research methods Flashcards
experimental methods
What are four different types of experiment:
- Laboratory experiment
- Field experiment
- Natural experiment
- Quasi experiment
experimental methods
Explain Laboratory Experiments
+ EVALUATION
- Laboratory experiments are conducted under specified controlled conditions in which the researcher manipulates the independent variable (IV) to measure the effect on the dependent variable (DV).
- The conditions are heavily controlled in order to minimise the effect of any extraneous variables, to prevent them from becoming a confounding variable which might adversely affect the DV.
- Participants will be aware that they are taking part in an investigation due to the contrived nature of the situation which may feel unlike real‐life.
EVALUATING LAB EXPERIMENTS
1. A strength of laboratory experiments is the high degree of control over extraneous variables which can be achieved. A researcher is therefore able, in most cases, to prevent extraneous variables from becoming confounding variables which negatively affect the DV. This provides a high degree of internal validity allowing for conclusions about cause and effect to be drawn between the IV and DV.
- A limitation of laboratory experiments is that they can lack external validity. The artificial nature of the environment in which the investigation is taking place means that the study can lack ecological validity. This means that the findings of the study cannot always be generalised to settings beyond the laboratory as the tasks often lack mundane realism and would not be everyday life occurrences. Since participants know they are being investigated their behaviour can also change in an unnatural manner resulting in demand characteristics being seen.
EXPERIMENTAL METHODS
what is field experiments?
+ EVALUATION
- Field experiments are carried out in natural conditions, in which the researcher manipulates the independent variable (IV) to measure the effect on the dependent variable (DV).
- The ‘field’ is considered any location which is not a laboratory.
- Participants in a field experiment typically do not know that they are taking part in an investigation with a view to observing more natural behaviour.
EVALUATION:
▪ The natural setting means that field experiments often have a higher level of ecological validity, in comparison to laboratory studies. This means that the results are more likely to be representative of behaviour witnessed in everyday life. However, because the setting is more natural, there is less control over extraneous variables. These can then become confounding variables and distort the findings meaning a firm cause and effect relationship cannot be drawn since other factors could have had an impact on the DV, other than the IV.
▪ There are important ethical issues associated with field experiments. Since participants are often unaware that they are in fact participants in a psychological investigation, they cannot give informed consent to take part. As such, the research may involve a breach of their privacy rights and a cost‐ benefit analysis will need to be conducted before proceeding with any study to ensure the perceived outcomes from the research will outweigh any personal costs to those involved.
EXPERIMENTAL METHODS
NATURAL EXPERIMENT?
+ EVALUATION
- In a natural experiment, the researcher does not manipulate the IV and instead examines the effect of an existing IV on the dependent variable (DV).
- This IV is naturally occurring, such as a flood or earthquake, and the behaviour of people affected is either compared to their own behaviour beforehand, when possible, or with a control group who have not encountered the IV.
- It is important to note that it is the IV which is natural in this type of experiment, and not necessarily the context in which the investigation is taking place since participants could be tested in a laboratory as part of the study.
EVALUATION:
- The naturally occurring IV means that natural experiments often have a higher level of external validity compared to laboratory and field experiments. These types of investigations are considered high in ecological validity given the real‐life issues that are being studied rather than manipulated artificially. However, natural experiments have no control over the environment and subsequent extraneous variables, which means that it is difficult for the research to accurately assess the effects of the IV on the DV. It may be that a confounding variable has affected the results so a cause and effect relationship must be drawn with extreme caution, if at all.
- A strength of using natural experiments is the unique insights gained into real‐life situations from using this methodology. Investigating a naturally occurring IV allows for research to be conducted into areas of psychology that could not be generated for ethical reasons or because of logistical and practical constraints. However, a naturally occurring event that interests researchers may only occur very rarely. This limits the opportunity to generalise the results to other similar events or circumstances.
EXPERIMENTAL METHODS
QUASI EXPERIMENT
+ EVALUATION
- Quasi experiments also contain a naturally occurring independent variable (IV), but one which already exists.
- However, in this instance the IV is a difference between people such as gender, age or a personality trait.
- The researcher examines the effect of this IV on the dependent variable (DV).
- Quasi experiments do not have to be conducted in a natural setting, although they often are.
- They may also be conducted in a laboratory setting, under controlled conditions.
EVALUATIONS:
▪ A limitation of quasi experiments is that participants cannot be randomly allocated to research conditions to remove the issue of bias in the procedure. Since the IV is a naturally occurring difference between the participants, the level of IV to which they belong is predecided. This means the psychologist will be less certain that the IV alone will have caused the effect which is measured through the DV as other dispositional or environmental factors may have played a role in the outcome. That being said, quasi experiments allow researchers to compare different types of people easily to provide insight into similarities or differences between these groups which could not be ethically generated otherwise.
▪ There are methodological issues associated with conducting quasi experiments. When quasi experiments take place under natural conditions, there is no control over the environment and subsequent extraneous variables, making it difficult to be sure that factors such as age, gender or ethnicity have affected the DV. On the other hand, when quasi experiments take place under laboratory conditions the high level of control means that the research often lacks ecological validity, and the findings cannot always be generalised to a real‐life setting since behaviour may not translate outside of the research environment.
OBSERVATIONAL TECHNIQUES:
What are the observational techniques?
When conducting an observation, the researcher has the choice between:
▪ Covert and overt
▪ Participant and non‐participant
▪ Naturalistic and controlled
▪ Structured and unstructured
OBSERVATIONAL TECHNIQUES:
COVERT OBSERVATIONS
+ EVALUATION
- A covert observation is also known as ‘undisclosed’ observation and consists of observing people without their knowledge; for example, using a one‐way mirror (covert non‐participant) or joining a group as a member (covert participant).
- Participants may be informed of their involvement in the study after the observation has taken place.
Evaluating Covert Observations :
* A strength of covert observation compared to overt observation is that investigator effects are less likely.
* Since the investigator is hidden in this type of observation there is less chance that their direct or indirect behaviour will have an impact on the performance of the participants.
* As a result, there is less chance of demand characteristics occurring whereby the participant tries to guess the aim of the investigation and act accordingly, since they are unaware that they are being observed.
* This means that the participants’ behaviour seen will be more natural and representative of their everyday behaviour.
- There are ethical issues associated with the covert method of observation inherent within its design. As participants are not aware they are taking part in an investigation, they cannot give fully informed consent nor exercise their right to withdraw. That being said, it is perfectly acceptable to observe human behaviour in a public place such as a shopping centre. This means that an assessment is made by the investigator before the observation begins to ensure that no privacy laws are being violated.
EXPERIMENTAL METHODS:
OVERT OBSERVATION:
+ EVALUATION
- An overt observation is an observational technique where the observations are ‘open’ and the participants know/are aware that they are being observed.
- For example, filming publicly (overt non‐participant) or joining a class and informing the other students that you are carrying out an observation (overt participant).
