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: 𝑥̅
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.
• 𝑥0 - 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: 𝑥̅
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.
• 𝑥0 - 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.
