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### 30 Cards in this Set

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 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