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.
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.
