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30 Cards in this Set

  • Front
  • Back
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
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
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
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