**Evaluating Overt Observations **
* A strength of the overt method is that it is often more ethical than the covert method. Since the participants are aware that their behaviour is being observed for the purposes of a psychological investigation, it is possible to inform them in advance of the aims and thus obtain informed consent. This awareness of participation also allows participants to exercise their right to withdraw themselves or their data from the investigation, before, during or after the observation is conducted. As a result, the reputation of psychological research as being ethical is protected.
- A drawback of using an overt style of observation is the possibility of investigator effects. It is possible for a bias to occur whereby what the investigator does influences the behaviour of the participants in a way which was not intended (e.g. body language or facial expressions). As a result, the participants may change their behaviour through demand characteristics and act in accordance with their perception of the research aims. Therefore, authentic and natural behaviour is not being observed, thus reducing the internal validity of the observation because it is overt.
EXPERIMENTAL METHODS:
PARTICIPANT OBSERVATIONS
+ EVALUATION
- In a participant observation, the person who is conducting the observation also takes part in the activity being observed.
- It can be either covert (a group member quietly observing others without their knowledge) or overt (a group member using a camera to record the behaviour of other members with their full knowledge).
Evaluating Participant Observations ▪
* A strength of using participant observations in psychological research is that the researcher can obtain in‐depth data. Since the observer is in close proximity to the participants, they are able to gain a unique insight into the phenomenon in question. In addition, through participating they are unlikely to overlook any behaviour that, as an external observer, would be missed due to nuances only seen by becoming a participant of the activity itself. This means that a comprehensive understanding of human behaviour can be achieved using this method of observation.
- ▪ A disadvantage of using the participant observation method is the possibility of investigator effects and the impact of the researcher on the other participants’ behaviour. The mere presence of the investigator as a member within the group might influence the participants’ behaviour in a way which was not intended. Consequently, the participants may change their behaviour through demand characteristics and act in accordance with their perception of the research aims. Consequently, natural behaviour is not being observed, thus reducing the internal validity of the observation because the investigator is a participant.
EXPERIMENTAL METHODS
NON-PARTICIPANT OBSERVATIONS
+ EVALUATION
- In a non‐participant observation, the person who is conducting the observation does not participate in the activity being observed.
- This type of observation is quite common in educational settings, as in teacher evaluations
- for example, when an observer sits in the corner of the room and watches the lesson.
- The aim is for the observer to be as unobtrusive as possible and not engage with any of the activities happening.
Evaluating Non‐Participant Observations - An advantage of using a non‐participant observation method is that investigator effects are less likely compared to a participant observation. The researcher is often observing at a distance from the participant(s) and in some situations, not visible to them at all. As a result, the behaviour of the investigator is unlikely to have a negative impact on the behaviour of the participants. This means that behaviour observed is more likely to be representative of natural and unaltered human conduct.
▪ There are disadvantages associated with the non‐participant method of observation. Due to a lack of proximity to the participant behaviour being studied, the researcher might miss behaviours of interest. This means that unique insights which contribute to the understanding of the human behaviour being observed will be overlooked because of not being involved personally.
EXPERIMENTAL METHODS
NATURALISTIC OBSERVATIONS:
+ EVALUATION
- A naturalistic observation is an observation carried out in an unaltered setting in which the observer does not interfere in any way and merely observes the behaviour in question as it happens normally.
- An example of this would be an observation carried out in a shopping centre as people go about their daily business.
Evaluating Naturalistic Observations - A strength of using naturalistic observations is that a higher level of ecological validity can be achieved. In an observation of this design, the researcher records naturally occurring behaviour in the original environment in which it ordinarily occurs. This means that the behaviour being recorded is likely to be more representative of everyday activities and reflect spontaneous actions that sometimes occur incidentally.
- ▪ There are issues of ascertaining reliability with naturalistic observations. Since observations of this kind record behaviours which are occurring naturally as they unfold it is difficult, if not impossible, for the exact same conditions to be replicated. Consequently, the test‐retest method of checking reliability cannot be used with this type of observational design, as the researcher is not in control of variables. This means that research attempting to understand human behaviour using naturalistic observations often **lacks replicability. **
EXPERIMENTAL METHODS
CONTROLLED OBSERVATIONS
+ EVALUATION
- A controlled observation is conducted under strict conditions, such as in an observation room or laboratory setting where extraneous variables (such as time of day, noise, temperature and visual distractions) can be controlled to avoid interference with the behaviour being observed.
- Sometimes one‐ way mirrors can be used for these types of observations.
- If the participants know they are being observed, this is an overt method which is most commonly the case for controlled observations.
Evaluating Controlled Observations
* A strength of controlled observations is that they can be replicated to check for reliability. By their very nature, the variables are highly controlled in this type of observational design. This means that standardised procedures, the manipulation of the independent variable and control over extraneous variables can be repeated by the same, or different, researchers to assess the reliability.
▪ A criticism of controlled observation is that they have a lower level of external validity. The researcher records behaviour in an artificial environment with variables subject to strict manipulation. This high level of control comes at a cost with the setting of the observation feeling quite unnatural as a result. Therefore, the participants’ behaviour may alter in response meaning that the observation no longer represents real‐life occurrences causing the ecological validity of the findings to be questionable.
EXPERIMENTAL METHODS
STRUCTURED METHODS
+ EVALUATION
- In structured observations, the researcher uses coded ‘schedules’ according to a previously agreed formula to document the behaviour and organise data into behavioural categories.
- A behavioural category is when psychologists must decide which specific behaviours should be examined.
- This involves breaking the target behaviour (e.g. aggression) into components that can be observed and measured (e.g. hitting or kicking).
Evaluating Structured Observations
▪ A strength of structured observations is that the researcher can compare behaviour between participants and across groups. The use of operationalised behavioural categories makes the coding of the data more systematic. When there is more than one observer, the standardised behaviour schedule results in greater inter‐observer reliability. It is important for research methodologies to be consistent so that accurate comparisons can be made.
▪ However, there may be problems with ascertaining high internal validity in a structured observation. This is because the researcher may miss some crucial behaviours during the observation which is pertinent to the aim of the investigation. As a result, the findings portrayed may not provide the full picture about the behaviour in question, as they could lack the finer details. This is a problem because what was intended to be measured was not achieved in its entirety.
EXPERIMENTAL METHODS
UNSTRUCTURED OBSERVATIONS
+ EVALUATION
- An unstructured observation involves every instance of the observed behaviour being recorded and described in as much detail as possible.
- This is useful if the behaviour that researchers are interested in does not occur very often and is more usual with naturalistic observation.
Evaluating Unstructured Observations
▪ A strength of unstructured observation is the richness of data obtained. Since behaviour is recorded in great detail, researchers are able to obtain a comprehensive view of human behaviour. This adds to the internal validity of the observational technique.
▪ Additionally, this type of observation is also prone to observer bias due to the lack of objective behaviour categories. This is a problem because the observer may then only record behaviour which is of subjective value to them, and not a valid representation of what is being displayed. As a result, there may be a problem with inter‐observer reliability as there will be a lack of consistency in the observations recorded.
