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PSy1005S Introduction to Psychology 2 Lecture Notes
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- University Of Cape Town (UCT)
All lecture notes from the 2020 UCT PSY1005S course
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Week 1, Chapter 1: Research MethodsWhy should we care about psychological myths?
They can be harmful
They can cause indirect damage
Accepting them can impede our critical thinking in other areas
From this it becomes clear that we need a science to deconstruct these potentially harmful myths.
The Scientific Process:
1. Start with a question
2. Develop a hypothesis
3. Define your variables
4. Test the hypothesis using sound research design
5. Analyse data
6. Report findings
7. More research and theory building
8. New hypothesis derived from theory
Quantitative Research: draws on the logical positivist paradigm and is interested in prediction and
causation. (Numerical)
Qualitative Research: draws on a number of paradigms and is interested in in-depth
understandings of behavior. (Words/ verbal)
The goals of quantitative research are describing behavior, predicting behavior, identifying the
causes of behavior and explaining the behavior.
Where do scientific ideas come from? Common sense, observations, past research, practical
problems and theories.
A theory is a systematic body of ideas about a particular topic or phenomenon, which has two
functions: it organizes and explains a variety of specific facts or descriptions of behavior, and it
generates new knowledge by focusing our thinking.
Scientific theories are grounded in actual data from prior research. They generate hypotheses which
can be tested. Hypotheses and therefore theories must be falsifiable.
Hypotheses are precise, testable statements of predictions based on theory for the outcome of a
study. They are statements of relationships between two variables.
There are five broad types of quantitative study:
1. Case study: examines one subject in detail, provides rich descriptive detail and suggests
hypotheses for further study. Rare phenomena are able to be studied in depth. Case studies cannot
establish cause or effect, and may not be representative. They often rely on the researcher’s
subjective opinions.
2. Naturalistic observation: behavior is observed in the setting where it naturally occurs. It can provide
detailed information about nature, frequency and context of naturally occurring behaviors. It cannot
establish cause and effect, and the observer’s presence may affect behavior of the participants in
the study.
3. Survey: questions or tests administered to a sample drawn from a larger population. A properly
drawn representative sample gives accurate information about the broader population, whilst
unrepresentative samples yield misleading results. An interviewer and social desirability bias may
distort the findings.
, 4. Correlational Study: strength of association between variables is assessed. It allows prediction, may
help to establish how results from experiments generalize more nature stings, and can examine
issues that cannot be studied practically or ethically in experiments. It cannot give causation.
5. Experiment: variables are manipulated and the effects on other variables are measured. It is the
best method for examining cause and effect, however careful design is essential, otherwise
confounding can threaten the validity of the results.
What is a sound research design?
Hypotheses are good when they are clearly stated and falsifiable
Good measurement variables (construct validity)
Sampling is thought about, and the researcher is interested in whether or not the data can be
generalized to the general population or not (external validity).
Design is based on whether or not we are interested in cause or in associations between variables.
Our research method needs to fit the results we wish to generate.
Analyse the data (use statistical methods properly and make sure the statistical conclusion is valid).
All findings must be reported even if we do not like them. This prevents bias and research fraud.
More research must be done, and just one study is never enough. Replication is key.
Lecture 3: Correlation and Causation
Correlations, and therefore correlational studies, can allow for predictions, but do not actually give
us causation. Experimental studies are the only studies that allow for causal conclusions.
Correlations allow us to examine the strength of association between to variables. Correlation can
lead to prediction of outcomes.
If both variables increase together, the correlation between them is positive, and is represented by a
number where r = 1 (in the perfect world) or as close to 1 as possible. If one variable increases and
the other decreases, we say the correlation between them is negative and r = -1 (or as close to -1
as possible). No correlation (r = 0) is indicated with a flat line on a graph, and indicates no
relationship between the variables.
Correlation coefficients vary from -1 to +1. The closer they are to 0, the weaker the correlation. The
direction of the correlation indicates how the variables are associated, with positive meaning both
variables increase or decrease together, and negative meaning the variables do the opposite of one
another (one increases whilst the other decreases, etc).
The independent variable (IV) is the variable we adjust, and the dependent variable (DV) is the one
we measure. The experimenter is therefore able to manipulate the IV in order to determine how it
affects the DV.
In correlation studies, the third variable (the confounding or extraneous variable) is always an issue.
This variable essentially interferes with the conclusions we are able to draw between the dependent
and independent variables, as they could in fact be causing some of the actions or outcomes we are
able to observe taking place with the dependent variable.
