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30 Cards in this Set
- Front
- Back
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Time Series Design & E.g.
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Examining a series of observations on some variable over time.
E.g. Trends in drunk driving arrest |
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Interrupted time series E.g.
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Series of observations is compared before and after some intervention is introduced.
E.g. Trends in accidents compared before and after roadside checkpoints are established |
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Variable Oriented V.S. Case Oriented Research
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•Case Oriented- Many cases are studied to understand a small number of variables
•Variable Oriented- A large number of variables are studied for a small number of cases |
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Case Study Details
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•In depth examination of one or more few cases on many dimensions
•Case can be individual people, neighbors, correctional facilities, state •May be quantitative or qualitative |
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Levels of Measurement (4)
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•Nominal- using numbers as labels
•Ordinal- categories can be ordered on some continuum •Interval- equal distance on the scale represents equal distance on dimension being measured •Ratio- Zero point measurement, which allows measurement of the exact amount of the property being measured |
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Population and Sample Define*** Also List Sample Sub points
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Population- aggregate of all cases to which one wishes to generalize statements
Sample- Subset of the population •Representative V.S. Bias sample •Random Selection |
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Concept of Probability (Model)
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•Probability of some outcome, A, can be defined as the ratio
P (A) = Number of observations favoring A___ Total number of possible observations Probability •Must range from 0 to 1.00 •Of an impossible event is 0 •Of a completely certain event is 1.00 •Is essentially a relative frequency and concerns the likelihood that something will occur |
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Frequency Distribution (3)
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A.Frequency Distribution- Table listing the number of individuals who obtained a given score on a variable
B.Relative Frequency- Proportion of times that score occurred C.Frequency Graphs |
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Types of statistics (3)
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A.Descriptive- Summarize or describe data from a population or sample
B.Inferential- Taking measurements form a sample, then from the observations, inferring something about a population C.Chi-square, t-test, f-test |
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Measures of Central Tendency (3)
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•Mode- Most frequent attribute
•Mean- Average of scores •Median- Middle value in a ranked distribution of attributes |
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Measures of Variability
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•Range- The distance that separates the highest and lowest variable
•Sum of Squares- Total of squared deviations from the mean •Variance- Sum of squared deviations from the mean divided by the # of cases •Standard deviation- square root of the variance (average amount of variation about the mean) |
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Skewness and Kurtosis
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Skewness- Tendency for scores to cluster on one side of the mean
•Positive Skewed- Below the mean •Negative Skewed- Above the mean Kurtosis- Flatness or peakedness of one distribution in relation to another •Platykurtic- Flat •Leptokurtic- Peak |
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Standard Score Definition and Attributes
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Standard Score- Number of standard deviation units that a score occurs above or below the mean.
Normal Distribution- Bell Shaped Curve •Symmetrical about the mean •Mean=Medial=Mode •50% occurs above the mean and 50% occurs below the mean •Standard score is called a Z score |
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Hypothesis testing
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Competing Hypothesis- Alternative V.S. Null
•Defining an unexpected result- Alpha level •Failing to reject V.S. accepting the null hypothesis |
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Assumptions of a T-Test and ANOVA
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•Interval or ratio level of measurement
•If the joint frequency distribution were to be plotted, the relationship would be linear or resemble a straight line •The deviation of points from this line must be uniform, or demonstrate equal variance |
|
|
Time Series Design & E.g.
|
Examining a series of observations on some variable over time.
E.g. Trends in drunk driving arrest |
|
|
Interrupted time series E.g.
|
Series of observations is compared before and after some intervention is introduced.
E.g. Trends in accidents compared before and after roadside checkpoints are established |
|
|
Variable Oriented V.S. Case Oriented Research
|
•Case Oriented- Many cases are studied to understand a small number of variables
•Variable Oriented- A large number of variables are studied for a small number of cases |
|
|
Case Study Details
|
•In depth examination of one or more few cases on many dimensions
•Case can be individual people, neighbors, correctional facilities, state •May be quantitative or qualitative |
|
|
Levels of Measurement (4)
|
•Nominal- using numbers as labels
•Ordinal- categories can be ordered on some continuum •Interval- equal distance on the scale represents equal distance on dimension being measured •Ratio- Zero point measurement, which allows measurement of the exact amount of the property being measured |
|
|
Population and Sample Define*** Also List Sample Sub points
|
Population- aggregate of all cases to which one wishes to generalize statements
Sample- Subset of the population •Representative V.S. Bias sample •Random Selection |
|
|
Concept of Probability (Model)
|
•Probability of some outcome, A, can be defined as the ratio
P (A) = Number of observations favoring A___ Total number of possible observations Probability •Must range from 0 to 1.00 •Of an impossible event is 0 •Of a completely certain event is 1.00 •Is essentially a relative frequency and concerns the likelihood that something will occur |
|
|
Frequency Distribution (3)
|
A.Frequency Distribution- Table listing the number of individuals who obtained a given score on a variable
B.Relative Frequency- Proportion of times that score occurred C.Frequency Graphs |
|
|
Types of statistics (3)
|
A.Descriptive- Summarize or describe data from a population or sample
B.Inferential- Taking measurements form a sample, then from the observations, inferring something about a population C.Chi-square, t-test, f-test |
|
|
Measures of Central Tendency (3)
|
•Mode- Most frequent attribute
•Mean- Average of scores •Median- Middle value in a ranked distribution of attributes |
|
|
Measures of Variability
|
•Range- The distance that separates the highest and lowest variable
•Sum of Squares- Total of squared deviations from the mean •Variance- Sum of squared deviations from the mean divided by the # of cases •Standard deviation- square root of the variance (average amount of variation about the mean) |
|
|
Skewness and Kurtosis
|
Skewness- Tendency for scores to cluster on one side of the mean
•Positive Skewed- Below the mean •Negative Skewed- Above the mean Kurtosis- Flatness or peakedness of one distribution in relation to another •Platykurtic- Flat •Leptokurtic- Peak |
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|
Standard Score Definition and Attributes
|
Standard Score- Number of standard deviation units that a score occurs above or below the mean.
Normal Distribution- Bell Shaped Curve •Symmetrical about the mean •Mean=Medial=Mode •50% occurs above the mean and 50% occurs below the mean •Standard score is called a Z score |
|
|
Hypothesis testing
|
Competing Hypothesis- Alternative V.S. Null
•Defining an unexpected result- Alpha level •Failing to reject V.S. accepting the null hypothesis |
|
|
Assumptions of a T-Test and ANOVA
|
•Interval or ratio level of measurement
•If the joint frequency distribution were to be plotted, the relationship would be linear or resemble a straight line •The deviation of points from this line must be uniform, or demonstrate equal variance |