EXPERIMENTAL METHODS
In observations, What are the two methods of sampling?
+ EVALUATION
TIME SAMPLING
* This is where the observer records behaviour at prescribed intervals
* ie every 10 seconds
strengths
* Time sampling methods allow for a better use of time since fewe observations are made
weakness
* not every behaviour that is relevant to the investigation will be counted in between the time frames allocated
EVENT SAMPLING
* this is where an observer records the number of times that the target behaviour occurs
* e.g a tally
strengths
* every behaviour of interest to the researcher wil be counted from the beginning through to the end of the observation
weakness
* there is the possibility that some behaviours could be missed if there is too much happening at the same time, resulting in some not being coded
SELF REPORT TECHNIQUES
WHAT IS A QUESTIONNAIRES?
- Questionnaires are a type of ‘self‐report’ technique, where participants provide information relating to their thoughts, feelings and behaviours.
- They can be designed in different ways, and can comprise of open questions, closed questions or a mixture of both.
SELF REPORT TECHNIQUES:
OPEN QUESTIONS
+ EVALUATION
- Open questions allow participants to answer however they wish, and thus generate qualitative data since there is no fixed number of responses to select from.
- Responses to these types of questions provide rich and detailed data which can provide insight into the unique human condition.
Evaluation of Open Question Questionnaires
▪ A strength of using open questions is that there is less chance of researcher bias. This is especially true if the questionnaire is also anonymous, since the participant can answer the questions in their own words, without input from the researcher providing a set number of responses. Consequently, there is less chance of the responses being influenced by the researcher’s expectations.
▪ However, there are limitations of using questionnaires in psychological research. Participants may answer in a socially desirable way, where they try to portray themselves in the best possible light to the researcher. This means that the open response may lack validity as it is not their natural response.
SELF REPORT TECHNIQUES
CLOSED QUESTIONS
+ EVALUATION
Closed questions restrict the participant to a predetermined set of responses and generate quantitative data.
The types of closed questions:
* Checklist: This is a type of question where participants tick the answer(s) that apply to them.
- Likert response scale - This is a type of question where participants rate on a scale their views/opinions on a question. For example: Psychology is the most interesting A‐Level subject. (Circle the number that applies).
- ranking scale - This is where participants place a list of items in their preferred order.
- For example: Rank the following activities according to how much time you spend on them each day.
(1 = most time, 4 =least time).
□ Talking face‐to‐face
□ Talking on the telephone
□ Text messaging
□ Other (e.g. Snapchat or Instagram)
evaluation
▪ An advantage of using closed questions is the nature of the data collected which is quantitative. This type of data makes it easy to analyse the results statistically or in a graphical format. This is useful because direct comparisons can be made between groups of individuals. This means the researcher can look for patterns and trends in the data that can lead to further research being conducted.
▪ There are limitations to adopting a closed question format in questionnaire research. By sticking to a predetermined list of questions, the researcher is unable to pursue and explore responses that are of particular interest. Additionally, closed questions often produce a response bias. This can happen because the participant doesn’t take the time to read all the questions properly and, for example, selects ‘yes’ for each of their answers. This means that the data generated may lack internal validity.
SELF REPORT TECHNIQUES
What factors should be considered when designing questionnaires:
6 Points
Keep the terminology simple and clear.
Keep it as short as possible.
Be sensitive; avoid personal questions. If you must, collect personal information at the end.
Do not use leading questions.
Do not use questions that make assumptions or sweeping statements.
Pilot and modify the questionnaire.
SELF REPORT TECHNIQUES
INTERVIEWS
* WHAT ARE THEY
* 3 DIFF TYPES OF INTERVIEWS
- Interviews are another type of self‐report technique which predominantly take place on a face‐to‐face basis, although they can also happen over the telephone.
There are three different interview designs:
STRUCTURED INTERVIEWS
* interviews can take the form of participants just answering a predetermined list of questions
UNSTRUCTURED INTERVIEWS
* they can be more like a relaxed conversation between friends
SEMI-STRUCTURED INTERVIEWS
* many fall between the two
- Responses are usually recorded, with the use of an interview schedule that the interviewer completes and/or audio or video recording, with the informed consent of the interviewee(s).
SELF REPORT TECHNIQUES
STRUCTURED INTERVIEWS
+ EVALUATION
- Structured interviews have the questions decided on in advance and they are asked in exactly the same order for each interviewee taking part.
- The interviewer uses an interview schedule and will often record the answers to each question by taking notes/ticking boxes on their schedule.
Evaluation of Structured Interviews
▪ An advantage of using structured interviews in psychological research is that the quantitative (numerical) data is easier to statistically analyse. This is useful because direct comparisons can be made between groups of individuals meaning that the researcher can look for patterns and trends in the data. Additionally, because the questions are standardised and asked in the same sequence every time to all participants, the interview is easily replicable to test for reliability.
▪ There are disadvantages of using the structured interview method. It is possible that over the course of running several interviews following the same schedule with different participants, that investigator effects may play a role. This is where the interviewer may, unconsciously, bias any responses given to the questions they ask by their tone of voice, intonations, body language and so on. Likewise, investigator effects can also occur between researchers where there is more than one researcher conducting the interviews.
SELF REPORT TECHNIQUES
UNSTRUCTURED INTERVIEWS:
+ EVALUATION
- Unstructured interviews are conducted more like a conversation, with the interviewer only facilitating the discussion rather than asking set questions.
- Very little is decided in advance (only the topic and questions needed to identify the interviewee).
- Therefore, this type of interview typically produces a large amount of rich qualitative data.
- Answers will usually be audio or video recorded, as to write them all down as quickly as they were spoken would be impossible for the interviewer, and would also spoil the relaxed atmosphere of the unstructured interview.
**Evaluation of Unstructured Interviews **
- The use of unstructured interviews can increase the validity of findings by significantly reducing the possibility of investigator effects. The open question schedule in unstructured interviews means that the investigator does not control the direction of the conversation to meet their own preconceived agenda. Participants can justify their answers in their own words with opinions rather than trying to guess the aim of the study through any clues given. This is useful because it reduces the possibility of participants displaying demand characteristics in their interview responses.
- Unstructured interviews generate large quantities of rich and interesting qualitative data. This allows the interviewer to clarify the meaning and gain further information from the participant if required to full understand complex human behaviour. However, unstructured interviews are more time consuming and costly, as this type of interview requires a trained psychologist to administer them. A further issue with this method is that statistical analysis can be challenging, as the data collected is qualitative, making it more difficult to identify patterns and trends without undergoing a content analysis first.
SELF REPORT TECHNIQUES
SEMI - STRUCTURED INTERVIEWS
+ EVALUATION
- Semi‐structured interviews comprise of mostly prepared questions that can be supplemented with additional questions as seen fit by the interviewer at the time.
- As with unstructured interviews, the interviewer can deviate from the original questions and consequently this type of interview also typically produces rich qualitative data.
