate relationship

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relationship between two random variables

(zero order)

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Partial Correlation

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Examines how a bivariate relationship (i.e. a relationship between two variables) is affected by a third variable
Distinctions between direct relationships, spurious or intervening relationships, interactive relationships

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If the partial correlation coefficient differs from the zero-order coefficient

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we conclude the third variable has an effect on the bivariate relationship

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Partial Correlation
when you introduce a THIRD variable to check to see

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see if you have a direct or a spurious relationship

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Partial correlations with a control variable added are called

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first-order correlations and are symbolized as ryx.z where the variable to the right of the dot is the control variable

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Interpreting the partial correlation result:
The first order partial correlation (ryx.z = 0.43) is lower than the zero-order correlation (ryx = 0.50), but

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the difference is slight
This suggests a direct relationship: regardless of SES, husbands’ household work increases with the number of children in the house

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Interpreting the partial correlation result:
If the partial correlation is much different (e.g. lower) than the original zero-order correlation, ...If the partial correlation is much higher than the zero-order r value,

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then we likely either have a spurious relationship between X and Y, or an intervening relationship...another causal structure may be at work