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44 Cards in this Set
- Front
- Back
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Statistical computations describing either the characteristics of a sample or the relationship among variables in a sample.
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Descriptive Statistics
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A logical model for assessing the strength of a relationship by askin how much knowing values on one variable would reduce our errors in guessing values on another variable.
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Proportionate Reduction of Error (PRE)
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Nominal Variables
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Lambda (teepee)
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Ordinal Variables
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Gamma (Y)
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Interval and Ratio Variables
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Pearson (r)
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A method of data analysis in which the relationships among variables are represened in th form of an equation.
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Regression Analysis
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A form of statistical analysis that seeks the equation for the straight line that best describes the relationship between two ratio variables.
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Linear Regression Analysis
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A form of statistical analysis that seeks the equation representing the impact of two or more independent variables on a single dependent variables.
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Multiple Regression Analysis
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Linear Regression
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Regression Line, Unexplained Variation, Explained Variation
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A fom of regression analysis in which the effects of one or more variables ar held constant, similar to the logic of the elaboration model.
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Partial Regression Analysis
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A form of regression analysis that allows relationships among variables to be expressed with curved geometric line instead of straight ones.
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Curvilinear Regression Analysis
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The body of statistical computations relevant to making inferences from findings based on sample observations to some larger population.
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Inferential Statistics
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Univariate Inferences: Cautions about making inferences
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1. The sample must be drawfrothe population about which inferences are being made.
2. The inferential statistics assume several things: (a) simple random sampling, (b) sampling with replacement, (c) 100 percent completion rate 3. Inferential statistics are addressed to sampling error only, not nonsampling error. |
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The probability that you could find an effect as large as (or larger than) the one in your study, even if there is no effect in the population.
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Statistical Significance (the p value)
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The Logic of Statistical Significance
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1. Assumptions regarding the independence of two variables in the population study.
2. Assumptions regarding the representativeness of samples selescted through conventional probability samling procedures. 3. The observes joint distributio of sample elements in terms of the two variables. |
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The linkage between testing for relationships in a specific set of observations (a study) and estimating how likely that relationship existsin the real world.
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A Guiding Principle
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We can never know with certainty whether a __________ in a random sample equals a _________ in the population.
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difference, dffference
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Assign a probability that a difference as large as the one you found (or larger) would occur in a population where there was no effect.
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Significants tests
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When you assume there is no relationship between X and Y.
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Null hypothesis.
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For a null hypothesis we need a statistic that:
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- Compares to the "observed" relationship to the "null" relationship
-Gives the hance that you could have a sample with that relationship if the one in the population is zero. |
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We use this test to see how likely it is that two groups are "different".
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The margin of error test
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Tests of statistical significance tell us the probability of making what type of error?
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Type 1 error: I.E. of mistakenly rejecting the null.
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In order to reduce the probability of a Type 1 error, we increas the probability of which error?
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Type 2 error, they are inversely related.
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1 in 20 times
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(p<.05)
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1 in 100 times
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(p<.01)
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1 in 1000 times
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(p<.001)
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Tests the likelihood of making a Type 1 error
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T-Test Type 1 error
X squared (chi square) test for joint distributions |
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p < .05 =
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Statistically Significant
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How to computer Chi-Squared
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For each cell in the table, subtract the expected frequency for that cell from the observed frequency. Then you square the quanitity. Then you divide the squared difference by the expected frequency.
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When you measure for judging the statistical significance of differences in group means.
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t-Test
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The value of ___ increases with the size of _______.
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t, the differences between means
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The value of ___ will increase with ________.
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t, the size of the differences between means.
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The value of t will be larger when _________.
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variations of values within each group are smaller.
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Assuming a 95% confidence (the conventional standard) an simple random samples...
When Sample Size is 1000... |
+- 3 percentage points
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Assuming a 95% confidence (the conventional standard) an simple random samples...
When Sample Size is 750... |
+- 4 percentage points
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Assuming a 95% confidence (the conventional standard) an simple random samples...
When Sample Size is 400... |
+- 5 percentage points
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Assuming a 95% confidence (the conventional standard) an simple random samples...
When Sample Size is 200... |
+- 7 percentage points
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Margin of error equals...
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+ or - 2 times the standard error.
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the null cannot be rejected if
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confidence intervals overlap. This is more likely to happen with smaller sample sizes because the margin of error is bigger.
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A form of multivariate analysis in which the causal relationship among variables are presented in a graphic format.
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Path Analysis
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An analysis of changes in a variable over time.
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Time-Series Analysis
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A method for determining the general dimensions of factors that exist within a set o measures.
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Factor Analysis
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Method of analysis in which cases under study are combined into groups representing an independent variable, and the extent to which the groups differ from one another is analyzed in terms of some dependent variable. Then, the extent to which the groups differ is compared with the standard of random distribution.
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Analysis of Variance (ANOVA)
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Analytic technique in which researchers map quantitative data that describe geographic units for a graphic display.
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Geographic Information Systems (GIS)
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