**Evaluation of Semi‐Structured Interviews **
▪ The use of semi‐structured interview can increase the validity of findings. The open questions in semi‐ structured interviews may encourage the participant to be honest in their answers, thus reducing social desirability bias as participants are able to justify their answers in their own words with opinions. However, the interviewer still retains control over the semi‐structured interview schedule compared to an unstructured interview, which can result in investigator effects which can affect the behaviour of the participants negatively.
▪ Semi‐structured interviews generate rich and interesting qualitative data. As with unstructured interviews, this allows the interviewer to clarify the meaning of the participants’ responses and gain further information if required. This provides a unique insight into explaining human behaviour. However, as a result, analysis of such data can be more difficult, time consuming and expensive to conduct compared to quantitative data which is easier to statistically analyse without undergoing further processing beforehand.
CORRELATIONS
What is a correlational Technique?
- Correlational techniques are non‐experimental methods used to measure how strong the relationship is between two (or more) variables.
- In an experiment, the effect of an independent variable upon the dependent variable is measured; however, in correlational studies the movement and direction of co‐variables in response to each other is measured.
- There is no claim of a cause and effect relationship,
although after a correlational study has been conducted, further research will often be conducted to determine if one variable is in fact affecting the other. - A real‐world example of this is seen with cigarette‐smoking and lung cancer: first it was noticed that there was a positive correlation between the number of cigarettes smoked and the likelihood of developing lung cancer. Later, this research was extended and a cause and effect relationship was discovered between cigarette‐smoking and lung cancer.
CORRELATIONS
what are the 3 different types of correlation?
Positive correlation:
As one variable increases the other variable increases. For example – height and
shoe size
Negative correlation:
As one variable increases the other variable decreases. For example – the GCSE grades of students and the amount of time they are absent from school.
Zero correlation:
occurs when a correlational study finds no relationship between variables. For example – the amount of rainfall in Wales and the number of people who have read the Lord of the
Rings trilogy.
CORRELATIONS:
What is the correlation coefficient?
- A correlation coefficient is used to measure the strength and nature (positive or negative) of the
relationship between two co‐variables. - The correlation coefficient number can range between ‐1.0 and +1.0.
- The nearer the number is to +1 or ‐1 the stronger the correlation.
- A perfect positive correlation has a correlation coefficient of +1 and for a perfect negative correlation it is ‐1.
CORRELATIONS
what are scattergrams?
- A scattergram (sometimes called a scattergraph) is a graph that shows the correlation between two sets of data (co‐variables) by plotting points to represent each pair of scores.
- It indicates the degree and direction of the correlation between the co‐variables, one of which is indicated on the X‐axis and the other on the Y‐ axis.
CORRELATIONS
evalutae the correlational techniques:
- There are limitations associated with using the correlational method. It is not possible to establish a
cause and effect relationship through correlating co‐variables. This means a researcher cannot
conclude that one variable caused the other variable to increase/decrease as there could be other
factors which influenced the relationship – referred to as the third variable problem. Moreover,
correlations only identify linear relationships and not curvilinear. For example, the relationship
between temperature and aggression is curvilinear, that is the relationship is positive to a point;
however, at very high temperatures aggression declines.
STRENGTHS:
Correlations are very useful as a preliminary research technique, allowing researchers to identify a link that can be further investigated through more controlled research.
CaN be used to research topics that are sensitive/ otherwise would be unethical, as no deliberate manipulation of variables is required.
CASE STUDIES
WHAT ARE CASE STUDIES + EVALUATION
- The purpose of a case study is to provide a detailed analysis of an individual, establishment or real‐life
event. - A case study does not refer to the way in which the research was conducted, as case studies can use
experimental or non‐experimental methods to collect data. - For example, a researcher may want to interview the participants, provide a questionnaire to their family or friends
- Case studies are often used where there is a rare behaviour being investigated which does not arise often enough to warrant a larger study being conducted.
- A case study allows data to be collected and analysed on something that psychologists have very little understanding of, and can therefore be the starting point for further, more in‐depth research.
- Examples of famous case studies in psychology include: HM, Phineas Gage, Little Albert and Little Hans.
Evaluation of Case Studies
* There are methodological issues associated with the use of case studies. By only studying one
individual, an isolated event or a small group of people it is very difficult to generalise any findings to
the wider population since results are likely to be so unique. This therefore creates issues with external
validity as psychologists are unable to conclude with confidence that anyone beyond the ‘case’ will
behave in the same way under similar circumstances, thus lowering population validity.
- An issue in case studies, particularly where qualitative methods are used, is that the researcher’s own subjectivity may pose a problem. In the case study of Little Hans, for example, Freud developed an
entire theory based around what he observed. There was no scientific or experimental evidence to
support his suggestions from his case study. This means that a major problem with his research is that
we cannot be sure that he objectively reported his findings. Consequently, a major limitation with case
studies is that research bias and subjectivity can interfere with the validity of the findings/conclusions. - A strength of the case study approach is
that it offers the opportunity to unveil rich,
detailed information about a situation.
These unique insights can often be
overlooked in situations where there is
only the manipulation of one variable in
order to measure its effect on another.
Further to this, case studies can be used in
circumstances which would not be ethical
to examine experimentally. For example,
the case study of Genie (Rymer, 1993)
allowed researchers to understand the
long‐term effects of failure to form anattachment which they could not do with a human participant unless it naturally occurred.
What are Independant and Dependant variables?
Independent Variable (IV) –
The variable that the researcher manipulates and which is assumed to have a direct effect on the dependent variable (DV).
Dependent Variable (DV) – The variable that the research measures. The variable that is affected by changes in the independent variable (IV).
define Hypothesis + operationalism
HYPOTHESIS
a clear and precise prediction about the difference or relationship between the variables in the study.
Operationalisation is the term used to describe how a variable is clearly defined by the
researcher.
* The term operationalisation can be applied to independent variables (IV), dependent variables
(DV) or co‐variables (in a correlational design).
- The hypothesis should always contain an operationalised independent variable and dependent variable.
- For example, if the aim of a study was: to examine the effect of hunger on the memory of food‐related
words, the IV is hunger levels (hungry vs. not hungry) and the DV might be the number of food‐related
words correctly recalled.
What are the Two Types of Experimental Hypotheses?
Directional hypothesis:
predicts the specific nature (direction) of the difference between two or more variables.
* This prediction is typically based on past research, accepted theory or literature on the topic.
* sometimes called one‐tailed.
* Examples of key words used in a directional
hypothesis are: higher, lower, more, less, increase, decrease, positive and negative.
* Example: There will be a significant increase in the number of food‐related words correctly recalled by
the participants who are hungry, in comparison with those who are not hungry.
Non‐directional hypotheses:
* predicts that a difference will exist between two or more variables without predicting the exact direction of the difference.
* This is usually because previous research has
been inconclusive, and the specific nature (direction) of the effect of the IV on the DV cannot be
predicted confidently.
* Non‐directional hypothesis are sometimes called two‐tailed.
* The key word used is difference.
