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Class notes PSYCH 100 (PSYCH100)
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Research StrategiesKey Components:
Planning the study:
Ask a testable question and decide how to collect data. It is important to consider what demographic
data was collected about the test subjects. Was any natural behavior changed for the duration of the
study? It is important to consider as many variables as possible.
Examining the data:
What is the best way to examine the collected data? What graphs are relevant to the study? Are there
any individual patterns around certain demographics, (eg. Smokers vs. Non-smokers) What does this
reveal about the study? Is there enough of a trend to validate the study?
Inferring from the data:
You need to use valid methods for determining the reason behind the results. Inferences are not enough
as correlation does not equal causation. It is important to further explore to see if these results could
have occurred randomly or if there is another possible explanation.
Drawing conclusions:
Based what was learned from the study, what conclusions can you draw? What are the possible
applications for this information? What is the cause and effect that was found by this study?
Distributional Thinking:
When data are collected to address a particular question, an important first step is to think of
meaningful ways to organize and examine the data. The most fundamental principle of stats is that data
vary. It is important to capture that variation and to understand it. By presenting data well, most
research questions will be answered without the need for sophisticated analyses.
Statistical Significance:
Even when patterns are found in data, there is often plenty of uncertainty in data. Data may be
measured incorrectly, or we may only have a “snapshot” of observations that don’t capture the full
picture.
The P value:
In research, the probability of a certain event happening is referred to as the P value. The p-value tells
you how often a random process would give a result at least as extreme as what was found in the actual
study. If all other variables are controlled for, a small p-value means that the results are significant.
,Typically, the p-value needs to be below 0.05 to rule out random chance affected the results of the
study.
Generalizability:
It is important to select a subject group that accurately reflect the population that the study affects.
Typically, a random sample is the best way to achieve this. This gives every member of the population an
equal chance of being selected. The easiest way to do this is to assign every member of the population a
number and have a computer randomly generate a subject pool from these numbers. If random
sampling isn’t used, bias often finds itself present. Non-randomized studies tend to over-represent
certain groups of the population and under-represent others.
Cause and effect conclusions:
The main question concerns the differences between the subject groups. In some studies, researchers
actively create the groups. This creates a similar question, could any differences we observe have
originated in the grouping process? Perhaps the difference is so large that we can discount random
group selection as the cause and look further into the differences between these two groups.
Importance of Diversity in psychological science:
It is critically important that we carefully consider the extent to which our samples are truly diverse and
random, the possibilities for alternative explanation for our results, and the degree to which our findings
may (or may not) be generalizable. Important demographics to consider for studies are race, age,
geographic location, socioeconomic status, sex, and gender. Diversity is incredibly important to ensure
results are generalizable.
Conclusion:
Statistical thinking involves the careful design of a study collect meaningful data to answer a focused
research question, detailed analysis of patterns in the data, and drawing conclusions that go beyond the
observed data. Random sampling is paramount to generalizing results from our sample to a larger pop.
And random assignment is key to drawing cause-and-effect conclusions. With both kinds of randomness,
probability models help us asses how much random variation we can expect in our results, in order to
determine whether our results could happen by chance alone and to estimate a margin of error.
Research designs
There are two main types of research: experimental and correlational research.
,Experimental research:
Researchers manipulate an independent variable and observe the effect on the dependant variable. It is
incredibly important that subjects are randomly assigned to guard against as many variables as possible.
Other considerations:
You should avoid introducing confounds into your experiments. Confounds are things that can
undermine your ability to draw casual inferences. An example of this is the placebo effect. Participant
demand, where a subject acts in a way which they think the researcher wants them to act, is another
example of a confound. To regulate this, double blind experiments are used. In these experiments,
neither the subject nor the researcher know the condition the participant is in.
Correlational designs:
When scientists passively observe and measure phenomena it is called correlational research. Here, we
do not intervene and change behavior, as we do in experiments. In correlational research, we identify
patterns of relationships, but we usually cannot infer what causes what. Importantly, with correlational
research, you can examine only two variables at a time, no more, no less.
More details about correlation:
To find out how closely related two variables are, a scatter plot is used. Once the scatter plot is built, the
results are summarized statistically as the correlation coefficient (r). This coefficient provides
information about the direction and strength of the association between two variables. A positive
correlation, where both variables increase together, is indicated by a positive r value. Negative values,
whereas one variable increases, the other decreases, are represented by a negative r value.
Problems with correlation:
This method of observation leaves uncertainties. We won’t know which variable is dependant and
independent or is there is a third variable which is unknown. Correlation does NOT equal causation.
Qualitative designs:
Just as correlational research allows us to study topics we can’t experimentally manipulate, there are
other types of research designs that allow us to investigate these harder-to-study topics. Qualitative
designs, including participant observation, case studies, and narrative analysis are examples of such
methodologies.
Quasi-Experimental designs:
These are similar to experimental research, except that random assignment to conditions is not used.
Instead we rely on existing group memberships. We treat these as the independent variable, even
though we don’t assign conditions or manipulate the variables. As a result, with quasi-experimental
designs casual interference is more difficult. This makes it harder to control for every single variable.
, Longitudinal studies:
These types of study track the same group of people over a long period of time. This period can range
from a few weeks to decades. These are quite costly to conduct but are a huge benefit to scientists and
can provide important information on hypotheses.
Genetics and Evolution
The debate of nature vs. nurture has troubled psychologists and biologists alike for centuries. This is very
hard to analyse in humans due to ethical considerations. Monozygotic and Dizygotic twins (Identical and
fraternal) have been a huge help to determining what characteristics are inherited, and which are
learned. Recent discoveries have found that genetics have a larger role on behavior than was previously
thought. Things like divorce rates, political beliefs, and television preferences are all somewhat
influenced by genetics. No behavior is entirely inherited however, and environment plays an important
factor. Despite all this research, scientists have been unable to organize traits from more to less
influenced by genetics.
Evolutionary theories in Psych:
Some behaviors in humans may be the result of evolution. These behaviors have evolved over time but
represent the same driving force that existed in our ancestors. There are two main classes of adaptation
that humans have evolved over time:
Survival adaptations:
These are mechanisms that evolved to help our ancestors handle the “hostile forces of nature”. Sweat
glands to deal with heat, Shivering to combat cold, and craving specific foods the body “knows” is high in
nutrients that it’s lacking. Fear is also an evolved behavior that protects us from situations that could
prove fatal. These are all traits that aide in physical survival.
Reproductive adaptations:
These are traits that evolved specifically to reproduce. This was first proposed by Darwin, called sexual
selection theory. Examples of this are a peacock’s feathers, a bullfrog’s croak, and the scream of the
female sloth.
Error Management Theory
The theory that human nature prefers less risky options over more risky options. The effect of these
repeated choices is amplified over generations. (It’s better to be safe than sorry)
Epigenetics
Activated DNA can have an effect on our behavior. What activates (DNA methylation) and deactivates
(histone acetylation) specific genes can be heavily influenced by our environment. Scientists are also
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