Research Methods

• Lab Experiments

  • Takes place in a lab or controlled setting
  • Lab experiments seen as most scientific 
  • One variable (independent variable) is manipulated and other variables are kept constant and controlled so the effects can be seen on what is being measured (dependent variable)
  • Have EXPERIMENTAL and CONTROL conditions – the experimental group does something and the control group does not. The control group provides a BASELINE MEASURE
  • Ps should be randomly assigned to the experimental or control conditions
• Field Experiment

  Are carried out in the FIELD – a natural environment

  Have as many controls as possible and a manipulated IV

  THE SAME AS LAB EXPS BUT IN A DIFFERENT TYPE OF SETTING
• Confounding Variable

  Anything other than the IV which may have had an impact on the DV. This could be every-day problems such as noise, temperature etc, but can also include anything which reduces internal validity such as experimenter bias etc.
• Extraneous Variable

  Factors which we recognise as potential confounding variables. Efforts should be taken to control all confounding variables. e.g. sleep
• 4 types of extraneous variables

  Situational variables, Participant / Person variables, Experimenter / Investigator Effects, Demand characteristics
• Situational Variables

  These are aspects of the environment that might affect the participant’s behaviour e.g. noise, temperature, lighting conditions etc. Situational variables should be controlled so they are the same for all participants.

  Standardized procedures: are used to ensure that conditions are the same for all participants. This includes the use of standardized instructions
• Participant / Person variables

  This refers to the ways in which each participant varies from the other, and how this could affect the results e.g. mood, intelligence, anxiety, nerves, concentration etc.

  For example, if a participant that has performed a memory test was tired, dyslexic or had poor eyesight, this could affect their performance and the results of the experiment. The experimental design chosen can have an effect on participant variables.

  Participant variables can be controlled using random allocation to conditions of the independent variable.
• Experimenter / Investigator Effects

  The experimenter unconsciously conveys to participants how they should behave - this is called experimenter bias.

  The experiment might do this by giving unintentional clues to the participants about what the experiment is about and how they expect them to behave. This affects the participants’ behaviour.

  Also, the personal attributes of the experimenter can affect the behaviour of the participants. 

  Experimenter effects can be controlled by using a double blind. - experimenter + ppt unaware of which condition they are in
• Demand Characteristics

  These are all the clues in an experiment which convey to the participant the purpose/aim of the research.

  Participants will be affected by: their surrounding; the researcher’s characteristics; the researcher’s behaviour, and their interpretation of what is going on in the situation.

  Experimenters should attempt to minimise these factors by keeping the environment as natural as possible, carefully following standardised procedures. Finally, perhaps different experimenters should be used to see if they obtain similar results.
• Objectivity

  Researchers should remain totally value free when studying; they should try to remain totally unbiased in their investigations. I.e. Researchers are not influenced by personal feelings and experiences. Not influenced by opinion or bias

  Objectivity means that all sources of bias are minimized and that personal or subjective ideas are eliminated. The pursuit of science implies that the facts will speak for themselves even if they turn out to be different from what the investigator hoped.
• Reliability

  This refers to whether a particular method and finding can be repeated with different/same people and/or on different occasions, to see if the results are similar.

  If a dramatic discovery is reported but it cannot be replicated by other scientists it will not be accepted.

  If we get the same results over and over again under the same conditions, we can be sure of their accuracy beyond reasonable doubt.

  This gives us confidence that the results are reliable and can be used to build up a body of knowledge or a theory: vital in establishing a scientific theory.
• Validity

   this refers to the extent that we can be sure that the IV is causing the DV and the study is measuring what it set out to measure.

  Internal Validity  – The extent to which we can be sure that changes in the DV are caused by changes in the IV and nothing else.

  External Validityreflects real life.

  • The extent to which the study

  Ecological Validity – The extent to which findings of research can be generalised to real life.
• Directional Hypothesis (one tailed)

  A prediction which specifies the direction of the effect (i.e. one level of the IV will increase the DV)
• Non-directional Hypothesis (two tailed)

  A prediction which does not specify the direction of the effect (i.e. the IV will effect the DV
• Null Hypothesis

  A prediction that the IV will have no effect on the DV.
• Independent Groups design

  Every participant experiences only one level (or condition) of the IV, allowing comparison between the results from different groups.
• Repeated Measures design

  Every participant experiences every level (or condition) of the IV, allowing comparison between each participants scores in each condition.
• Matched Pairs design

  Every participant experiences only one level (or condition) of the IV, allowing comparison between the results from different groups. HOWEVER – participants in one condition are carefully ‘matched’ (i.e. same age, gender, socio-economic group etc) with participants in the other in order to reduce individual differences.
• Bar charts

  Used to represent ‘discrete data’ where the data is in categories, which are placed on the x-axis. The mean or frequency is on the y-axis. Columns do not touch and have equal width and spacing.
• Histogram

  Used to represent data on a ‘continuous’ scale. Columns touch because each one forms a single score (interval) on a related scale, e.g., time - number of hours of homework students do each week. Scores (intervals) are placed on the x-axis
• Scatter graph

  Used for measuring the relationship between two variables. Data from one variable is presented on the x-axis, while the other is presented on the y-axis. We plot an ‘x’ on the graph where the two variables meet. The pattern of plotted points reveals different types of correlation
• Descriptive Statistics

  Methods used to summarise the findings of research such as measures of central tendency, measures of dispersal and graphs.
• Inferential Statistics

   Methods used to test the significance of research findings, i.e. how does the observed value compare with the critical value.
• Critical Value

  The statistical value equating to a probability of 0.05 that differences between levels of the IV, (or relationship between co-variables) occurred by chance.
• Observed Value

  A statistical representation of the actual difference found between levels of the IV (or the actual relationship found between co-variables).
• Nominal Data

  A level of data where scores of participants have been grouped into categories.
• Ordinal Data

  Data where it is possible to rank scores in order.
• Interval data

  Data where each unit on a scale represents an equal interval, i.e. represents exactly the same quantity of the thing being measured, such as the Celsius measure of temperature.
• Inferential statistics

  tell us if differences or relationships are significant by comparing an observed value with a critical value.
• Observed value

  The result of a statistical test. This is a numerical representation of the difference between scores in two levels of an IV, (or the strength of a relationship between co-variables).
• Critical value

  The minimum statistical value required for a difference between scores in levels of an IV, (or the strength of a relationship between co-variables) to be considered significant.
• p= ≤0.05

  the level of probability used to establish critical values for inferential statistics. = less hat a 5% probability that differences occurred by chance.