* Example: There will be a significant difference in the number of food‐related words correctly recalled for
participants who are hungry, in comparison with those who are not hungry.
SAMPLING:
What is Sampling?
- Sampling involves selecting participants from a target population.
- The target population is the particular subgroup to be studied, and to which the research findings will be generalised.
- A target population is usually too large to study in its entirety, so sampling techniques are used to choose a representative sample.
For example, a sample could be 20 A‐level students from a school that has 500 A‐level students in total.
SAMPLING:
What are the 5 common types of sampling?
Random
Systematic
Stratified
Opportunity
Volunteer
SAMPLING
Random sampling
+ evaluation
- With random sampling, every member of the target
population has an equal chance of being selected. - This involves identifying everyone in the target population and then selecting the number of participants you need in a way which gives everyone an equal chance of being selected,
- such as
pulling names from a hat, or using a computer software package which generates names/number randomly and without bias. - If a researcher was trying to achieve a random sample from 500 A‐level students in a school, they would place the name of each student on role into a hat/computer name generator and then select the first 20, for example, to be participants in their study.
Evaluation of Random Sampling
A strength of obtaining a random sample is that it is free from researcher bias. Since the sample is
generated by a computer generator or by selecting names from a hat the researcher does not have any
input into who is selected. This significantly reduces the possibility of them choosing a biased sample of
participants who would serve to support their aims. This means that the sample is likely to be
representative so can be generalised to the target population.
There are drawbacks associated with the random sampling procedure. Ensuring that everyone in the
target population has an equal chance of being selected is a difficult and time consuming task. It is also a possibility that individuals who are picked may be unwilling to take part. This results in the sample
being more akin to a volunteer sample.
SAMPLING
Systematic Sampling?
- With systematic sampling, a predetermined system is used to select participants.
- For example, every fifth person is chosen and the same interval is then consistently applied to the whole of the target population such as the 10th, 15th, 20th person and so on.
- If a systematic sample of 500 A‐level students in a school was required, a researcher would list every
student on role against a number, perhaps listed in alphabetical order, and then chose every 10th person to achieve a sample of 20 participants for their study (e.g. person 10, 20, 30, etc.)
Evaluation of Systematic Sampling
An advantage of using a systematic sampling system is that it is free from researcher bias. Since the researcher is not selecting participants by choice, but by following a predetermined system, this
reduces any potential influence that the investigator may have over obtaining the sample.
However, the systematic sampling method may not be truly unbiased. It might be that every Nth person
has a particular characteristic in common, for example being right‐handed. Although it would be fairly unlikely and unlucky to get a sample who were all similar on a particular trait, it remains a possibility
with using this technique. Therefore, the sample generated may not be representative meaning
generalisation to the target population would be more difficult.
SAMPLING
STRATIFIED SAMPLING
+ EVALUATION
- In stratified sampling, subgroups within a population are identified.
- Participants are obtained from each stratum (‘layer’ or category) in proportion to their occurrence within the population.
- For example, if a class of A‐level psychology had 20 students: 18 males and 2 females, and a researcher
wanted a sample of 10 to participate in their study, the sample would consist of 9 males and 1 female, to represent this population proportionally.
Evaluation of Stratified Sampling
A strength of obtaining a stratified sample is that it is largely free from researcher bias. In this
technique, the sample is generated randomly once the subcategories/strata have been identified. This
significantly reduces the possibility of the researcher choosing a biased sample of participants who
would serve to support their aims. This means that sample is likely to be representative because each
particular subgroup, if selected appropriately, will be represented within the sample. This means that
any findings generated from research with a stratified sample can be generalised to the target
population with greater confidence.
There are limitations associated with the stratified sampling method. Ensuring that the
subgroups/strata in target population are all accurately identified is sometime a difficult and time‐
consuming task. Furthermore, stratification is not a perfect process since the subgroups identified
cannot possibly reflect all the individual differences that exist between those in the target population.
Therefore, a truly representative sample would be extremely difficult to obtain using this technique.
SAMPLING
OPPURTUNITY SAMPLING
+ EVALUATION
- Opportunity sampling consists of selecting anyone who is available and willing to take part in the study at the time.
- This is a technique which is often used in psychological research due to its ease of application.
- For example, an opportunity sample from a school that has 500 A‐level students in total would involve approaching the students who were, for example, in the sixth form centre during their free period to ask them to participate in a study. The first 20 who agree to take part would form part of the sample.
Evaluation of Opportunity Sampling
A strength of opportunity sampling is the convenient nature of the technique. In comparison to all other sampling methods, obtaining an opportunity sample is quicker and easier since it requires less
effort on behalf of the researcher. As a result, it is likely to save money and is therefore favoured as the
most economical technique.
There are issues of bias, however, with an opportunity sample. As the sample is drawn from a very specific area or location, e.g. university, this means that it is likely only students will be available to take part who are not representative of the target population. In addition, there in an increased risk of
investigator bias as the researcher has complete control over who they approach. This means that they may select particular individuals or avoid others according to their own subjective preferences.
SAMPLING
VOLUNTEER SAMPLING
- Volunteer sampling consists of participants self‐selecting to take part in a study by either volunteering when asked or by responding to an advert.
- For example, a psychologist could place posters in various locations around a school asking for A‐level
students to volunteer to take part in their study, providing an email address to reply to or a time, date and venue to attend for participation. The first 20 volunteers would form part of their sample.
Evaluation of Volunteer Sampling
There are strengths of choosing to use a volunteer sample in a psychological investigation. In this way,
participants generally approach the researcher rather than the other way around. This means that the technique requires minimal effort and input on behalf of the researcher. As a result, this makes
obtaining a sample quicker and easier, in comparison to other methods.
There are issues of bias associated with volunteer sampling. Very often it is a particular type of person
that is likely to take part in research as only those who see the advert will come forward to participate.
Furthermore, those individuals who are more curious or inquisitive by nature may volunteer more
readily. Therefore, the sample is likely to be biased and not representative of the target population
which makes generalisation of the findings more difficult.
What are Pilot Studies?
- Pilot studies are small‐scale prototypes of a study that are carried out in advance of the full research to
find out if there are any problems with the following:
**Experimental design – **
do the participants have enough time to complete the tasks?
**Instructions for participants – **
are the instructions clear?
Measuring instruments –
They allow for categories and questions to be checked and modified where necessary.
Carrying out a pilot study beforehand is a way to ensure time, effort and money are not wasted on a
flawed methodology. It is important that a pilot study uses a sample that (although smaller) is
representative of the target population that will be used in the main research.
what are the three main experimental designs?
The three main types of experimental design are:
Repeated measures
Independent groups
Matched pairs
EXPERIMENTAL DESIGN
REPEATED MEASURES
- Repeated measures is a design where the same participants take part in each condition of the experiment.
- The data obtained from both conditions is then
compared for each participant to see if there
was a difference.
