Psychology CHP.1 Flashcards
For SAC/Exam
Steps of scientific research
observation, asking a question, gathering information, forming a hypothesis, testing the hypothesis, making conclusions, reporting, and evaluating.
Independent variables (IV)
is the characteristic of a psychology experiment that is manipulated or changed
dependent variables (DV)
is the variable that is being measured or tested in an experiment
extraneous variables (EV)
are any variables that you are not intentionally studying in your experiment or test
hypothesis
testable prediction about what you expect to happen in a study
sampling procedures
is the process of selecting a representative group from the population under study
mean
“average”
median
The Median is the “middle” of a sorted list of numbers. middle number in a list of numbers
mode
The number which appears most often in a set of numbers
random sampling
Everyone in the entire target population has an equal chance of being selected.
quantitive data
are measures of values or counts and are expressed as numbers
qualitative data
Qualitative data are measures of ‘types’ and may be represented by a name, symbol, or a number code.
experimental designs
describes the way participants are allocated to experimental groups of an investigation. Types of design include Repeated Measures, Independent Groups, and Matched Pairs designs.
stratified sampling
The researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.
opportunity(convenience) sampling
Uses people from target population available at the time and willing to take part. It is based on convenience.
systematic sampling
Chooses subjects in a systematic (i.e. orderly/logical) way from the target population, like every nth participant on a list of names.
repeated-measures design
Where the same participants are allocated to all groups (i.e. take part in all conditions) of an experiment.
independent groups design
Where different participants take part in each experimental condition (they will be allocated randomly).
matched pairs design
Where participants take part in only one experimental condition, but they are recruited specifically to be similar in relevant characteristics (e.g. intelligence, gender, age) to ‘matched’ participants in the other condition(s)
repeated-measures design strengths
The results will not be subject to participant variables (i.e. individual differences between participants), putting more confidence independent variable changes being solely due to manipulated changes in the independent variable.
As the same participants are used [at least] twice, extra participants do not need to be recruited.
independent groups design strengths
Order effects cannot be observed, as no participants will be used in more than one condition.
Data collection will be less time-consuming if all conditions of the experiment can be conducted simultaneously.
matched pairs design strengths
Order effects will not be observed as participants only take part in one condition.
The tailored participant-matching process reduces the risk of participant variables (individual differences) from affecting results between conditions.
repeated-measures design weaknesses
There is risk of observing order effects (e.g. practice/fatigue effects, or demand characteristics), but this risk be reduced by counterbalancing (i.e. controlling the order of variables so that each order combination occurs the same number of times, e.g. one half of participants partake in condition A followed by B, whereas the other half partake in B followed by A).
If a participant drops out, data will be lost from all conditions of the experiment rather than one.
independent groups design weaknesses
Different participants need to be recruited for each condition, which can be difficult and expensive.
There is a risk of participant variables (individual differences between participants) affecting the results between conditions, rather than solely manipulation of the independent variable.
