Training Industry Magazine - July/August 2020 - 42

›	 Which managers of the participants
are inclined to provide support for
the training.
The three data-driven data sets pinpoint:
›	 Which research-based training transfer
components and research-based
training transfer factors contribute
to training transfer.
›	 Obstacles participants encountered
post-training that prevented them
from applying what they learned on
the job.
›	 A just-in-time measure of scrap learning.
Data for calculating the two predictive
data sets are collected from participants
immediately following their participation
in a learning program using a survey.
The survey consists of 12 questions
developed from the 12 training transfer
factors described earlier.

DATA SET 1
To predict which learners are most likely
to apply what they learned in a training
program, participant responses are
summarized into average scores. The
average scores are then organized into
numeric order from highest to lowest,
and the top 15%, middle 65% and bottom
20% scores are calculated.

These percentages align with the results
Brinkerhoff and Mooney found in their
2008 training transfer research:

DATA SET 2
To predict which managers of the
learners are inclined to provide support
for the training requires two sets of
scores. One is a composite score based
on the 12 survey items described earlier,
and the other is an average manager
training support score. Composite
scores are calculated based on the
number of employees a manager sends
to training, and the score is an average
of the employees' scores on the 12
survey items. For example, if a manager
sends three employees to training and
the average scores on the 12 questions
for the three is 5.92, 5.43 and 5.69, the
composite score would be 5.68 (based
on a seven-point scale).
Manager training support scores are also
calculated based on how many employees
a manager sends to training. The score
is an average of employee responses to
the two survey items measuring the level
of support the manager provides to the
employees before and after the training.
For example, a manager sends three
employees to training and each one
scores the pre-training and post-training
manager support survey items as follows:
›	 Employee 1 pre-training support 6,
post-training support 7
›	 Employee 2 pre-training support 3,
post-training support 2
›	 Employee 3 pre-training support 4,
post-training support 4
The training support score for the
manager would be 4.33 (6 + 7 + 3 + 2 + 4
+ 4 = 26 ÷ 2 = 13 ÷ 3 = 4.33).
Predictions regarding which managers
are inclined to provide support for the
training are calculated by subtracting
the composite score from the average

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manager training support score. Positive
difference scores indicate that a manager
is inclined to provide active support for
the training. In contrast, low and negative
ratings suggest that a manager is inclined
to provide weak support. In the example
above, the manager would have a
difference score of - 1.36 (4.33 - 5.69),
indicating he or she is inclined to provide
weak support. Only managers with three
or more employees attending the training
are included in the predictions to ensure
valid results.

DATA SET 3
Data set three identifies which of the
training transfer components and
training transfer factors described earlier
are contributing to training transfer.
Scores are calculated by computing
an average for each of the 12 training
transfer factors and grouping the
factors according to the training transfer
component with which they align. An
average score for each component is
then calculated. To determine if any of
the component score differences are
significant, a statistical test is performed.
Components identified as contributing
the least to training transfer are
candidates for corrective action.

DATA SETS 4 AND 5
Data for calculating the final two measures
are collected from participants 30 days
post-program using a survey or focus
groups and consists of three questions:
Training Industry Magazine - July/August 2020

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