Data Description Tables and Graphs Data Reduction

Tables and Graphs Tables: Numerical data in a matrix format Graphs: Visual representation of data Types of Graphs/Tables: –Displaying information about a single variable Frequency Cumulative Frequency –Displaying information about the relationship among 2 or more variables Scattergram One or more IV and one or more DV Time Series

Sample of N=10 from p.418 IDSexA (weight)F (recall)G (correct) 04m f614 35f f m m512 55m m611 34f m512

Information about a single variable: Frequency graphs and tables Frequency Table Counts of responses One row for each score or range of scores Histogram Bar graph Stem and leaf plot Frequency polygon Cumulative frequency polygon

Relationships Between 2 Variables In frequency plots, x axis is DV, but in most plots with 2 variables, DV is on y axis Scattergrams Tables with 1 IV and 1 DV (2 variables) Tables with more than 1 IV (more than 2 variables) Line and Bar Graphs Time Series Graphs

Scattergrams –Used to show correlations –Any type of variable (IV or DV) can be on either axis, but often have DV on ordinate and IV on abscissa –Each plotted point represents the score of one individual on two separate variables

Tables with 2 or more Variables In a table, the scores represent the DV The columns represent the levels of the IV With only 1 IV, there will be only 1 row With 2 IVs, the rows will represent levels of the second IV Additional IVs will be represented as groups of columns

Graphs with 2 or More Variables DV is represented on the y axis Levels of IV or IVs are represented on the x axis With 2 or more IVs, levels of an IV may be represented by color, shape, or shading Bar graphs are appropriate for categorical IVs Line graphs are appropriate only for quantitative IVs on x axis

Time Series Graphs X axis represents passage of time

Data Reduction 1.Prepare the data sheets for transferring data 2.Transcribe data onto a data summary sheet Matrix containing all the data One data point per row Columns represent variables (DV in last column usually) 3.Create coding guide 4.Use coding guide to create computer data file 5.Check for missing data, invalid data, and outliers (descriptive statistics, or exploratory data analysis) 6.Proceed with analysis (descriptive and inferential statistics)