2022 Introduction to Statistics in Research Mitchell 2nd ed

I N T R O T O R E S E A R C H : D A T A V I S U A L I Z A T I O N & C O M M O N S T A T T E S T S

Explanatory variable: in regression, the explanatory (also called the independent variable) is the one that is supposed to explain the other. (i.e. the hotter the temperature (explanatory or independent variable) the more swimmers (dependent variable) in your pool).

Factor analysis: a technique for discovering patterns among variables to determine an underlying combination of the variables.

Five-number summary: the median, the upper and lower quartiles, and the largest and smallest observations of a variable’s distribution.

Focus group: the simultaneous discussion with a small number of research participants (usually 8 to 10) who interact with a moderator to generate data on a particular topic. Used often in exploratory studies.

Frame, sampling frame: a collection of units from which a sample will be drawn. Typically the frame is a subset of a population of interest. Some bias can be introduced.

Frequency table: lists the frequency (number) or relative frequency (percentage) of observations in different ranges. From lowest value to highest value with a column for count, percent, valid percent, and cumulative percent.

Glass’s Delta: A measure of effect size. Use if each group has a different standard deviation (SD). It is also used between the experimental and control group.

Goodness of fit: a measure of how well the regression model is able to predict Y.

Grounded theory: a technique where analysis of the data takes place simultaneously with its collection. The purpose is to develop general concepts or theories to analyze the data.

Halo effect: error caused when prior observations influence perceptions of current observations.

Hedge’s correction : Effect size test. Use this test when you have a different and/or small sample size (i.e. 20 males in group 1 and 10 females in group 2). Small sample size means less than 20.

Heteroscedasticity: indicates a mixed scatter in a scatterplot. If your data has a pizza shape, you are probably dealing with heteroscedasticity. Linear regression is not usually a good idea if the data are heteroscedastic.

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Table 36: Example of Heteroscedasticity and Homoscedasticity

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