Scientific processes

• Aim


  A general statement of what the researcher intends to investigate, essentially the purpose of the study. E.g. To investigate whether drinking energy drinks makes people more talkative.
• Hypothesis


  a testable statement predicting the outcome of the study
• Non-directional (two-tailed) hypothesis


  Predicts there will be a difference or correlation
• Directional (one-tailed) hypothesis


  Predicts the direction of the results
• Null hypothesis


  Predicts there will be no difference or relationship
• Sample


  a subset of the population who take part in the research
• Population


  The large group of people the researcher wants to study and from which the sample is selected
• Opportunity sampling


  selecting people who are available and willing to participate in the study
• Volunteer sampling


  participants select themselves to be part of the study by responding to an advert placed by the researcher asking for people to take part
• Random sampling


  selecting participants using a lottery method (e.g. drawing names from a hat or a computerised randomiser).ALL members of the target population have an equal chance of being selected.
• Stratified sampling


  from the wider population a sub-group is created (strata) based on age, social class etc. Then the population is randomly sampled within each strata
• Systematic sampling


  Every nth member of the target population is selected e.g. every 3rd pupil on a school register.
• Bias (sampling)


  refers to certain groups of people being over or under-represented within the sample selected
• Generalisation


  The extent to which findings and conclusions from a particular study can be broadly applied to the population.
• Strengths and weaknesses of an opportunity sample


  + Convenient

  - Unrepresentative/biased sample

  - Researcher bias
• Strengths and weaknesses of a volunteer sample


  + Requires little effort from researcher

  - Unrepresentative/biased sample.

  - P's will be more prone to demand characteristics.
• Strengths and weaknesses of a random sample


  + Free from researcher bias

  - Difficult and time consuming to obtain sample

  - Participants selected may refuse to take part
• Strengths and weaknesses of a stratified sample


  + Produces a representative sample and generalisation is possible.

  + Avoids researcher bias.

  - Can be very time consuming.

  - Requires a detailed knowledge of the population characteristics.
• Strengths and weaknesses of a systematic sample


  + Avoids researcher bias

  + Fairly representative

  - Can still result in a unrepresentative sample
• Pilot study


  a small-scale trial run of a study that takes place before the real investigation is conducted.
• Aims of pilot study


  To check that the procedures and materials work, and the instructions to participants are clear.

  This allows the researcher to identify any potential issues and to modify the design or procedure
• Experimental design


  Refers to how participants are allocated to the different conditions (or IV groups) in an experiment.
• Repeated measures design


  Each participant takes part in all conditions of the experiment
• Independent groups design


  Participants only take part in one of the conditions of the experiment. They are usually randomly allocated to one of the conditions
• Matched pairs design


  Pairs of participants are matched on a key variable/s (i.e. IQ score) then one participant from each pair would be randomly allocated to a different condition of the experiment
• Strengths & weaknesses of matched pairs design


  +The issue of participant variables is greatly reduced.

  + Order effects are avoided.

  + Demand characteristics less likely.

  - Much more time-consuming compared to IGD & RMD

  - Can be difficult to match p's exactly
• Strengths and weaknesses of an independent groups design

  + Less likely to be demand characteristics

  + Order effects are avoided

  - Participant variables/individual differences may affect results

  - More p's needed compared to RMD
• Strengths and weaknesses of repeated measures design


  + Participant variables less likely to affect results

  + Less p's needed compared to IGD

  - Order effects are likely to occur

  - Demand characteristics more likely
• Independent Variable (IV)


  the variable that the researcher manipulates
• Dependent Variable (DV)


  the variable that is measured by the researcher
• Extraneous variable (EV)


  Any variable, other than the independent variable (IV), that may have an effect on the dependent variable (DV) if it is not controlled.
• Confounding variable


  Any variable, other that the independent variable (IV), that has not been controlled so does affect the DV. Therefore we cannot be sure of the true reason for the changes to the DV/difference found.
• Operationalisation of variables


  The process of clearly defining variables so they can be measured
• Control group


  In an experiment, the group that is not exposed to the manipulation of the IV - contrasts with the experimental group and acts as a comparison group.
• Control


  These minimise the effects of variables other than the independent variable
• Random allocation


  Allocating participants to experimental groups or conditions using random selection methods. This can reduce the effects of participant variables/individual differences
• Counterbalancing


  Used to deal with order effects that arise when using a repeated measures design. The group is split in half with one half completing the two conditions in one order (condition A, then B) and the other half completing the conditions in the reverse order (condition B, then A).
• Randomisation


  The use of chance in order to control for the effects of bias when designing materials and deciding the order of conditions.
• Standardisation


  Using exactly the same formalised procedures and instructions for all participants in a research study.
• Demand characteristics


  participants may receive 'cues' from the researcher or the investigation and change their behaviour as a result.