correlational designs
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| Correlation | show 🗑
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| 3 Characteristics of a Correlation | show 🗑
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| The direction of the relationship | show 🗑
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| The Form of the Relation | show 🗑
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| show | A perfect relationship is +1.00 or -1.00;
the lack of a relationship is r = .00
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| The 4 Major Uses of Correlations | show 🗑
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| show | -If two variables are known to be related in a systematic way, then we can use one of the variables to predict the other variable. E.g., If we assume that SAT scores are related to college GPA, I can predict a student’s GPA in college based on their SAT
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| Validity | show 🗑
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| show | A measurement procedure is considered reliable to the extent it produces stable, consist measurements. In other words, the same individuals should score very similar scores under similar conditions.
ex: MTMM
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| show | -Many psychological therapies make specific predictions about the relationship between two variables
Ex: Behavioral Therapy suggests that depression is sustained because less events that are pleasant to the depressed patient is experienced
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| Issues in Interpreting Correlations | show 🗑
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| Correlation simple describes a relationship between 2 variables | show 🗑
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| Ceiling Effect | show 🗑
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| Floor Effect | show 🗑
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| Restriction of Range | show 🗑
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| Coefficient of Determination | show 🗑
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| show | It does not matter if the scores of either variables are transformed in to standard scores or added or multiplied by a constant, the correlation between the two variables will remain the same
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| Bivariate normal distribution | show 🗑
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| Simple Regression | show 🗑
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| show | 1.It helps describe the relationship between the two variables.
2.It identifies the center or ”central tendency” of the relation.
3.The line can be used for prediction
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| Regression line | show 🗑
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| Homoscedasticity | show 🗑
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| Standard Error of Measure | show 🗑
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| show | -In pre&posttest study,the sample's posttest mean is closer to the posttest population mean than their pretest mean was to the pretest population mean
-occurs when you have a nonrandom sample from a pop. and 2 measures that are imperfectly correlated
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| Multiple Regression | show 🗑
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| show | a statistical procedure in which the correlations between responses (variables) to questionnaires or other measures are used to discover common underlying factors; those variables that are highly correlated are “grouped” together to make a factor
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| Factor | show 🗑
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| show | the correlation between the variable and the factor; they range from -1 to +1. Normally, a variable is considered to contribute meaningfully to a factor with a loading of at least +0.30
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| Some Uses of Factor Analysis | show 🗑
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| show | -Factor analysis may be used to untangle the linear relationships into their separate patterns within items of a measure or groups of measures.
-Each pattern will appear as a factor delineating a distinct cluster of interrelated data
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| show | -Factor analysis can be useful for reducing a mass of information to an economical description
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| Structure | show 🗑
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| show | -Factor analysis can be used to group interdependent variables into descriptive categories e.g ideology, revolution, liberal voting, and authoritarianism.
-It can be used to classify nation profiles into types with similar characteristics or behavior
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| Hypothesis testing | show 🗑
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| show | -unknown domain explored through factor analy.
-Factor analy. fulfills enables the scientist to untangle interrelationships, to separate different sources of variation, and to partial out or control for undesirable influences on the variables of concern
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| Theory | show 🗑
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Created by:
rperales24
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