Developing Useful Measurements
Since religion provokes strong feelings and church participation tends to be emotion-laden and often volatile, we need metrics that are durable and reliable enough for us to hear above the "noise" of our feelings.
That means, first, that we need numerical measures that can be tracked and charted.
For example, attending a class is a better measure than saying one likes or doesn't like the class. Track behavior, then ask why.
People's willingness to teach Sunday School is a better measure than a goal statement saying Sunday School is important.
Second, we need measures where a variance has identifiable meaning.
For example, if volunteer count on, say, a yard cleanup day declines steadily for several years, it might mean this ministry needs to be stopped or changed.
If giving to a capital project lags far behind giving to operations, it might indicate the capital project lacks adequate support.
Changes over time tend to reveal more than simple slice-of-time measurements.
Third, meaningful measures are those that involve people's behavior or choices.
Opinion surveys mean less than behavior tracking. People vote with their feet, as they say, not with their mouths.
In a religious system where nice, passive and polite are operative values, people often say one thing and do another; the former to avoid criticism or feeling out of step, the latter as an expression of their actual values.
Fourth, we need measures that are useful for macro-level trends and micro-level behaviors.
Sunday attendance, for example, is a useful macro measurement, because it shows high-level trends and allows for adequate budgeting, staffing and planning.
When tracked by age group, gender and frequency of attendance, Sunday attendance is a useful micro measurement, because it shows segmented behavior and allows fine-tuning of staffing and planning.
Finally, gather the same metrics every week, every month, every year, as appropriate. One-time measures don't mean as much as consistent measurements over time.
One-time measures can reveal a gap between expectation and actuality. But measurements over time show trends in actual response.
It is trends that matter, not isolated numbers
