One of the ways that you can tell if you have written an "answerable"
experimental question is to try and reduce the question down into variables
(see "variables " module).
In psychology experiments reported in scholarly journals the form of these
questions and/or hypotheses varies. For example, let's take two fairly
simple variables: Arousal level and test performance. First of all, let's
contrast an experimental hypothesis with a question:
- *hypothesis*: I predict that arousal and test performance will be
significantly related.
- *question*: What is the relationship between test performance and
arousal?
Another way of differentiating among experimental hypotheses is to contrast
directional and non-directional hypothesis. A directional hypothesis is more
specific, in that the experimenter predicts, not only that a specific
relationship will exist, but, further, the direction of that relationship.
For example:
- *non-directional*: I predict that arousal and test performance will be
significantly related.
- *directional*: I predict that, as arousal increases, test performance
will decrease.
Finally, we can also differentiate hypotheses that imply causality vs those
that are relational. A directional and causal hypothesis is the most
specific of those discussed so far. (We will address the topic of causality
in research design in much more detail in future lessons.)
- *relational*: I predict that, as arousal increases, test performance
will decrease. (Note that all of the example questions/hypotheses thus far
have been relational).
- *causal*: I predict that anxiety causes/leads to poor performance on
tests. (As we'll see in future virtual lectures, in order to truly address
causal hypotheses, a more stringent research design possessing certain
characteristics is required).
As you might have already surmised we can think of these different types of
experimental questions/hypotheses on a continuum from least
specific/stringent to most specific and stringent.
Further, the greater the specificity, the less likely we are to find our
predicted results just by chance. This is why, for example, we can be more
confident of research results that are consistent with a causal-directional
hypothesis, than is the case of findings that are consistent with a
non-directional hypothesis. More specific and stringent predictions are used
more frequently in areas in which a great deal is already known, while less
stringent predictions and questions are more common in "exploratory"
studies, in areas where a great deal is not known.
We can actually think of these aspects of research questions and hypotheses
as three variables, each with two levels, as we discussed in the variables
tutorials. What's more we can examine the relationship among these
"variables" by viewing them in the form of two, two by two (2 x 2) matrices.
This is illustrated below with example questions and hypotheses based on the
two variables "stress" and "health". However, it is not possible to come up
with an example for each cell of these matrices because it is not possible
to propose a non-directional and causal hypothesis. One of the preconditions
for establishing causality is directionality. In order for one event to
cause another event, the former must precede the latter in time.
*Questions*
relational
causal
non-directional Are stress and health related?
*Causal hypotheses must be directional.*
directional Are stress and health inversely related, such that health
decreases as stress increases? Does increased levels of stress lead to
decreased levels of health?
*Hypotheses*
relational
causal
non-directional I predict that stress and health are related.
*Causal hypotheses must be directional.*
directional I predict that stress and health are inversely related, such
that health decreases as stress increases. I predict that increased levels
of stress will lead to decreased levels of health.
experimental question is to try and reduce the question down into variables
(see "variables " module).
In psychology experiments reported in scholarly journals the form of these
questions and/or hypotheses varies. For example, let's take two fairly
simple variables: Arousal level and test performance. First of all, let's
contrast an experimental hypothesis with a question:
- *hypothesis*: I predict that arousal and test performance will be
significantly related.
- *question*: What is the relationship between test performance and
arousal?
Another way of differentiating among experimental hypotheses is to contrast
directional and non-directional hypothesis. A directional hypothesis is more
specific, in that the experimenter predicts, not only that a specific
relationship will exist, but, further, the direction of that relationship.
For example:
- *non-directional*: I predict that arousal and test performance will be
significantly related.
- *directional*: I predict that, as arousal increases, test performance
will decrease.
Finally, we can also differentiate hypotheses that imply causality vs those
that are relational. A directional and causal hypothesis is the most
specific of those discussed so far. (We will address the topic of causality
in research design in much more detail in future lessons.)
- *relational*: I predict that, as arousal increases, test performance
will decrease. (Note that all of the example questions/hypotheses thus far
have been relational).
- *causal*: I predict that anxiety causes/leads to poor performance on
tests. (As we'll see in future virtual lectures, in order to truly address
causal hypotheses, a more stringent research design possessing certain
characteristics is required).
As you might have already surmised we can think of these different types of
experimental questions/hypotheses on a continuum from least
specific/stringent to most specific and stringent.
Further, the greater the specificity, the less likely we are to find our
predicted results just by chance. This is why, for example, we can be more
confident of research results that are consistent with a causal-directional
hypothesis, than is the case of findings that are consistent with a
non-directional hypothesis. More specific and stringent predictions are used
more frequently in areas in which a great deal is already known, while less
stringent predictions and questions are more common in "exploratory"
studies, in areas where a great deal is not known.
We can actually think of these aspects of research questions and hypotheses
as three variables, each with two levels, as we discussed in the variables
tutorials. What's more we can examine the relationship among these
"variables" by viewing them in the form of two, two by two (2 x 2) matrices.
This is illustrated below with example questions and hypotheses based on the
two variables "stress" and "health". However, it is not possible to come up
with an example for each cell of these matrices because it is not possible
to propose a non-directional and causal hypothesis. One of the preconditions
for establishing causality is directionality. In order for one event to
cause another event, the former must precede the latter in time.
*Questions*
relational
causal
non-directional Are stress and health related?
*Causal hypotheses must be directional.*
directional Are stress and health inversely related, such that health
decreases as stress increases? Does increased levels of stress lead to
decreased levels of health?
*Hypotheses*
relational
causal
non-directional I predict that stress and health are related.
*Causal hypotheses must be directional.*
directional I predict that stress and health are inversely related, such
that health decreases as stress increases. I predict that increased levels
of stress will lead to decreased levels of health.
