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PYC3704 Full Summary

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  • August 25, 2021
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  • 2021/2022
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PYC3704 Summary

Topic 1: Quantitative methods in research psychology
1.1 Quantitative research in psychology
What is psychology?
Psychology is a discipline that strive to collect info and develop
theories about mental processes. Psychologist’s aim is to
establish facts that are valid and can be proven on scientific
grounds.
Empirical knowledge: based on the observation of physical
events for example, contemplation, unexplained insights,
mystical experiences and claims by authority.
Theories: explain why things are as we observe them.
Quantitative = numbers
Inference: conclusions that follow from certain info.
Inferential statistics: generalisations based on imperfect
numeric data, it has a high probability to be true, but not
completely certain.


1.2 Constructs as the building blocks of theories
Constructs: concepts that have been abstracted out of our
experience of human behaviour that serve as explanations for
certain aspects of behaviour.
Theories: framework for facts


1.3 How constructs are made visible through measurement
Variables: refers to a number that can take on any one of a
range of possible values.
Types:

, 1. Discrete: only whole numbers (1, 2, 3).
2. Continuous: real numbers
Constants: can only take on a single size. In contrast with
variables.
Two types of variables:
Dependent: the focus of the research (Y). Being tested or
measured.
Independent: something that the researcher manipulates to
see how it affects the dependent variable. (X).
Hidden variables: affects on the dependent variable we are
not aware of.
Hawthorne effect: people change their behaviour when they
realize that someone is paying extra attention to them.


1.4 Collecting info by sampling data
Data: collected info
Inferential statistics: use of statistical techniques to make
generalisations among the relationship between two variables.
Descriptive statistics: parametric statistics.
Population: people or objects you are interested in studying.
Sample: to take only a part of the population and “guess”
certain characteristics about them based on the sample.
Simple random sample: where everyone has the same chance
being included.
Types of sampling:
 Random sampling: where each member of the population
has an equal chance of being included in the sample.
 Systematic sampling: selecting individuals at fixed
intervals.
 Stratified sampling: dividing population into homogeneous
subgroups and drawing random samples.

,  Cluster sampling: sampling individuals from well-
delineated areas who have characteristics found in the
rest of the population.
Convenience sample: where a researcher has no choice but to
make use of the participants they can find for financial or other
reasons.
Population mean: µ (muu)
Population standard deviation: σ (sigma)
Sample mean: x
Standard deviation: s
Measurement errors:
Assumptions we can make:
 We assume that any variable contains a ‘true’ element
and an ‘error’ component.
 We assume that the mean of the error component is 0. We
can do it because it is reasonable to assume that positive
and negative deviations from a perfect score cancel each
other out.
 Error terms are distributed around the mean of 0 in a
normal distribution.
 x 0 - true measurement
 x - The actual intensity of the construct that the
measurement represents
 e – error component, error variance (spread of
measurements)
 x = x0 + e


1.5 The research hypothesis
Hypothesis: educated guess
Operational hypothesis: hypothesis that is stated clearly and
specifies exactly what to observe and what should be true
when valid.

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