Changing the way you learn

Intro: Psychological Investigations

Self Report
1. Questionnaires and Interviews
  1. Open/Closed Questions
  2. The main strength of self-report methods are that they are allowing participants to describe their own experiences rather than inferring this from observing participants
  3. However participants may not respond truthfully, either because they cannot remember or because they wish to present themselves in a socially acceptable manner. Social desirability bias can be a big problem with self report measures as participants often answer in a way to portray themselves in a good light.
  4. Rating Scales
    1. Likert scale
      1. Strength - is that they can give us an idea about how strongly a participant feels about something. This therefore gives more detail than a simple yes no answer. A further strength is that the data are quantitative data which are easy to analyse statistically.
      2. Weakness - with the scales for people to respond towards the middle of the scale perhaps to make them look less extreme. Participants may provide the answers that they feel they should and importantly as the data is quantitative it does not provide in depth replies.
  5. Fixed Choice Questions - Yes/No
2. Reliability
  1. A experiment is said to be reliable or consistent if the study can produce similar results if used again in similar circumstances. (ability to repeat)
3. Validity
  1. This refers to whether a study measures or examines what it claims to measure or examine. It is argued that qualitative data is more valid than quantitative data.
4. Sampling - psychologist use sampling techniques to choose people who are representative of the population as a whole.
  1. Opportunity Sampling - Taking the sample from people who are available at the time the study is carried out and fit the criteria your are looking for.
  2. Self selected sampling - consists of participants becoming part of a study because they volunteer when asked or in response to an advert. It is useful as it is quick and relatively easy to do. It can also reach a wide variety of participants. However, the type of participants who volunteer may not be representative of the target population for a number of reasons.
  3. Random Sampling - This is a sampling technique which is defined as a sample in which every member of the population has an equal chance of being chosen. For example pull names out of a hat
  4. Stratified Sampling - Stratified sampling involves classifying the population into categories and then choosing a sample which consists of participants from each category in the same proportions as they are in the population.
  5. Snowball Sampling - Snowball sampling can be used if your population is not easy to contact. You could ask a participant who fits your target population to tell their friends about the study and ask them to get in touch with the researcher and so on.
Experiment
1. Laboratory experiments
  1. A laboratory experiment is an experiment conducted under highly controlled conditions.
  2. By changing one variable (the independent variable) while measuring another (the dependent variable) while we control all others, as far as possible,
  3. It is argued that laboratory experiments allow us to make statements about cause and effect, because unlike non-experimental methods they involve the deliberate manipulation of one variable, while trying to keep all other variables constant.
  4. Demand characteristics - If a participant knows they are in an experiment they may seek cues about how they think they are expected to behave.
  5. Ethics - For example, experiments often involve deceiving participants to some extent. However, it is possible to obtain a level of informed consent from participants.
2. Field Experiments
  1. real world situation
  2. The independent variable is still manipulated unlike in natural experiments. Field experiments are usually high in ecological validity and may avoid demand characteristics as the participants are unaware of the experiment.
3. Quasi or natural experiments
  1. A quasi experiment is where the independent variable is not manipulated by the researcher but occurs naturally.
  2. It is worth noting that quasi experiments are very common in psychology because ethically and practically they are the only design that can be used.
4. Experimental Design
  1. Independent Measures Design
    1. use two conditions with different particapnts
  2. Repeated Measures Design
    1. uses same participant in each condition
  3. Matched Pairs Design
    1. different participants in each group are as simular as possible
5. Hypothesis
  1. It is important that the independent and dependent variables are clearly stated in the hypothesis.
  2. When a hypothesis predicts the expected direction of the results it is referred to as a one-tailed hypothesis.
  3. When a hypothesis does not predict the expected direction of the results it is referred to as a two-tailed hypothesis.
  4. The hypothesis that states the expected results is called the alternate hypothesis because it is alternative to the null hypothesis.
  5. The null hypothesis is not the opposite of the alternate hypothesis it is a statement of no difference.
6. Descriptive Statistics
