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# Stats Exam 1

Statistics set of methods and rules for organizing, summarizing and interpreting data
Data observations or measurements
Population the set of individuals of interest in a particular study
Sample a set of individuals selected from a population, usually intended to represent the population in a research study.
Parameter characteristics (usually numerical) of a population, usually derived from the measurements if the individuals in the sample (mean, median, mode)
Statistic characteristic (usually numerical) of a sample, usually derived from measurements of the individuals in the sample (estimate because it is a sample)
Descriptive Statistics summarizes, organizes and simplifies data, a statistical procedure
Inferential Statistics uses sample data to make generalizations to a population
Sampling Error the discrepancy that exists between a sample statistic and a population parameter, the natural differences that exist by chance
Dependent Variable the one that us observed to assess the effect of the treatment
Independent Variable the variable that is manipulated by the researcher
Confounding Variables (Confounds) a variable that unintentionally is allowed to vary with the independent variable and therefore the researcher does not know if the independent variable or the confounding variable causes the results
Construct a hypothetical concept used in theories, internal attributes or characteristics that cannot be directly observed but are useful for describing and explaining behavior
Operational Definition describes a set of operations or procedures for measuring a construct and it defines the construct in terms of the resulting measurement
Discrete Variable consists of separate indivisible categories. No values can exist between two neighboring categories, restricted to whole countable numbers
Continuous Variable there are infinite number of possible values that fall between any two observed values, it is divisible into an infinite number of fractional parts, usually rounded off
Scales of Measurement the complete set of measures and the relationships between the categories
Nominal Scale consists of a set of categories that have different names, numbers represent categories (ex: 1= female 2=male)
Ordinal Scale consists of a set of categories that are organized in an ordered sequence, rank (ex: 1= first place 2= second place 3= third place)
Interval scale consists of ordered categories that are all intervals of exactly the same size, the zero point is arbitrary and does not indicate a zero amount of the variable being measured
Ratio Scale an interval scale with the additional feature of an absolute zero point
Quasi experiment an almost experiment
Differential Research study in which you can not randomly assign people to groups, they fall into specific groups because of certain characteristics they have (ex: gender)
Time Series Research measure you at 1 time then give you some treatment then measure you a second time but you can't control what happens between that time, you don't know what other factors come in between that treatment
Frequency Distribution an organized tabulation of the number of individuals located in each category on the scale of measurements, takes disorganized set of scores and places them in order from highest to lowest, grouping together all individuals with the same score.
Measures of central tendency score that best represents frequency distribution (mode, median, mean)
4 times to use median ordinal data, skewed distribution, open ended distribution, undetermined values
Measures of Variability gives you a number that indicates how far apart the numbers are from one another
Range and why is it not often used? tells you distance from very lowest point to the very highest point and often not used because unstable and insensitive