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### 80 Cards in this Set

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 Mode (NOIR) Category or score that occurs the most NOIR- Only one in which you can use Nominal level variables Highest # of repeating data Eg. (122579) the mode would be (2) (12233444567) mode=(3,4 are bi-model values) Median (OIRI) Divides an array of values into two equal halves, midpoint least effected by outliers of all three, good to use with outliers Eg. (1223456789) Median 4.5-Even amounts of numbers (345678910) Median would be 6-odd numbers Mean Ratio- the average of values,highest level of measurement best represents the central tendency use w/ largest # of cases, even w/ outliers good with large # of values Add ll values in data set & divide by the total number of values in set Eg. (3,4,5,6,7,10,11) 46/7=6.57 What are the 4 levels of measurement? Nominal Ordinal Interval Ratio Nominal Categorizes values into descrete subclasses Must have 2 or more values No numerical value Uses value categories Only use w/mode Eg, Do you believe govt. should provide health care? 1. yes 2. no 3. undecided Ordinal Numerical values, preserves rank order Can rank order values from high to low and most to least Use w/median Does not indicate absolute quantities or assume equal intervals btwn. categories Eg. How would you rate your social worker? 1 very good 2. good etc. Interval Can use to rank order diff. measurements of a variable Places the value for the variable on an equally spaced continuum Preserves rank order Ratio preserves rank order, unit differences and fixed zero points Skewed Distribution Symmetrical, ends do not tapper off Positively skewed= tail curves to the right Negatively skewed= tail curves to the left Normal distributions Symmetrical bell shaped curve that often arises when a trait is composed of a large number of random independent factors; the curve possesses a specific mathematical formula Interval & Ratio Level Normal Curve Interval or ratio level variable that is normally distributed represented by a bell shape curve The Mode,Median & Mean all occur at the highest point in the center of the distribution Graph types bar line pie histogram Pareto frequency polygons stem & leaf Graphs can communicate the bigger picture. Improve communication Is possible to lie w/graphs like other analysis Bar Graph NOIR NOIR The highest of each bar reflects the frequency of the value category Bars of equal width, do not touch each other Line Flip the access & put the frequency on the bottom Pie-Charts NOIR NOIR used w/all levels use "absolute percents" when making pie chart Histogram OIR Looks like bar, but the bars touch each other Uses height of bar to reflect the frequency of a value or value category Bars can be various widths, however, if the are "equal" must be Ordinal Pareto IR Portrays rank ordering of values of a variable by frequency in descending order. Reflects "cumulative frequencies & cumulative percentages" of cases at any point or when added to the group Frequency Polygon IR picks points to connect to Stem & Leaf IR Displays all of the actual case values in he distribution of a variable Stem= 1st or 2nd digits of a persons age Leaf= 2nd digit of a persons age Which graphs are used more often? Frequency Polygon & Histogram Central Tendencies Shows what is typical with the data Trimmed Mean Designed to minimize the effect of a few extreme outliers. Combines the best features of Mean & Median Usualy top 5% & the bottom 5% of values in an array are thrown out. The remaining 90% of values are averaged. *This is trimmed mean* Use w/small amount of values & outliers **Less than 30 points of data use trimmed mean Weighted Mean The weighing fo numbers in order to arrive at a value that is more meaningful for the data set than either the arithmetic mean or trimmed mean. A. Summarized data B. Provide a common reference point to compare 2 groups of data Correlation The degree of association btwn. or among variables, figuring out trends effectiveness, some may be weak & some may be strong or moderate (does not predict what other variables will do) Covariance The degree to which certain values of one variable are found to be associated with certain values of other variables Scattergram Displays the correlation btwn. 2 or more variables, it simultaneously portrays the info. for 2 values- not more than 2 values. If you see a pattern form it could mean that a relationship exists What is the highest correlation? +1.0 What is the weakest correlation? -1.0 What is NO correction? 