RM 1

• What is descriptive statistics?

  The use of graphs, tables and summary statistics to identify trends and analyse sets of data.
• What are measures of central tendency?

  The general term for any measure of the average value in the set of data.
• Measures of central tendency - mean:

  The arithmetic average calculated by adding up all the values in a set of data and dividing it by the number of values there are.

  It is the most sensitive of the measures of central tendency as it includes all of the scores/values in a data set within the calculation.

  This means it is more representative of the data as a whole.

  The mean is easily distorted by extreme values.
• Measures of central tendency - median:

  The central value in a set of data when values are arranged from lowest to highest.

  Extreme scores do not affect it, however it is less sensitive than the mean as not all scores are included in the final calculation.
• Measures of central tendency - mode:

  The most frequently occurring value in a set of data. In some data sets there may be two modes (bi-modal) or no mode if all the scores are different.

  It is a very crude measure; not representative of the data as a whole.

  For some data, data in categories, the mode is the only method you can use e.g. identifying the most "typical" or average value would be to select the modal group.
• What are measures of dispersion?

  Measures of dispersion is the general term for any measure of the spread or variation in a set of scores.
• Measures of dispersion - range:

  A simple calculation of the dispersion in a set of scores which is worked out by subtracting the lowest score from the highest score and adding 1 as a mathematical correction.

  It is easy to calculate, but takes into account extreme values; unrepresentative of the data set as a whole.
• Measures of dispersion - standard deviation:

  A sophisticated measure of dispersion in a set of scores. It tells us how much scores deviate from the mean by calculating the difference between the mean and each scores. All the differences are added up and divided by the number of scores. This gives the variance. The standard deviation is the square root of the variance.

  The larger the standard deviation, the greater the dispersion or spread within a set of data. A large standard deviation in an experiment suggests that not all participants were affected by the IV in the same way because the data is quite widely spread. It may be that there are a few anomalous results (and vise versa).

  A precise measure of dispersion than the range, but can be distorted by a single extreme value.
• Qualitative data:

  Data that is expressed in words and non-numerical.

  Qualitative data methods of collection are those that are concerned with the interpretation of language from, for example, an interview or an unstructured observation.

  More richness of details - broader in scope and gives participants more license to develop their thoughts and feelings and opinions on a given subject - greater external validity.

  More difficult to analyse - not statistical so patterns and comparisons within and between data may be hard to identify - conclusions rely on subjective interpretations (Biased).
• Quantitative data:

  Data that is expressed numerically; can be counted.

  Quantitative data collection techniques usually involve gathering numerical data in the form of individual scores from participants. Data is open to being analysed statistically and can easily be converted into graphs, charts etc.

  Relatively simple to analyse - comparisons can be easily be converted into graphs, charts etc.

  More objective and less open to bias.

  Much narrower in scope - less external validity.
• Primary data:

  Information that has been obtained first hand by the researcher for the purposes of a research project. It is also known as field research. In psychology, such data is often gathered directly from participants as part of an experiment, self-report or observations.

  Fits the job - authentic data obtained

  Requires time and effort
• Secondary data:

  Information that has already been collected by someone else and so pre-dates the current research project. In psychology, such data might include the work of other psychologists or government statistics,

  Inexpensive and easily accessible requiring minimal effort.

  Substantial variation in the quantity and accuracy of secondary data.
• What are correlations?

  A mathematical technique in which a researcher investigates an association / relationship between two co-variables.
• What are co-variables?

  The variables investigated within a correlation, for example height and weight. They are not referred to as the independent and dependent variables because a correlation investigates the association between the variables, rather than trying to show a cause and effect relationship.
• What is a positive correlation?

  As one co-variable increases so does the other.
• What is a negative correlation?

  As one variable increases, the other decreases.
• What is a zero correlation?

  When there is no relationship between the co-variables.
• The difference between correlation and experiments:

  In a experiment, the researcher controls or manipulates the independent variable (IV) in order to measure the effect on the dependent variable (DV). As a result of this deliberate change in one variable it is possible to infer that the IV caused any observed changes in the DV.

  In contrast, in correlation, there is no such manipulation of one variable and therefore it is not possible to establish cause and effect between one co-variable and another.

  Influences on other variables are also known as intervening variables.
• Strengths of correlations:


  1. Provide a precise and quantifiable measure of how the two variables are measured.

  2. Correlations are often used as a starting point to assess possible patterns between variables before experiments.

  3. Relatively quick and economical - no need for a controlled environment, secondary data is utilised.
• Limitations of correlations:


  1. Studies can only tell us how variables are related but not why - cannot demonstrate cause and effect between variables and therefore we do not know which co-variable is causing the other to change.

