This document reviews concepts from Browne and Keeley (2012) regarding causation and evaluating research. It discusses how there are often rival causes that could explain outcomes other than the main cause proposed by researchers. When evaluating research, it is important to consider rival explanations and look critically at the evidence and statistics provided rather than accepting the initial conclusions. Generating alternative rival causes is a creative process that helps gain a more objective understanding of the issues.

1. Review of Browne and Keeley (2012)

2.  We need to be reasonably sure, at the very least,
that what we are doing is having a positive impact.
 Often, the perfect causal relationship does not exist.
 Other factors, often known as rival causes, can be
equally responsible for the change in our clients.
 When evaluating information about treatment
programs and research studies, rival causes are also
important.
 May think about these as nuisance variables, or
important variables to consider.

3.  Rival causes imply some alternative
interpretations for the interpretation made by
the researcher for why events turned out as
they did.
 A plausible alternative explanation that can
explain why a certain outcome occurred.
 Need to look deeply at the evidence and try to
understand the causal relationship that the
researcher or writer is hoping to have us
accept.
 You should view your therapy the same way.

4. 1. Many kinds of events are open to
explanation by rival causes.
2. Most communicators will provide you with
only their favored causes…Must analyze
these.
3. Generating rival causes is a creative process.
The rival causes will not be obvious.
4. The certainty of a cause is inversely related
to the number of possible causes.

5.  Can I think of other ways to interpret the
evidence?
 What else might have caused this act or these
findings?
 If I look at this from another point of view,
what might I see as important causes?
 If this interpretation is incorrect, what other
interpretation might make sense?

6.  Comparing groups who receive different
treatments is not as straightforward as we
might hope
 Oversimplifcation fallacy leads us to determine
results based on causal factors that are
insufficient to account for the event or by
overemphasizing the role of one or more of
these factors.
 Confusing causation with association
Problems Determining Causation

7.  Confusing “after this” with “because of this”
 Post Hoc- Assuming that a particular event, B, is caused
by event A.
 Could be that the sequence is a coincidence or due to
other factors.
 Explaining Events
 Fundamental attribution error-Overestimate the
importance of personal tendencies relative to situational
factors in interpreting the behavior of others.
 Victim blaming is an example of this.
 Could also related to wrongful theories.
More Causation Problems

8.  Do the causes make logical sense?
 Are the causes consistent with other
knowledge that you have?
 Are the causes important for explaining or
predicting events?

9.  Be open to other potential causes.
 Be clear with your clients what parts of your
treatment are predicting the outcome.
 Be aware of rival causes, attempt to control or
address their impact on what you are doing.

10.  What meaning can we take away from the use
of statistics?
 Often times, researchers can use statistics to deceive.
 Also, statistics will have inherent limitations.
 Use of statistics require certain rules to be followed.
Certain statistics are simply inappropriate for certain
kinds of data.
 Some times they are ill-defined
 Do not fit the phenomenon being studied.
 Lack of operational definitions serve to confuse things.

11.  Unknowable and biased
 Incomplete reporting of data- Need to look for the
information that is missing.
 Making choices as to what data is reported.
 Confusing averages
 Mean
 Median
 Mode
 Range

12.  Concluding one thing, providing data for other
finding
 Use statistics that prove one thing, but then claim to
have proved something different.
 Generally, researchers are supposed to be “blind” to
data until the results are found. Readers should
attempt to do this, too.
 Ask---”Do the statistics match the conclusions??”
 Deceiving by omitting information
 Statistics are often incomplete.
 “What relevant comparisons were omitted? What
didn’t they explore?”

13.  Try to find out as much as you can about how the statistics
were conducted and findings were attained.
 Be curious about the data, especially averages and
percentages. Look for the range and other data that is often
omitted.
 Be alert to researchers using statistics to make one
conclusions, though they have proved something else
altogether.
 Blind yourself to the researcher’s statistics and compare the
evidence with what is actually provided.
 Form your own conclusions from the statistics. If it does not
match the researcher’s conclusion, then something is wrong.
 Determine what information is missing.