0 (zero) How do you determine if a relationship exists? -1.0 0 +1.0 (these are the numbers you look at to determine if a relationship exists, if 2.5 is your #, it is wrong because you can't go over 1.0 or -1.0 1 Tail Test Predicts relationship btwn. variables, predicts a specific direction Eg. Watching hockey increases beer consumption (A relationship btwn. the two, it takes it sin some sort of direction either improves, or increases or diminishes etc.) 2 Tail Predicts relationship, but no specific direction Eg. Gender is related to job satisfaction levels (does not predict whether M or F will be found to have higher levels of job satisfaction) Proves some sort of relationship btwn. two variables, does not increase or decrease Null Test x=y, says there is no difference btwn. the population & the sample population Dichotomous Variable Has 2 value categories Eg. Gender, T/F Binary Variable Assigns numerical value to categories of 1 or 0 to indicate presence, or absence of variable. Used when coding Dummy Variables Created by converting a qualitative into a binary variable, breaks it into sub-sets. Eg. gender=make & female Information analyzed data Data numbers or scores generated by research study, measurements collected in a research study Constant Does not differ in quality or quantity Conceptualization 4 step process used to narrow down list of potential variables by identifying those that must be measured to get answers to our research question Steps in Conceptualization Process 4 S's Select-most impt. Variable to study State- what is mfeant by each variable Specify-how each variable is to be measured State the value- of categories or values t hat each variable can assume Operationalization Specifying exactly how to measure the variable that we have conceptualized, survey/observation Reliability The degree of consistency of measurement Addresses the question=To what degree does the measurement of a variable produce consistent results? Validity The desire to whixch a measurement instrument accurately measures what it claims to measure* using measurement instruments Research Hypothesis A statement of a relationship btwn. or among variables-often stated in the future tense because it predicts what will be found-expresses what we believe to be true Eg. Smoking Pot (IV) compromises the brain Ability to function properly (DV) Independent Variable (IV) The variable that is predicted to do the influencing Dependent Variable (DV) Believed to be influenced by the (IV) Predictor Variable (PV) Used for prediction Criterion Variable (Outcome Variable) Variables whose values we hope to predict Discrete Variable Can take on only an finite number of variables whose values Dichotomous Variable Has 2 value categories Eg. Gender, T/F, M/F Binary Variables Assigns numerical value to categories of 1 or 0 to indicate presence Used when coding Dummy Variable Created by converting a qualitative variable into a binary variable breaks it into sub-sets Eg. Gender=male & female Descriptive Analysis Nominal variables Reduce large amounts of data to a simpler form, easier to understand Inferential Analysis Ratio Variable Used when *sample* is drawn from population & not the *total* population Frequency Number of observations falling into a cell or value category of a specific variable Frequency Distribution Table or graph, shows the # of times (frequency) w/which different variables occur in a group of observations Absolute Frequency Number of times each value occurred *this total should always equal the last cumulative frequency number Cumulative Frequency Adds the absolute value together Absolute Percent Uses the absolute frequency and divides it by 10 and then multiplies it by 100, turn it into %, should equal 100 Cumulative Percent Adds the absolute %, always want it to be 100% Valid Percent incudes what? The missing values, *Percent* does not. Grouped Frequencies What is meaningful group? Reduces the # of values to a smaller #, easier to understand, while not loosing meas Descriptive Analysis Nominal variables Reduce large amounts of data to a simpler form, easier to understand Inferential Analysis Ratio Variable Used when *sample* is drawn from population & not the *total* population Frequency Number of observations falling into a cell or value category of a specific variable Frequency Distribution Table or graph, shows the # of times (frequency) w/which different variables occur in a group of observations Absolute Frequency Number of times each value occurred *this total should always equal the last cumulative frequency number Cumulative Frequency Adds the absolute value together Absolute Percent Uses the absolute frequency and divides it by 10 and then multiplies it by 100, turn it into %, should equal 100 Cumulative Percent Adds the absolute %, always want it to be 100% Valid Percent incudes what? The missing values, *Percent* does not. Grouped Frequencies What is meaningful group? Reduces the # of values to a smaller #, easier to understand, while not loosing measurement precision Variability Indicates the degree of variation among and value categories 5 different measures of variability RIMVS Range Interquartile range Mean deviation Variance Standard Deviation *Uses only Interval & Ratio to measure variability *Communicated best in a frequency distribution or graph, such as a bar chart Range The distance the encompasses all values within the data set Expressed as a formula: Range=maximum value-minimum value +1 Maximum Value The value of the case w/ the largest value of the variable Max age=35 MIn age=30 The range would be 6 (35-30+1=6) There are potentially 6 different ages(or values) that are included w/in the range: 35,34,33,32,31 & 30 Minimum Value The value of the case w/the smallest value of the variableMax age=35 MIn age=30 The range would be 6 (35-30+1=6) There are potentially 6 different ages(or values) that are included w/in the range: 35,34,33,32,31 & 30 What is an Outlier Data points that are far removed from and numerically distant from the rest of the points