EMAC ASSESSMENTS, LLC
CREATE COMPETITIVE ADVANTAGE WITH EMAC'S LAMPE
LEADERSHIP MODEL AND OUR HALO TECHNOLOGIES.
CAUSAL-CHAIN ANALYSIS IN THE HALO TECHNOLOGIES
When the employees complete their Customized Organizational Inquiry (COI), they express their degree of agreement with an item statement. These choices are typical:
5 Strongly Agree
4 Agree
3 Neutral
2 Disagree
1 Strongly Disagree.
The result is the average score. An average may mask a wide range of opinions. One should use caution when examining results. (It is possible to drown in a river whose average depth is one inch). Some might strongly agree while others might strongly disagree with the same COI item. This data variety is called variance. The purpose of causal-chain analysis is to determine the best overall explanation for the variance of each COI item in terms of a management practice that can be changed.
In particular, causal-chain analysis seeks to uncover the best knob to explain the variance of each COI item. The knobs are derived from the other survey completed by your employees, the Organizational Diagnostic Survey (ODS). The knobs represent management practices under the control of your organization. Improving the scores of these knobs provides a double benefit: It improves the organization and it improves the COI score.
The necessarily complex procedure to uncover the best predicting knob for each COI item is called causal-chain analysis. Causal-chain analysis involves statistical analysis, linear programming, the analytical pyramid, decision theory, and common sense. A causal-chain is formed when the best predictor of a COI passes a series of logically connected tests. This results in a chain of reasoning to identify, verify, and validate the knobs which best explain the variance of each COI item.
The number of possibilities is large. There are 53 possible knobs (6 Desired Organizational Characteristics, 12 Holonomic Processes, 29 Leadership Practices, and 6 Dynamic Congruency Conditions). If there are 35 COI items, this means that there are 53 x 35 = 1,855 possible correlations to calculate and analyze. This immense set of data is unwieldly. Causal-chain analysis is a procedure for separating the wheat from the chaff.
The identity of the best predictor is often a surprise. For example, one corporation's best predictor of the COI item: "I am satisfied with my overall compensation," did not even involve the organization's rewards system. The best predictor of the COI Item was LP20, Encouraging Best Decision Making. It turned out that dissatisfaction with the means for making decisions was a source of dissatisfaction with pay, which was a surrogate for general management behavior. The underlying issue was how decision-making procedures were executed. Improving decision making procedure was both more effective and much less expensive than making the "obvious" of raising everyone's pay. Besides, given the high variance some respondents were entirely satisfied with their overall compensation.
The causal-chain analysis views the problems of determining the best predictor an empirical problem. Instead of assuming a relationship, we actually calculate it. We want to find a predictor that not only predicts but makes sense. A good predicting knob of a COI item must pass three tests, which follow the analytical pyramid.
Often, a COI item has more than one causal-chain leading to a good predictor. We arrive at the best predictor by successively applying still more tests in order to eliminate the weaker ones.
For those desiring greater detail, a Supplementary Analysis Report can be ordered to provide full causal-chain information for any and all of the COI items. The use of knobby analyses methods allow one to "drill-down" to explore greater detail.
HALO is interested in deriving implementable recommendations. The more precise the knob of a COI Item, the easier it is to implement changes in its values. It is our goal to select Leadership Practices because they are the most specific link to your COI Items. The Desired Organizational Characteristics are more global than the Holonomic Processes and the Holonomic Processes are more global than their constituent Leadership Practices.
Tabulating COI Causal-Chain Results
Chapter 6 of the Organizational White Paper, a Standard Report for HALO, presents two facts for every COI item. The first is its raw average score and the second is the identity of the best predictor KIP. Sometimes (usually in a small sample or if the data variance is low) there may be more than one best predictor. Sometimes there is no best predicting LP. Table 6.1 is a sample of a COI Causal-Chain Results for a research and policy organization we call Euphoria Center.
The scores in Table 6.1 are actual but the identity of the organization has been changed in order to preserve confidentiality. This organization had exceptionally high scores on its 33 COI items. Note that some LPs are best predictors for multiple COI items. For example LP16, Integrating Jobs with the Organization, occurs four times in Table 6.1. Later in the Organizational White Paper (Chapter 8), Table 6.1 is turned inside out to show which COIs were predicted by each LP. The recommendations in the Organizational White Paper select these LPs having two benefits: The LPs with the largest Potential Improvement Value and the LPs with the best predictor of COI items.
| Site Map |