  2. Untested variable is causing the relationship between the two co-variables we are interested in - an intervening variable - third variable problem (uncounted variable)

  3. Misused or misinterpreted
• What are open questions?

  Questions for which there is no fixed choice of response and respondents can answer in any way they wish.
• What are closed questions?

  Questions for which there is a fixed choice of responses determined by the question setter.
• Designing questionnaires - Likert Scales

  A likert scale is one in which the respondent indicates their agreement (or otherwise) with a statement using a scale of usually five points. The scale ranges from strongly agree to strongly disagree.
• Designing questionnaires - Rating scales

  A rating scales works but gets respondents to identify a value that represents their strength of feeling about a particular topics e.g. very entertaining and not entertaining.
• Designing questionnaires - Fixed Choice Option

  Includes a list of possible options and respondents are required to indicate those that apply to them.
• Designing interviews:

  Most interviews involve an interview schedule, which is the list of questions that the interviewer intends to cover. This should be standardised for each participant to reduce the contaminating effect of interviewer bias. Typically, the interviewer will take notes throughout the interview, or alternatively, the interview may be recorded and analysed later.

  Interviews usually involve an interviewer and a single participant, through group interviews may be appropriate especially in clinical settings. In case of a one-to-one interview, the interviewer should conduct the interview in a quiet room, away from other people, as this will increase the likelihood the interviewee will open up. Answers will be treated in the strictest confidence.
• Writing good questions:

  Clarity is key when designing questionnaires and interviews. If respondents are confused by or misinterpret particular questions, this will have a negative impact on the quality of the information received. With this in mind, the following are common errors in question design that should be avoided where possible.
• writing good questions: overuse of jargon

  Jargon refers to technical terms that are only familiar to those within a specialised field or area.

  Where questions are unnecessarily complex the best questions are simple and easily understood.
• writing good questions: emotive language and leading questions

  The authors attitude towards a particular topic is clear from the way in which question is phrased. Emotive lang should be replaced with more neutral alternatives.

  Leading questions should be completely avoided.
• writing good questions: double-barrelled questions and double negatives

  A double-barrelled question contains two questions in one; the issue being that respondents may agree with one half of the questions and not the other.

  Finally, questions that include double negatives can be difficult for respondents to decipher.
• What are self-report techniques?

  Any method in which a person is asked to state their own feelings, opinions, behaviours or experiences related to a given topic.
• What is a questionnaire?

  A set of written questions used to assess a person's thoughts and/or experiences.

  Questionnaires involve a pre-set list of written questions (or items) to which the participant responds. Psychologists use questionnaires to assess thoughts and/or feelings. A study may simply consists of a question to find out about the kind of dreams people have or a long list of items designed to assess an individual's personality type.

  A questionnaire may be used as part of an experiment to assess the dependent variable.
• Open and closed questions:

  There are a number of possible styles of questions in a questionnaire but these can be broadly divided into open questions and closed questions.

  An open question does not have a fixed range of answer and respondents are free to answer in any way they wish. They produce qualitative data that is rich in depth and detail but may be difficult to analyse.

  A close question offers a fixed number of responses. Produces qualitative data that can be turned into quantitative data.
• Strengths of questionnaires:

  + Cost effective

  + Can gather large amounts of data quickly

  + Can be completed without the researcher being present

  + Straightforward to analyse - statistical analysis, comparisons can be made
• Limitations of questionnaires:

  + people can lie due to social desirability bias (type of demand characteristics)

  + response bias- answer in the same way e.g all yes or no etc. due to doing the questionnaire too quickly

  + Acquiescence bias- the tendency to say yes
• What are interviews?

  A 'live' encounter (face to face or on the phone) where the interviewer asks a set of questions to assess an interviewees thoughts/and or experiences. There are two types of interview: structured and unstructured.
• Structured interviews:

  Made up of a pre-determined set of questions that are asked in a fixed order - like a questionnaire but conducted face-to-face in real time.

  + Straightforward to replicate due to standardised format.

  + Format reduces differences between interviewers.

  x Cannot deviate from the topic
• Unstructured interviews:

  Works lot like a conversation - no set questions. There is a general aim that a certain topic will be discussed, and interaction tends to be free-flowing. The interviewee is encouraged to expand and elaborate their answers.

  + More flexibility - gain more insight

  x Analysis is not straightforward

  x Social desirability risk
• Semi-structured interview:

  There is a list of questions that have been worked out in advance but interviewers are also free to ask follow up questions when they feel it is appropriate.
• Unstructured observational design:

  One of the key influences on the design of any observation is how the researcher intends to record their data. The researcher may simply want to write down everything they see. This is referred to as an unstructured observation and tends to produce accounts of behaviour that are rich in details. This method may be appropriate when observation are small in scale and involve few participants.

  1. Qualitative data - difficult to analyse

  2. Benefit from more richer and in depth detail of data

  3. Greater risk of observer bias