- A third (or fourth or fifth) variable that represents an alternative explanation for a two-variable relationship

- shows whether a bivariate relationship holds up to alternative explanations

-takes into account the effects of variables other than the primary IV and DV.

-the numerical values just "name" the attribute

-no ordering of the cases is implied

-can only come up with a mode

-can't have a meaningful mean or median

         -example: jersey numbers in basketball

              -a player with #30 is not more of anything than player with #15, and is certainly not twice whatever #15 is.

-the attributes can be rank-ordered

-distances b/t attributes do not have any meaning

-Example: survey coding of Educational Attainment 0=less than H.S, 1=some H.S, 2=H.S degree, 3=some college, 4=college degree

            - Higher #'s mean more education. But the distance from 0 to 1 is not the same as 3 to 4

-the distance between attributes has meaning

-Example: when we measure temperature, the distance from 30-40 is the same distance as 70-80. The interval b/t values is interpretable. 

-Makes sense to compute an average of a interval variable 

-Ratios don't make sense: 80is not twice as hot as 40

-there is always an absolute zero that is meaningful

-most "count" variables are ratio

       -Example: weight

       - Example: the number of clients in the past six months

            - you can have zero clients and have it be meaningful

-population parameters

-statements/assumptions about unobserved data from a population

-data comes form a type of probability distribution, while making infreences

-Example: t-tests

- interval or ratio

-uses info about the mean and deviation

-no assumption of normal distribution

-non-sample

-usually nominal or ordinal

-example: the various forms of Chi-Square tests

-essentially a null category, since almost all statistical tests assume one thing or another about the properties of the source population(s)

-single variable in isolation

-deal with only one variable

-Example: age: the researcher would look at how many subjects fall into the given age attributes

-commonly used int he first, descriptive stages of research

-describes the relationship b/t two variables together

-shows statistical relationship between variables

-things that tend to appear together

-Example: water pollution in a stream and people who drink the water- statistical relationship b/t two variables: pollution in water and health of people drinking it

-there is no different between variables

-the mean for group 1 is the same as the mean in group 2 (X1=X2)

-one group is larger than the other

-in 3 groups, x is larger than y and x therefore it is an alternative 

-the p-value is always between 0 and 1 and it tells you the probability of the difference in your data due to sampling error

-the null hypothesis is rejected if the p-value is less than the significance or p level 

       - (most often set at 0.05 or 95%)

-.000 does not mean 100% significant, instead write as <.005 or <.001

-occurs when we say that a relationship exits when in fact none exists.

-falsely rejecting a null hypothesis

-occurs when we say that a relationship does not exist, when in fact it does

- falsely accepting a null hypothesis

-the process used to determine whether the null hypothesis is rejected or in favor of the alternative research hypothesis

-statistical significance test eliminates the possibility that the results arose by chance, allowing a rejection of the null hypothesis (H0). 

-if the sample mean would be different from the population mean but that it would be different in a specific direction, it would be lower.

-the region of rejection is entirely within one tail of the distribution (right or left)

-hypotheses predicting that only 1 value will be different from another, w/o predicting which will be higher (left of right). 

-test statistic in either tail of the distribution (+ or -) leads to rejection of the null of no difference.

-split b/t both tails of the distribution, .025 in the upper and .025 in the lower bc your hypothesis specifies only a difference, not a direction.

-A straight line drawn through the center of a group of data points plotted on a scatter plot.

-shows whether these two variables appear to be correlated.

-The expected mean value of Y when all X=0. 

-If X never = 0, then the intercept has no real meaning and there is no interest in the intercept. bc it doesn’t tell you anything about the relationship between X and Y.

-the value at which the fitted line crosses the y-axis.

-the slope is the measure of the steepness of a line

-To find the slope, you divide the difference of the y-coordinates of a point on a line by the difference of the x-coordinates.

       -y2-y1/x2-x1

       - =rise/run

- used to compare strength of the effect of IV on the DV. 

-the IV with the largest Beta has the strongest effect

-answers the question of which of the IV's has a greater effect on the DV in a multiple regression analysis, when variables are measured in different units of measurement. (ex: income measured in dollars and family size measured in # of individuals).

-1. To study or Not to Study

-2. How you Study

-3. Who you're studying

-4. Questions about What you're studying

-5. Questions about your relationship to the research 

-6. Questions about Integrity of your research

-7. Questions about Purpose of your research

-a study of homosexual encounters in public places (bathrooms)

-used to be known as "tea-rooming" 

-observed and describe various social cues (body language, hand language, etc.) 

-The encounters usually involved three people: the two engaged in the sexual activity, and a look-out, called "watchqueen" in slang

-compares observed frequencies to expected frequencies 

-nominal and ordinal

-ex: Is the distribution of sex and voting behavior due to chance or is there a difference between the sexes on voting behavior? 

-looks at differences between two groups on some variable of interest

-the IV must have only two groups (male/female, undergrad/grad) 

-interval or ratio

-ex: Do males and females differ in the amount of hours they spend shopping in a given month? 

-tests the significance of group differences between two or more groups

the IV has two or more categories

-only determines that there is a difference between groups, but doesn’t tell which is different

-interval or ratio

-EX: Do SAT scores differ for low-, middle-, and high-income students? 

-measures the linear relationship between two interval/ratio level variables.

-Pearson's r is always between -1 and +1, where -1 means a perfect negative, +1 a perfect positive relationship and 0 means the perfect absence of a relationship.