So why do we do a correlational study? They are useful in identifying real-world association that can
then be studied under controlled lab conditions (e.g. white cars have less accidents due to them
being more visible). The also allow us to test whether relationships found in the laboratory
generalize to the real world. They allow for prediction, and sometimes they are the only practical
method of studying something.
The three criteria for establishing causality: (NB from now – PhD level)
1. Covariation between the two variables (when one variable changes, the other changes too/ shows
the effect)
2. One variable comes before the other variable in time (temporal precedence)
3. Eliminate other possible, plausible explanations (third/ extraneous variables)
,Lecture 4: The Experimental Method
The experimental method is the only way to really establish causality with confidence.
There are two key defining characteristics of an experiment: random assignment, and that the
assignment is in two or more groups. Random assignment ensures that we do not end up with
“special groups.”
The only difference between experimental groups and control groups (groups given placebo drugs
etc) is what you (the experimenter) does to them. Experimental control is partly achieved through
random assignment to experimental and control groups, and partly achieved through carefully
thinking through possible cofounding variables.
What makes a good experiment? Firstly: random assignment (1). If there is no random assignment,
it is not an experiment. Random assignment is possible with a large sample size (2) that ultimately
leads to experimental groups and control groups that are all ultimately the same in important ways.
It gives you statistical power to find an effect.
Another important point that makes is good experiment is careful planning (3) so that cofounding
variables are eliminated.
Remember: in correlational studies there is no random assignment, e.g. You observe that your
classmates who sit toward the front of the class get good marks, while those who tend to get poor
marks sit towards the back.
Lecture 5: Counfounding variables in experiments
Counfounding variables are variables that could influence the outcome of the experiment in an
unforeseen manner. They are two variables that are intertwined in such a way that you cannot
determine which one has influenced the effects that you are measuring.
There are three main types of confounding variables:
1. Demand characteristics: a situation in which the results of an experiment are biased because the
experimenters’ expectancies regarding the performance of the participants on a task create an
implicit demand for the participants to behave as expected.
2. Placebo effects: a placebo is a treatment that has no effect, good or bad. A placebo effect is
therefore a group of people receiving treatment that show a change in their behavior because of
their expectations, not because of the treatment itself. In experiments with placebos, placebo
controls are used.
3. Experimenter expectancy effects: a form of reactivity in which the researcher’s cognitive bias
causes them to subconsciously influence the participants of an experiment. To avoid this from
happening, experimenters have to be trained well so that they behave consistently with all
participants. Experimental and treatment conditions also have to take place simultaneously, and if
, possible procedures should be automated. Experimenters who do not know what the hypothesis is,
or which group the participants are in should also be used.
Lecture 6: Operationalizing variables
Variables vary. In other words, they change and therefore have to be operationally defined in order
to be studied. What operations does the researcher define to be that variable?
Operational definitions can vary wildly from study to study. Here is an example regarding an
operational definition of aggression:
How many times in the past 3 months have you been involved in a physical fight while drinking or using drugs?
(Choose one)
0 Never
1 Once
2 Twice
3 Three or more times
8 Refuse to answer
From the example above it is clear to see that we are not interested in the reason for the fight, and
only care about the number of times substance-related aggression has occurred.
The “never, sometimes, often” measurement is another form of operationalizing variables.
There are four types of measurement scales we can use in quantitative research:
1. Nominal: categories with no numerical value (e.g. male/ female, or the language of the
questionnaire)
2. Ordinal: rank ordering (like movie ratings, or “never, sometimes, often”)
3. Interval: numerical values but no true zero (temperature, intelligence scores, extraversion,
intuitiveness)
4. Ratio: there is a true zero, where zero means the absence of the variable measured (weight, age,
BMI, depression)
Reliability and validity is incredibly important in measurement. Any test score we use or view = the
true score + any error score. Reliable tests have low error, i.e. Cronbach’s alpha > 0.7. Tests must
be reliable in order to be valid.
To recap: what makes a good experiment? Random assignment, large sample size, careful
planning so that confounding variables are eliminated, and valid and reliable operationalizations of
variables.
Lecture 7: Ethics
Research as well as research methods always need to be performed ethically. The Belmont Report
gave us three fundamental principles:
1. Beneficence: maximize the benefits of the research and minimize the risks, i.e. confidentiality
2. Autonomy: people are treated as capable of making decisions about whether or not to participate in
research, i.e. a consent form needs to be signed before the participants take part in the research
3. Justice: fairness regarding receiving both the benefits and the burdens of the research
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