Evaluation of Repeated Measures
There are strengths associated with using
the repeated measures design. Since the
same participants are taking part in all
conditions of the experiment, fewer
participants are required. This makes the
design less costly and time consuming, as
fewer participants need to be recruited. In
addition, the use of the same participants
across conditions reduces the possibility of
participant variables such as individualdifferences playing a part in the different results obtained, meaning that the effect on the DV can be
attributed to the IV with more confidence.
There are issues with adopting a repeated measures design in psychological research. As the same participants take part in both conditions of the experiment, order effects can occur. Participants who experience practice effects may perform better in the second conditions as they know what is
expected of them. Participants who experience fatigue (boredom) may perform worse in the second
condition, because they give up. To address this issue, researchers can use counterbalancing which
offsets any order effects as half the participants take part in ‘Condition A’ followed by ‘Condition B’
while the other half complete the ‘Condition B’ followed by ‘Condition A’. Any order effects
experienced by those who started in Condition A should be offset by those who started in Condition B.
Furthermore, repeated measures experiments are also prone to demand characteristics as participants
are more likely to guess the aim of the experiment when they take part in both conditions.
EXPERIMENTAL DESIGN
INDEPENDANT GROUPS
- An independent measures design uses of two separate groups of participants; one group in each condition of the experiment.
- Participants should be allocated to their group (condition) by random allocation, which ensures that each participant has an equal chance of being assigned to one group or the other.
- This is important to reduce investigator effects, resulting in a biased sample being placed into the two conditions, and the influence of individual differences whereby participant variables influence the measurements taken in the DV (dependent
variable).
Evaluation of Independent Groups
A strength of using independent groups design is that it avoids order effects. As participants only take
part in one condition of the experiment, they are less likely to become bored and give up reducing the
impact of order effects. In addition, this research design also reduces demand characteristics, as
participants are only taking part in one condition of the experiment. This means that they are less likely
to guess the aim of the experiment and display demand characteristics, making the results higher in
validity.
There are disadvantages of using an independent groups design. More participants are required as
different people take part in the different conditions of the experiment. This makes the design more
expensive and time consuming for the researcher who must recruit more individuals to take part.
Additionally, participant variables may affect the results. For example, differences in age, sex or social
background may affect the results by acting as an extraneous variable on the DV which means that
psychologists cannot be certain that the IV caused the changes measured.
EXPERIMENTAL DESIGN
MATCHED PAIRS
- Pairs of participants are matched from the sample, in terms of key variables such as age or IQ.
- After matching takes place, the participants are treated much like those in independent measures.
- One member of each pair is placed in the experimental group and the other member in the control group.
Evaluation of Matched Pairs
There are benefits of adopting a matched
pairs research design in psychological
research. Because the researcher pairs up
the participants so that each condition has
people with similar abilities and
characteristics, this reduces participant
variables. In addition, order effects (such as
practice or fatigue) are less of an issue
compared to a repeated measures design as
the participants only take part in one
condition of the experiment and therefore
they are less likely to become bored and
give up.
There are issues involved with creating a
matched pairs research design. More participants are required, as different participants take part in
the different conditions of the experiment, making the design more expensive and time consuming for
the researcher. Furthermore, it is very difficult, if not impossible, trying to find close or exactly matched
pairs. This means that individual differences may still play a role in the measurement of the DV
reducing the certainty that the IV affected the change.
what is an extraneous variable?
Extraneous variables are any variable other than the IV that might affect the DV and thus affect the results.
* Where extraneous variables are important enough to cause a change in the DV, they become confounding variables.
There are many different types of extraneous variables that psychologists need to take account
of when designing their investigations:
Situational variables – variables connected with the research situation. For example, the temperature, time of day, lighting, materials, etc.
* They are controlled though standardisation, ensuring that the only thing which differs between the two groups is the IV.
Participant variables – variables connected with the research participants. For example, age,
intelligence, gender, etc. They are controlled through the experimental design, such as matched pairs
design, or by randomly allocating participants to conditions, which helps to reduce bias.
What is random allocation
- Random allocation of participants to their groups, for example in an independent measures design, is an extremely important process in psychological research.
- Random allocation greatly decreases the possibility
that participant variables in the form of individual differences, such as mathematical ability, will adversely affect the results.
what is counterbalancing
- To combat the problem of order effects with repeated measures design, researchers can counterbalance the order of the conditions.
- The sample is split in half with one half completing the two conditions in one order and the other half completing the conditions in the reverse order.
- Any order effects should be balanced out by the opposing half of participants.
- For example, the first ten participants would complete condition A followed by condition B but the remaining ten participants would complete condition B then A.
What is Randomisation?
This is when trials are presented in a random order to avoid any bias that the order of the trials might
present.
what is standardisation?
- This is the process in which all situational variables of a procedure used in research are kept identical, so
that methods are sensitive to any change in performance. - Under these circumstances changes in data can
be attributed to the IV. - In addition, it is far more likely that results will be replicated on subsequent occasions when research is standardised.
confounding variables
what are demand characteristics?
- Demand characteristics occur when the participants try to make sense of the research and change their behaviour accordingly to support what they believe are the aims of the investigation.
- Demand characteristics are a problem as the participants act in a way to support the hypothesis rather than displaying natural behaviour, making the results lack validity.
- Conversely, the participant may deliberately
try to disrupt the results, a phenomenon known as the ‘screw‐you’ effect. - Demand characteristics are controlled by not allowing the participants to guess the aim of the research or the identity of the IV which can be achieved by using a single‐blind experimental technique–>when only the researcher knows the true aim of the experiment, and a measure of deception has been used so that the participants cannot easily guess the aim.
- Therefore, they are unable to try to either support or undermine the research on purpose.
- An example of this is in medical tests when comparing the effects of a therapeutic drug with a placebo, where only the researcher knows which is which.
confounding variables
what is investigator affects?
- Investigator effects are where a researcher (consciously or unconsciously) acts in a way to support their prediction.
- This can be a problem when observing events that can be interpreted in more than one way.
- For example, one researcher might interpret children fighting as an act of violence, while another might observe this as rough and tumble play.
- Investigator effects are best controlled by not allowing either the participants or the researcher
(investigator) to know the aim of the research and/or identity of the IV. - This is achieved by using a double‐blind experimental technique. In this instance, only the person who originally designed the experiment
knows the true aim, and a measure of deception has been used so that the participants and researcher are not aware. Therefore, either consciously or unconsciously, the investigator is unable to influence the participants. - An example of this is in medical tests comparing the effectiveness of a therapeutic drug with a
placebo, where neither the researcher nor the participants knows which is which.
ETHICAL GUILDLINES
what did the british psychological society (BPS) code of ethics state regarding experiments?
The British Psychological Society (BPS) code of ethics sets out a series of guidelines that researchers need
to consider when undertaking psychological research. Six of the main ethical guidelines include:
Deception
Right to withdraw
Informed consent
Privacy and confidentiality
Protection from harm
Ethical issues
What is deception, why is unethical and how to deal with issue?
DECEPTION: When information is deliberately withheld from participants or they are knowingly misled.
