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Political Statistics
Mid Term
| Question | Answer |
|---|---|
| Research Design | An overall set of procedures for evaluating the effect OF an independent variable ON a dependent variable. |
| hypothesis | A testable statement about the empirical relationship between cause and effect. (it tells us what we should find when we look at the data) |
| variable | An empirical measurement of a characteristic. |
| dependent variable | Represents the effect in a causal explanation. |
| independent variable | Represents the causal factor in an explanation. |
| concept | An idea or mental construct that represents phenomena in the real world. |
| conceptual definition | Describes the concept's measurable properties and specifies the unit(s) of analysis. |
| operational definition | Describes the instrument to be used in measuring the concept and provides a procedural blueprint; a measurement strategy. |
| unit of analysis | The entity we want to analyze (person, city, state, county, country, etc.) |
| Ecological Fallacy | Arises when aggregate-level phenomena is used to make inferences at the individual level. |
| systematic measurement error | Introduces consistent, chronic distortion into an empirical measurement. (test anxiety, verbal skills, etc.) (durable) |
| random measurement error | Introduces haphazard, chaotic distortion into the measurement process. (fatigue, commotion, etc.) (not durable) |
| reliability | A consistent measure of the concept. (gives the same reading every time) (no random error) |
| validity | Records the true value of intended characteristics and does not measure any unintended characteristics. (measures what you want to measure) (no systematic error) |
| Levels of Measurement | Nominal-Level Variables Ordinal-Level Variables Interval-Level Variables |
| Central Tendency | The typical average. (Center) -Mean -Median -Mode |
| Dispersion | The variation of cases across its values. (Spread) -Range -Variance -Standard Deviation |
| Frequency Distribution | A tabular summary of a variable's values. |
| Sampling Distribution | The distribution of a statistic (ie mean) for all possible samples within a population. |
| Non-Standard Distribution | A distribution of data points. |
| Standard (Standard Normal) Distribution | 1) All the properties of Normal Distribution 2) A distribution of Z-Scores 3) mean is always 0 and st dev is always 1 |
| Probability Distribution | A statistical function describing all possible outcomes that a random variable can take within a given range. |
| test group | Composed of subjects who receive a treatment that the researcher believes is causally linked to the dependent variable. |
| control group | Composed of subjects who do NOT receive the treatment that the researcher believes is causally linked to the dependent variable. |
| experimental design | Ensures the test group and control group are the same in every way except one-the independent variable. |
| random assignment | Occurs when every participant has an equal chance of being in the control group or the test group. |
| selection bias | Occurs when nonrandom processes determine composition of the test group and the control group. |
| internal validity | Within the artificially created conditions, the effect of the independent variable ON the dependent variable is isolated from other plausible explanations. |
| external validity | When the results of the study can be generalized; its findings can be applied to situations in the natural world. |
| direct relationship | Runs in a positive direction; An increase in the IV is associated with an increase in the DV. (ordinal) |
| inverse relationship | Runs in a negative direction; An increase in the IV is associated with a decrease in the DV. (ordinal) |
| linear relationship | An increase in the IV is associated with a consistent increase or decrease in the DV. (positive or negative) (interval) |
| curvilinear relationship | Relationship b/w IV and DV depends on which interval or range of the IV is being examined. (not linear) (interval) |
| rival explanation | An alternative cause for the dependent variable. |
| controlled comparison | Accomplished by examining the relationship b/w the IV and the DV, while holding constant other variables suggested by rival explanations and hypotheses. |
| compositional difference | Any characteristic that varies across categories of an IV. (Dems and Repubs vary by gender, age, income, etc.) |
| spurious relationship | The relationship b/w IV and DV weakens, perhaps dropping to zero. (XYZ scenario) |
| additive relationship | The IV and the control variable (Z) make meaningful contributions to the explanation of the DV. (XYZ scenario) |
| interaction relationship | The relationship b/w the IV and the DV is not the same for all values of the control variable (Z). (XYZ scenario) |
| zero-order relationship (aka gross or uncontrolled relationship) | An overall association b/w two variables that does not take into account other possible difference b/w the cases being studied. |
| controlled comparison table | Presents a cross-tabulation b/w an IV and a DV for each value of the control variable. |
| controlled effect | A relationship b/w a causal variable and a DV within one value of another causal variable. |
| partial effect | Summarizes a relationship b/w two variables after taking into account rival variables. |
| population | The universe of cases the researcher wants to describe. |
| sample | Number of cases drawn from a population. |
| random sampling error | The extent to which a sample statistic differs, by chance, from a population parameter. |
| standard deviation | Summarizes the extent to which the cases in an interval-level distribution fall on or close to the mean of the distribution. |
| range | The maximum actual value minus the minimum actual value. |
| variance | The measure of the dispersion of values around the mean. |
| Central Limit Theorem | Rule that states if you take an infinite number of samples from a population, the means of those samples would be normally distributed. |
| Normal Distribution | Distribution used to describe interval-level variables. |
| standardization | Occurs when the numbers in a distribution are converted into standard units of deviation from the mean of the distribution. |
| Z Score | A standardized value. deviation from the mean/standard unit |
| probability | The likelihood of the occurence of an event or set of events. |
| Student's t-distribution | A probability distribution that can be used for making inferences about a population mean when the sample size is small. |
| degrees of freedom | Refers to a statistical property of a large family of distributions. =sample size (n) minus the number of parameters being estimated by the sample. |
| sample proportion | The number of cases falling into one category of the variable divided by the number of cases in the sample. |
| Empirical Rule | A statistical rule stating that for a normal distribution, almost all data will fall within three standard deviations of the mean. One St Dev=68.3% Two St Dev=95.4% Three St Dev=99.7% |
| standard error | The standard deviation of a sampling distribution. (sigma/N) |
| Basic Law of Probability | Given that all outcomes of an event are equally likely, the probability of any specified outcome is equal to the ratio of the number of ways that that outcome could be achieved to the total number of ways that all possible outcomes can be achieved. |
| 3 Rules of general probability | 1) The probability is b/w 0 and 1 2) Sum of all probabilities in a sample space will always = 1 3) Probability – 1 = complement of probability |
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