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are simply describing what is or what the data shows. 


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you are trying to reach conclusions that extend beyond the immediate data alone (generalize). 


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Descriptive statistics are Are a means of organizing and summarizing data through 

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Tables Graphs Numerical summary measures 


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identity, magnitude, and exponential 


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identity, magnitude, exponential, and true value 


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categorical and scale/metrics 


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Two types of categorical data 

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all subjects assigned a value, those with the same value are the same on that characteristic but categories are not necessarily the same. 


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Scale data can be converted into 

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Persons or objects with the same value are the same on some attribute.
The values of the scale have no 'numeric' meaning 


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People or objects with a higher scale value have more of some attribute.
The intervals between adjacent scale values are indeterminate.
Scale assignment is by the property of "greater than," "equal to," or "less than." 


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Intervals between adjacent scale values are equal with respect the attribute being measured. 


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Using the wrong type of data may violate the assumptions of statistical tests which then makes interpreting your results __________/________ 

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List of variables Assigned values
Level of measurement 


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Facilitates data entry into SPSS 


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two types of nonparametric data 

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Two types of parametric data 

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the set of elements we are planning study 


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Organizes ungrouped data by indicating the number of times (frequency) each score was obtained 


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Frequency distribution is ranked from 

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Frequency distribution, table of results that shows the 

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number of times a characteristic comes up 


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A type of bar graph, in which lines or bars represent each score or set of scores rather than lines connecting the frequencies of each score 


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The degree to how the scores fall disproportionately to one end of the distribution 


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normal, unimodal and symmetric 


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, , , , , , , , , , , , 

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Three measures of central tendency 

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The score in the distribution that occurs most frequently 


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Middle score of the distribution that divides the distribution into halves containing an equal number of scores 


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Arithmetic average of ALL the scores in the distribution Add all the scores and divide by the number of scores 


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Central tendency for nominal data 

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Central tendency for ordinal data 

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Central tendency for scale data 

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Two measures of variability 

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range and standard deviation 


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The numerical difference between the highest and lowest scores in the distribution (doesnâ€™t make sense for nominal data but works for ordinal and scale level data). 


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A statistic that indicates the average distance of the scores from the mean of the distribution (only works for scale level data). 


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Variation measure for ordinal data 

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Variation measure for scale data 

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range and standard deviation 


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Variation measure for nominal data 

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Degree to which the scores of TWO or more variables are RELATED A measure of the relationship between two variables 


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the DIRECTION and STRENGTH of a relationship between 2 variables 


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Two types of correlation tests 

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Spearman Rank Order Correlation Pearson ProductMoment Correlation 


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Increase in one variable is accompanied by an increase in the other variable 


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As one variable increases, the other variable decreases Inverse relationship 


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Two aspects of correlation coefficient 

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direction and strength/magnitude 


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To find direction look at 

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to find magnitude look at 

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When do you use Spearman's 

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