**WHY IS IT UNETHICAL: **
It prevents participants from giving fully informed consent which means that they might be taking part in research that goes against their views or beliefs.
HOW TO DEAL WITH ISSUE:
At the end of the study the participants should be fully debriefed and told the true aim and nature of the research. At this point the participant should be
given the right to withdraw the publication of their results. The contact details of the experimenter should be given if participants have any further questions or queries.
ETHICAL ISSUES
What is right to withdraw, why is it unethical if broken , how to solve issue?
RIGHT TO WITHDRAW:
Participants have the right to withdraw (remove themselves or their data from the study) at any stage. This includes after the research has been conducted, in which case the researcher must destroy any data or information collected.
WHY IS IT UNETHICAL?
Participants who are not given the right to withdraw
may feel unnecessary or undue stress and are therefore not protected from harm.
HOW TO SOLVE:
At the end of the study the participants should be fully debriefed and told the true aim and nature of the research. At this point the participant should be given the right to withdraw the publication of their results. The contact details of the experimenter should be given if participants have any further
questions or queries.
ETHICAL ISSUES
what is informed consent, why is it an ethical issue if broken and how to deal with issue
INFORMED CONSENT:
When someone consents
to participate in research,
their consent must be fully
informed which means the
aims of the research should
be made clear before they
agree to participate.
WHY ITS UNETHICAL:
Lack of informed consent
may mean that the
participant is taking part in
research that goes against
their wishes or beliefs. It is
possible that the participant
may have felt obliged to take
part or even coerced into it,
especially if they are not fully
informed.
HOW TO SOLVE:
Presumptive consent: involves
taking a random sample of the
population and introducing them to
the research, including any
deception which may result. If they
agree to take part in the research it
can be presumed that other future
participants would do the same so
the consent is generalised.
Prior general consent: involves
participants agreeing to take part
beforehand in numerous
psychological investigations, which
may or may not involve deception.
This, in effect, means that they will
have given consent for being
deceived.
Retrospective consent: involves
participants giving consent for their
participation after already taking
part, for instance, if they were not
aware that they were the subject of
an investigation.
Children as participants: involves
gaining the consent of the parent(s)
in writing for children under the age
of 16 to participate in any
psychological research.
ETHICAL ISSUES
PRIVACY, UNETHICAL, SOLVE
PRIVACY:
Privacy is the right of
individuals to decide how
information about them will
be communicated to others.
HOW IT IS UNETHICAL:
A skilled researcher may
obtain more information from
a participant than they wish
to give which could be an
invasion of privacy and the
participant may later feel
ashamed or embarrassed.
SOLVE BY:
The participant should be provided
with fully informed consent and
the right to withdraw at any stage.
Furthermore, the researcher should
explain to participants the way(s) in
which their information will be
protected and kept confidential,
e.g. no names will be published in
the final report and any written
information or video information
will be destroyed.
ETHICAL ISSUES
CONFIDENTIALITY, UNETHICAL, SOLVE
CONFIDENTIALITY:Confidentiality is where a
participant’s personal
information is protected by
law under the Data
Protection Act both during
and after the experiment.
WHY ITS IS UNETHICAL IF BROKEN:
A person’s details or data may
be used by other parties
against the participant’s
wishes.
SOLVE BY :
Participants are provide with a fake
name, number or initials to protect
their identity and assure
anonymity. They should not be
identifiable by any person,
institution or organisatioN
ETHICAL ISSUES
PROTECTION FROM HARM, UNETHICAL, SOLVE BY
PROTECTION FROM HARM:
Psychologists have the
responsibility to protect
their participants from
physical or psychological
harm, including stress.
The risk of harm must be no
greater than that which they
are exposed to in everyday
life.
UNETHICAL:
Participants should leave the
research in the same state as
they entered it. If they are
harmed, they may suffer from
long‐term effects that could
impact their lives in future.
SOLVE BY:
The researcher should remind
participants of their right to
withdraw throughout and after the
research. The researcher should
terminate the experiment if the
level of psychological or physical
harm is higher than expected.
Participants should be debriefed at
the end of the experiment and in
some instances they may be
referred to counselling.
What is PEER REVIEW + EVALUATION
Peer review is an independent assessment process that takes place before a research study is published
and is undertaken by other experts in the same field of psychology. All psychologists must be prepared for
their work to be scrutinised in this way which is conducted anonymously.
There are several aims of the
peer review process:
To provide recommendations about whether the research should be published in the public domain or not, or whether it needs revision.
To check the validity of the research to ensure it is of a high quality.
To assess the appropriateness of the procedure and methodology.
To judge the significance of the research in the wider context of human behaviour.
To assess the work for originality and ensure that other relevant research is sufficiently detailed.
To inform allocation of future research funding to worthy investigative processes.
Evaluation of Peer Review
There are drawbacks associated with the peer review process. Since the peer reviewers are often
anonymous in their reporting on the academic research, there is a possibility that they will use this fact
as a means to criticise rivals in their field of psychology. This is perpetuated by the fact that there tends
to be limited funding for new research so the element of competition could bread jealousy amongst
researchers. As a result, inaccurate or unfair criticism may be received following the peer review
process which is not a valid reflection of the quality of the research.
An issue with the use of peer review is that it is sometimes difficult to find a suitable peer. This is
especially true when conducting psychological research on a new or ground‐breaking topic. A possible
consequence of this is that research which is not of high quality will be passed as suitable for
publication as the researcher did not fully comprehend the aims or content. Conversely, results may be
published which preserves the status quo by supporting existing theories more readily than more
unconventional research might, resulting in a positive publication bias.
Although peer review is not without its faults, there are merits to conducting the process on
psychological research. In particular, the process helps to prevent any substandard research from
entering the mainstream which serves to protect the reputation of the discipline. Likewise, as experts
within the field often act as peers, there is less opportunity for plagiarized work or duplications of
research to be published. This means that the journals who publish the work will be trusted for the
articles that they disseminate.
TYPES OF DATA
QUANTITIVE DATA
Quantitative data is numerical data that can be statistically analysed and converted easily into a graphical
format. Experiments, structured observations, correlations and closed/rating‐scale questions from
questionnaires all produce quantitative data.
Evaluation of Quantitative Data
A strength of quantitative data is that it is easy to analyse statistically. When large amounts of
numerical data are generated it is relatively easy to conduct descriptive statistics or inferential tests of
significance which allow for comparisons and trends to be identified between groups. Since established
mathematical procedures are in place for this type of analysis it makes quantitative data more
objective.
A disadvantage of quantitative data is its lack of representativeness. Since this type of data is often
generated from closed questions, the responses gained are narrow in their scope towards explaining
complex human behaviour. This means that, in comparison to qualitative data, the numerical findings
can often lack meaning and context. As such, it may not be a true representation of real life and thus
lacks validity.
TYPES OF DATA
QUALITATIVE DATA + EVALUATION
Qualitative data is non‐numerical, language‐based data expressed in words which is collected through
semi‐structured or unstructured interviews and open questions in a questionnaire. It allows researchers to
develop an insight into the unique nature of human experiences, opinions and feelings.
