**Ordinal Analysis - Concatenated Orderings**

**Generic Question: **Is there a meaningful difference
between dependent observations on a set of
orderings (variables)?

**Data Type**: Two or more quantitative orderings (variables) with identical
scaling. Repeated measures /
within-subjects / dependent observations

**
Hypotheses**: The ordinal pattern in a given set of orderings (e.g., A < B; A < B < C;
[A = B] > C)

Traditional analogs

Summary

**Software Access**: The *Ordinal Analysis -- Concatenated
Orderings* procedure can be found by selecting "*Analyses --> Ordinal
Analysis --> Concatenated Orderings*" from the Main Menu. The procedure window
appears as:

**Videos and Files:** The following videos
and files describe the procedure and its various
options. Be sure to set the video quality to High Definition (1080p) if the
browser does not automatically do so. If you wish to follow along with the
analyses on your own computer, download and open the data set(s) linked at the
bottom of this page.

**Definitions**(a list of definitions commonly used in the OOM software and in observation oriented modeling):**Introduction**(a brief introduction to the procedure that describes how the analysis is conducted):- Ordinal Analysis -- Concatenated Orderings Introduction (two orderings, like a dependent samples t-test)
- Ordinal Analysis -- Concatenated Orderings : II Introduction (three orderings, like a repeated measures ANOVA)

**Specific Options**(the Analyze, Missing Values, Randomization Test, etc. options in the analysis and options window above are explained and demonstrated):- Classification Imprecision
- Missing Values: Omit or Include
- Ordinal Classifications
- Analyze: Numbers or Deep Structures
- Randomize (with four sub-options):
- Randomize: within Cases
- Randomize: within Orderings / All Observations
- Randomize: Deep Structures
- Choosing a randomization procedure (a Powerpoint hand-out explaining what to consider when choosing between the two sub-options)

- Pairwise Results (see the Concatenated Orderings : II video linked under Introduction above)
- Individual Results (Append and Save)
- Save Difference Scores
- Separate by Ordering feature

**Creating a Profile Plot**- Creating Profile Plot
- Editing an OOM graph in Powerpoint (this example shows a multigram, but the steps can be followed for a saved profile plot as well).

**Comparing Two PCCs**(a demonstration of how a chance-value can be determined for the difference between two PCCs):**Post Hoc Pattern**(shows how to run the analysis in the event a predicted ordinal pattern is not available):**Planning Sample Size**(based on the chance-value)- Description of steps in the procedure to determine sample size (review the steps on this handout before watching the videos linked below)
- Creating a Data Set
- Running the Analysis and Examining Randomization Results Distribution
- Comparison of planned sample size and actual sample size for the Affective Realism study (handout)

**Reporting Results**

- Example APA write-ups for the
*Ordinal Analysis -- Concatenated Orderings*procedure. - See our
Persons as Effect Sizes paper. The first study reported in this paper
used the
*Ordinal Analysis - Concatenated Orderings*procedure, sans the randomization test. The data were taken from Seigel et al.'s published study.

**Data**

**Data Sets**(used in videos above)- Affective Realism Study #1, n = 45