Psych 342

Lab 2.5

Symbolic Distance Effect

Instructions for lab report

For this lab report, you will analyze the group data from the lab exercise, plot the group data means (provided by the instructor) on the graphs in the MEL Lab workbook, and write a manuscript describing the study in APA format. You should include the graphs from the workbook as tables or figures in your paper.

On the course web page you will find three data files to use for the analyses, along with some tables of means that you may find helpful for completing the workbook graphs. In the tables, "conditio" is the question (0="which is larger"; 1="which is smaller") and "largersm" is the pair type (1=both large; 2=both small; 3=one of each). The data in the Table is for correct responses only, as are the data in the data files.

The analyses you are to do are the following:

• Repeated measures ANOVA for the effects of question type (“Which is larger” [q0] vs. “Which is smaller” [q1]) and type of animal pair (p1=both large, p2=both small, p3=one of each).
• Data file: lab2_5-symbdist-question-condition.dat
• The data are arranged in this fashion:
• One line per subject, subject number is the first variable
• Then with 6 mean RTs (one for each condition) for each subject
• Pair type is nested in question type:
  q0p1 q0p2 q0p3 q1p1 q1p2 q1p3
• Download the data to your computer (right click and save to disk)
• Import the data to SPSS (file type = “data”, variable names in first row)
• Run a repeated measures ANOVA (analyze, general linear model, repeated measures)
• Report the main effects (question and pair) and the interaction (q*p)
• Repeated measures ANOVA for the effects of ordinal distance between animals in each pair (distance = 1 to 11)
• Data file: lab2_5-symbdist-distance-for-anova.dat
• The data are arranged in this way:
• One line per subject, subject number is the first variable
• 11 mean RTs per subject, for the 11 ordinal distances between animals in a pair.
• Download the data file and import to SPSS
• Run a repeated measures ANOVA
• Report the main effect of distance, and the test of linear trend
• Regression of distance on RT
• Data file: lab2_5-symbdist-distance-for-regression.dat
• The data are arranged in this way:
• 3 columns with variable names in first row:
  subject, distance, resprt
  (subject, distance, reaction time)
• Do a linear regression of distance on resprt: analyze, regression, linear
• Report the correlation between distance and RT, and report how much the responses speed up with each increase in ordinal distance (in other words, the slope of the regression equation, which is reported as the unstandardized Beta weight, or “B”).

An outline of Methods and Results sections, with reminders of important things to include:

Method

Participants.

Materials. Describe the stimuli in the experiment.

Design. Identify the independent and dependent variables. Identify any important counterbalancing variables. This section can be combined with the materials section if desired.

Procedure. Give a step-by-step description of what happened to the participant and what the participant did in the experiment.

Results

Describe what was done to the data. For example, were any participants or data points were excluded? Did you only analyze correct responses? What kind of data reduction (means per condition for each subject for example) or transformations did you do before running the analysis?

Refer the reader to a table or figure in which the summary statistics (such as means per condition) are presented. The figures and tables from your MEL LAB workbook can be used for this purpose for the lab reports. The means could also be reported in the text rather than in a table or figure. Provide a measure of variability for any means you present. (Standard deviations, standard errors, or the MSerror from an ANOVA table are all acceptable ways to report a measure of variability).

State the predicted results, or state what the question to be answered was. State the results verbally. Also report the test statistic (test of significance, with a p value) that supports your statement of the results. For example, “Participants responded faster in condition A than condition B, t (25) = 3.3, p < .05.”