  1. Simply offer us a way to describe a summary of our data. Inferential statistics go a step further and allow us to make a conclusion related to our hypothesis. You may be pleased to know that we will not be doing inferential statistics until the second year.
7. Mean
  1. Add/Divide
8. Median
  1. In numerical order, middle number
9. Mode
  1. Most Common
10. Graphs
  1. Bar Chart
  2. Scatter Diagram
  3. Box Plot
Observation
1. Observational studies -investigations where the researcher observes a situation and records what happens but does not manipulate an independent variable.
2. Tend to be high in ecological validity as there is no intervention and if the observer remains undetected the method avoids problems with demand characteristics.
3. Strength - observational studies is that they get to see how participants actually behave rather than what they say they do. observational studies is that they offer ways of studying behaviour when there are ethical problems with manipulating variables. For example there will be less ethical issues.
4. Weakness - hard to repeat
5. Observations do not provide information about what participants are thinking or feeling.
6. Participant observation - is a type of observational study where the observer is also a participant in the activity being studied. This type of observation can be useful because it provides more insights about behaviour but does have a problem that the observer may lose some objectivity.
7. Structured observation - is where the researchers design a type of coding scheme to record the participants' behaviour. Structured observations generally provide quantitative data. Coding schemes are ways of categorising behaviour so that you can code what you observe in terms of how often a type of behaviour appears.
8. Controlled Observation - occurs when the researchers control some variables. These observations may be carried out in laboratory situations or natural situations.
9. Sampling observational data
  1. Event Sampling - researcher recoding an event every time it happens
    1. Strength - Wont miss anything that the participant does
    2. Weakness - may become difficult to recorded every event that happens
  2. Time Sampling - researcher decides on a time for example 5 seconds and then records what behaviour is occurring a at that time.
    1. Strength - Lots of depth Quantitative Data
    2. Weakness - it is time consuming going through every piece of data individually. Alsi difficult within a group to watch variety of people
10. Validity - If participants are aware they are being observed they may behave in the way they feel they should behave. Validity could also be reduced by observer bias. That is the observer may be influenced by expectations and not record objectively what happened
  1. This then can be improved by putting information in categories so they are coded in a different or clearer way. Observers could be kept unaware of the aims of the observation or more observers could be employed.
11. Reliability - A measurement is said to be reliable or consistent if the measurement can produce similar results if used again in similar circumstances.
  1. A common way of assessing the reliability of observations is to use inter-rater reliability. This involves comparing the ratings of two or more observers and checking for agreement in their measurements.
Correlation
1. Correlation refers to a measure of how strongly two or more variables are related to each other.
  1. A positive correlation means that high values of one variable are associated with high values of the other. Or if you like, the variables increase together.
  2. A negative correlation means that high values of one variable are associated with low values of the other. Or if you like, as one variable increases the other decreases.
  3. If there is no correlation between two variables they are said to be uncorrelated.
2. A correlation coefficient refers to a number between -1 and +1 and states how strong a correlation is. If the number is close to +1 then there is a positive correlation. If the number is close to -1 then there is a negative correlation. If the number is close to 0 then the variables are uncorrelated.
  1. +1.0 perfect positive +0.8 strong positive +0.5 moderate positive +0.3 weak positive + 0.1 very weak positive 0 no -0.1 very weak -0.3 weak negative -0.5 moderate negative -0.8 strong negative -1.0 perfect negative
3. A hypothesis is a testable, predictive statement. The hypothesis will state what the researcher expects to find out.
4. A hypothesis for correlation is a prediction of a relationship and not a difference or cause and effect. Therefore you should never write a hypothesis for correlation that includes the words difference, cause or effect
5. Descriptive Statistics
  1. In correlational analysis the data is summarised by presenting the data in a scattergraph (or scattergram)
  2. It is important that the scattergram has a title and both axes are labelled.
6. Evaluation of Correlational Analysis
  1. Correlations are very good for showing possible relationships between variables and some times are the only practical or ethical way of carrying out an investigation.. Many researchers use it at a starting point for research
  2. However correlational analysis cannot demonstrate a cause and effect relationship between variables.

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Intro: Psychological Investigations

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