8.5: Key Terms

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Alpha Level (statistical significance): The probability of rejecting the null hypothesis when it is true
Alternative hypothesis (Ha): Also known as research hypothesis, it is simply an alternative working statement to the null hypothesis. Essentially, it is the claim a researcher is making when testing the relationships between data
Bar Graph: A visual representation of the data, usually drawn using rectangular bars to show how sizable each value is. The bars can be vertical or horizontal
Cases: are the people, places, things or actions (subjects) that are being observed in a research project
Central Tendency - consists of the mode, the median, and the mean, which locate the center of a distribution of a particular data set. It identifies “the most typical case” in that data distribution
Coding: refers to the conversion of words or words phrases into numerical expressions that can be used for statistical analyses
Coefficient: a numerical expression of the relationship between the outcome and explanatory variables
Confidence Levels: representation of statistical significance or alpha levels on regression tables
Descriptive Statistics: The numerical representation of certain characteristics and properties of the entire collected data
Deviation: distance of an observation from the mean
Frequency Table: A table that includes frequency, proportion, percentage and cumulative percentage of a particular observation
Histogram: A type of graph here the height and area of the bars are proportionate to the frequencies in each category of a variable.
Interquartile Range (IQR): The IQR is the difference between the 75th percentile (where 75% of values are located under that point) and the 25th percentile (where 25% of observations are below this point)
Interval Scale: A quantitative variable that possesses the property of equal intervals, but does not possess a true zero
Large-n: a dataset with a large number of cases
Mean: Sum of the observed value of each subject divided by the number of subjects
Median: The point in the distribution that splits the observations into two equal parts. It is the middle point of the data distribution when the observations are ordered by their numerical values.
Mode: The most frequently occurring category/value in data.
Nominal Scale: Identifies the groups to which a participant belongs; does not measure quantity or amount
Normal Distribution: A distribution with a bell-shaped curve where the value of the mean, median, and the mode is the same, and data near the mean are more frequent in occurrence.
Null hypothesis (Ho): A working statement that posits the absence of statistical relationship between two or more variables. In statistics, we desire in proving whether a working statement can be proven false
Ordinal Scale: Subjects are placed in categories, and the categories are ordered according to amount or quantity of the construct being measured. However, the variables are not equidistant from each other
Quantitative Methods: analyses that involves some kind of mathematical analysis or complex mathematical measurement
Range: the difference in the value between the maximum and minimum value
Ratio Scale: An interval quantitative variable that displays a true zero
Scatter Plot: A graph that uses Cartesian coordinates (i.e., a plane that consists of x-axis and y-axis) to display values for two variables from a dataset to display how one variable may influence the other variable
Standard Deviation: The square root of the variance. It represents the typical deviation of observation as opposed to the average squared distance from the mean.
Standard Error(s): An estimate of the standard deviation of the coefficient (Miller)
collection of variables
Statistical inference: Defined as the process of analyzing data generated by a sample, but then used to determine some characteristic of the larger population
Time-Series Plot: A graph used to display the changes in the values of a particular variable measured at a different point in history.
Type-I error: An error of mistakenly reject the null-hypothesis that was true
Type-II error: An error of failing to reject the null hypothesis that is false
Units of Analysis: the “who” or the “what” that you are analyzing for your study. Often interchangeable with word cases
Variable: defined by Hatcher (2013) as having “some characteristic of an observation which may display two or more values in a data set”.
Variance: The average of the squared deviation.
Z-score: A statistic that tells us the number of standard deviations that a particular observation falls above or below from the mean.