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### 57 Cards in this Set

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 A numerical representation of information is a Statistic Weight is an example of this type of variable Continuous List 2 types of quantitative variables Discrete, continuous These variables can not be directly measured, but are inferred Latent These variables can be observed and directly measured Observable These variables are numeric in nature Quantitative These variables are nonnumeric in nature Qualitative These numeric variables measure "how many" -- they cannot be subdivided Discrete These numeric variable measure "how much" Continuous List 4 scales of measurement Nominal, Ordinal, Interval, Ratio This type of scale of measurement has discrete, qualitative variables Nominal This scale of measurement has qualities including magnitude, equal intervals, and absolute 0 Ratio This scale of measurement has qualities including magnitude and equal intervals Interval This scale of measurement only has the quality of magnitude Ordinal This scale of measurement has no special qualities, but includes things like names or lists of words. Nominal A Likert scale or rank ordered scale is an example of this type of scale of measurement Ordinal Temperature is an example of this type of scale of measurement Interval Age, height, and weight scales are examples of this type of scale of measurement Ratio You can multiply or divide items on this type of scale of measurement Ratio You can add and subtract items on this type of scale of measurement, but cannot multiply or divide Interval List 4 general ways in which researchers and test developers describe statistics Frequency, Central Tendency, Variability, Relationships This is a way to show a disorganized set of scores and place them in order, showing how many (people) obtained each of the scores Frequency Distribution This type of graph uses vertical lines and bars to portray the distribution of test scores. Histogram This variation of a histogram replaces bars with lines connecting the midpoint of each class interval. Frequency Polygon This graph gives us a better idea of the shape of the distribution as well as the frequency of scores Smoothed Frequency Polygon (or frequency curve) A frequency curve (smoothed frequency polygon) that is not symmetrical is called a Skewed curve In this skewed curve, the majority of data falls on the lower end of the scale Positively skewed curve In this skewed curve, the majority of data falls on the upper end of the scale Negatively skewed curve Three common measures of central tendency are the Mean, Median, Mode The average score in a distribution is referred to as the Mean Given these numbers, calculate the mean, median, and mode: 78,85,86,90,98,100,100,102,110,115,142,146,165 Mean = 109 Median = 100 Mode = 100 In descriptive statistics, this type of central tendency has two modes Bi-modal This measure of central tendency is the middle score, or the score that divides the distribution in half Median For symmetrical distributions, these 3 measures of central tendency are equal Mean Median Mode In central tendency measures, this is the score that appears most frequently Mode Calculate the range of these numbers: 1,2,2,3,3,3,4,4,5,5,6,6,40 39 Range is an example of the measurement of these types of descriptive statistics Variability This measure describes the average distance of test scores from the mean Standard deviation This curve has the following properties: *bell shaped, *bilaterally symmetrical, *mean, median and mode are equal to each other *Asymptotic tails *Unimodal *100% of the scores fall between -3 and +3 standard deviations from the mean Normal Curve (aka normal distribution or bell curve) 68% of a population within a normal curve falls between what standard deviations -1 and +1 standard deviations This measure of relationship indicates that two variables move in the same direction Positive correlation This measure of relationship indicates that two variables move in opposite directions Negative correlation A complete absence of a relationship between 2 variables is indicated by this number 0 A perfect positive relationship between two variables has this correlation coefficient +1 A perfect negative relationship between two variables has this correlation coefficient -1 The number of violent crimes committed is strongly positively correlated with the number of ice cream sales at a given time. Does this mean that one causes the other? No. Correlation does not mean causation. Linear relationships between two continuous variables are measured using this type of correlation coefficient Pearson Product Moment Correlation (r) A variant of Pearson's r used for finding the association between two ordinal variables and does not require a linear relationship between variables is Spearman's Rho (r) Likert scales often use this type of correlation coefficient measure Spearman's Rho (r) The degree of association between two nominal variables is assessed by this correlation coefficient measure Phi Coefficient This is used to assess the size and direction of a relationship between variables Correlation This statistical method is used in the analysis of relationships among variables for predictive purposes Regression In measurements of relationship, this is used to predict the value of one variable based on the value of another single variable Simple linear regression In measurements of relationship, this is used to predict the value of one variable based on the value of two or more independent variables Multiple regression This analyzes the relationship among variables for purposes of reducing the number of necessary variables Factor Analysis Creat demographic questionnaire for study with 5 questions/statements including: 2 nominal, 1 ratio, 1 ordinal, and 1 interval scale. Identify each type. Nominal: Relationship Status, Gender Ordinal: Rate your interest in taking this test on a scale from 1 to 5 . . . Interval: What was your temperature in Fahrenheit degrees on your last doctor's visit Ratio: What is your current weight Difference between regression, factor analysis, and spearman's rho. Explain how you would determine which analysis to conduct and provide an example of each. The all are measurement methods involving relationships between variables, however they are used for different purposes. Regression measures correlation strength and direction of a relationship between variables. There are different types of regression (Simple linear and Multiple Regression) analysis; all are used for predictive purposes. (example - if there is a strong positive correlation between IQ and grades, I could take one of the two variables and figure out the other (dependent) variable based on that. Spearman's Rho is a way to calculate the correlation coefficient (used in regression). It is used to find the relationship between ordinal variables and doesn't require a linear relationship between the variables. You would use Spearman's Rho to figure out if a correlation between 2 ordinal variables exists. Factor Analysis is used to simplify variables. You would use it to reduce the number of questions necessary on a given test while still yielding accurate results.