The document discusses measuring interventions and change in organizational development. It covers selecting appropriate variables to measure, designing good measures that are operationally defined, reliability which concerns accurately translating data, validity which ensures a measure reflects what it intends to, and using quasi-experimental research designs with longitudinal measurement, comparison units, and statistical analysis. The document also discusses assessing alpha, beta, and gamma types of perceptual changes resulting from interventions.

1. MASTER OF SOCIAL WORK II YR
PRESENTATION
ON
MEASURING INTERVENTION &
CHANGE.

2. SYNOPSIS
INTRODUCTION
MEASUREMENT OF OD INTERVENTIONS:
SELECTING VARIABLE
DESIGNING GOOD MEASURE
RELIABILITY
VALIDITY
RESEARCH DESIGN
OD CHANGES

3. INTRODUCTION
Assessing OD Interventions involves judgments about
whether an intervention has been implemented as
intended & if so, whether it is having desired results.
Managers investing resources in OD efforts increasingly
are being held accountable for results being asked to
justify in terms of bottom-line outcomes. Measurement
of Organizational Interventions provides development of
useful implementation & evaluation feedback.

4. MEASUREMENT OF OD INTERVENTIONS
Selecting Appropriate
Variable.
Designing
good measure.

5. Selecting Appropriate Variable
Ideally, the variables measured in OD evaluation should derive from
the theory or conceptual model underlying the intervention. The
model should incorporate the key features of the intervention as well
as its expected results.
For example, the joblevel diagnostic model proposes several major
features of work: task variety, feedback, and autonomy. The theory
argues that high levels of these elements can be expected to result
in high levels of work quality and satisfaction.
Whether the intervention is being implemented could be assessed
by determining how many job descriptions have been rewritten to
include more responsibility or how many organization members
have received cross-training in other job skills. Again, these
measures would likely be included in the initial diagnosis, when the
company‟s problems or areas for improvement are discovered.

6. OPERATIONAL DEFINITION FOR DESIGNING GOOD
MEASURE
A good measure is operationally defined; that
is, it specifies the empirical data needed how
they will be collected and, most
important, how they will be converted from
data to information. These measures consist of
specific computational rules that can be used to
construct measures for each of the behaviour.
They provide precise guidelines about what
characteristics of the situation are to be
observed and how they are to be used.

7. RELIABILITY
Reliability concerns the extent to which a measure represents the “true” value of a variable; that
is, how accurately the operational definition translates data into information. The 1st source of
reliability is by or through measurement, rigorously & operationally defining the chosen variables.
Clearly specified operational definitions contribute to reliability by explicitly describing how
collected data will be converted into information about a variable. Second, use multiple methods to
measure a particular variable through use of questionnaire, interviews, observation.Third, use
multiple items to measure the same variable on a questionnaire.Fourth, use of standard
questionnaire.

8. Validity
Validity concerns the extent to which, a measure actually reflects the variable it is
intended to reflect.On a measure of happiness of employees, for e.g., the test would be
said to have face validity if it appeared to actually measure levels of happiness.In other
words, a test can be said to have face validity if it „looks like ‟ it measures what it is
supposed to measure. If the experts agree that the measure appears valid it is called
Content Validity.If measures of similar variables correlate highly with each other, it is
called Criterion/Convergent validity. If measures of non similar variables show no
association, it is called Discriminant Validity

9. RESEARCH DESIGN
In assessing OD Interventions, practitioners have turned to quasi-
experimental Research design with the following features:
 Longitudinal measurement:
This involves measuring results repeatedly over relatively
long time periods. Ideally, the data collection should start before the change
program is implemented and continue for a period considered reasonable for
producing expected results.
 Comparison unit:
It is always desirable to compare results in the intervention
situation with those in another situation where no such change has taken place.
Although it is never possible to get a matching group identical to tile
intervention group, most organizations include a number of similar work units
that can be used for comparison purposes.
 Statistical analysis
Whenever possible, statistical methods should be used to rule
out the possibility that the results are caused by random error or chance. Various
statistical techniques are applicable to quasi experimental designs, and OD
practitioners should apply these methods or seek help from those who can apply
them.

10. Assessing OD Changes
The use of multiple measures also is important in
assessing perceptual changes resulting from
intervention. Considerable research has identified
three types of change
 Alpha,
 Beta, and
 Gamma change.

11. OD CHANGES
Alpha Change:
It concerns a difference that occurs along some relatively stable
dimension of reality. . For example, comparative measures of
perceived employee discretion might show an increase after a job
enrichment program. If this increase represents alpha change, it
can be assumed that the job enrichment program actually increased
employee perceptions of discretion.
Beta Change:
It refers to recalibration of units of measure in a
stable dimension.
Gamma change:
It involves fundamental redefinition of dimension.