Evaluation of Qualitative Data
A strength of obtaining qualitative
data is the rich detail obtained by the
researcher. Since participants can
develop their responses freely this
provides the investigator with
meaningful insights into the human
condition. Because of this, the
external validity of findings is
enhanced as they are more likely to
represent an accurate real‐world
view.
A limitation of qualitative data is that
it can be subjective. Due to the rich,and often lengthy, detail of participants’ responses, interpretations of this type of data can often rely
on the opinions and judgements of the researcher. This means that any preconceptions that the
researcher holds may act to bias any conclusions drawn.
TYPES OF DATA
PRIMARY DATA + EVALUATION
Primary data refers to data that has been collected for a specific reason and reported by the original
researcher. It is data that the participant reports directly to the researcher (often via an interview
/questionnaire) or is witnessed first‐hand (via an observation/experiment). Primary data is sometimes
referred to as field research.
Evaluation of Primary Data
A strength of primary data is its authenticity. This is because it is collected with the sole purpose of
being for a specific investigation. Since the data collection is designed to suit the aims of the research,
this enables the researcher to exert a high level of control. This is advantageous as there is a greater
probability that the data generated will fit the aims of the investigation, reducing any wasted time on
behalf of the researcher and ensuring that the information prepared for analysis is relevant.
There are limitations of gathering primary data. Designing and carrying out a psychological study can
take a long period of time and considerable effort. This means that expenses can accrue due to the
time investment needed on behalf of the researchers in addition to any equipment that needs to be
purchased. Therefore, in comparison to primary data, secondary data which already exists can save the
researcher time, effort and money.
TYPES OF DATA
SECONDARY DATA+ EVALUATION
Secondary data is information that was collected by other researchers for a purpose other than the
investigation in which it is currently being used. In other words, it is data which already exists. Examples
include Government reports like the census or statistics about mental health from the NHS. It is sometimes
referred to as desk research because the significance of the data is already known.
Evaluation of Secondary Data
There are many strengths to using secondary data. Since the information already exists in the public
domain, it means that it is much less time consuming and expensive to collect. This means that
researchers can find the information they desire with very little effort. This makes the collection and
use of secondary data much easier when compared with primary data.
A limitation of using secondary data involves concerns over accuracy. Given the information was not
gathered to meet the specific aim of the research, it stands to reason that there may be significant
variability in the quality of the data. This means that much of the data may be of little or no value to
the researchers.
TYPES OF DATA
META ANALYSIS + EVALUATION
Meta‐analysis refers to a process whereby investigators combine findings from multiple studies (secondary
data) on a specific phenomenon to make an overall analysis of trends and patterns arising across research.
This can include a qualitative review of previous research or a statistical, quantitative analysis to test for
significance of effect size. An example of a meta‐analysis from developmental psychology is that conducted
by van Ijzendoorn et al., investigating cross‐cultural variations in attachment. In total, their analysis
examined 32 studies from eight different countries that had used Ainsworth’s strange situation. In total,
the results of over 1,990 infants were included in the analysis.
Evaluation of Meta‐Analysis
There are advantages of adopting a meta‐analysis methodology. Since the results are combined from
many studies, rather than just one, the conclusions drawn will be based on a larger sample which
provides greater confidence for generalisation. This, therefore, serves to increase the validity of the
patterns and trends identified.
There are issues of bias associated with meta‐analyses. Since the researcher is selecting data from
research which has already taken place, they may choose to omit certain findings from their
investigation. This could be particularly true if the previous findings showed no significant results or
were inconclusive. As a result, the findings and conclusions from the meta‐analysis will be biased as
they do not accurately represent all of the relevant data on the topic.
What is DESCRIPTIVE STATISTICS?
- Once quantitative data has been collected, it is important to summarise this data numerically.
- This quantitative summary is called descriptive statistics, and allows researchers to view the data as a whole.
- It also helps the reader to get an understanding of the data and saves them from needing to navigate
through lots of results to get a basic understanding of the data. - Descriptive statistics typically include a measure of central tendency and a measure of dispersion (which will have been selected based on the type of data collected), and can also include percentages.
what is the MEAN
- Perhaps the most widely used measure of central tendency is the mean.
- The mean is what people most are referring to when they say ‘average’: it is the arithmetic average of a set of data.
- It is the most sensitive of all the measures of central tendency as it takes into consideration all values in the dataset. Whilst this is a strength as it means that all the data is being taken into consideration, It can be very misrepresentative of the data set if there are extreme scores present.
- The mean is calculated by adding all of the data together, and dividing the sum by how many values there are in total.
- The value that is then given should be a value that lies somewhere between the maximum and minimum values of that dataset. If it isn’t, then there is a human error with the calculations!
What is the MEDIAN?
- the median takes the middle value within the data set.
- If there is an even number of values within the data set, there will be two values that fall directly in the middle. In this case, the midpoint between these two values is calculated. To do this, the two middle scores are added together and then divided by two.
- This value will then be the median score.
What is the MODE?
- This refers to the value or score that appears most frequently within the data set.
- Whilst easy to calculate, it can be quite misleading of the data set. Imagine if the lowest value in the example data set (12%) appeared twice. It wouldn’t be truly representative of the whole data set; however, this would be the mode score.
- A strength of using the mode is that it can be used on categorical data, whilst the mean and median cannot.
- However, it is also possible that there is no mode for a data set, if all of the values are different.
What is the RANGE?
- The range is calculated by subtracting the lowest score in the data set from the highest score in the data set and (usually) adding 1.
- The addition of 1 to the calculation is a mathematical correction which allows for the fact that some of the scores in the data set will have been rounded up or down.
- This value is very straightforward to calculate, which is a clear strength of using the range.
- However, it is important to recognise that a data set with a strong negative skew can have a similar range to a data set with a strong positive skew, in which case it may be providing a very limited insight into the data set.
- Equally, it is only taking into consideration the two extreme scores, which may not be an accurate representation of the data set as a whole.
What is STANDARD DEVIATION?
- A much more informative measure of dispersion is the standard deviation.
- The standard deviation looks at how far the scores deviate from the mean.
- If the standard deviation is large, this suggests that the data is very dispersed around the mean and, for example, the participants scored very differently. * However, if the standard deviation value is quite small, this suggests that the values are very concentrated around the mean, and that everyone scored relatively similarly to one other.
- The standard deviation score takes into consideration all of the values within the data set, and is a very precise measurement.
- However, in the same way as the mean, the fact that it takes into account every value means that it can be easily distorted by an extreme value, which could in turn mean that it misrepresents the data.
What is NORMALLY AND SKEWED DISTRIBUTION
- Data that is normally distributed produces a symmetrical bell‐shaped curve when plotted, indicating that most scores are close to the mean, with a progressively fewer scores being located at the extremes of either tail of the distribution.
- In this instance, the median and mode also occupy the same centre point of the curve as the mean does.
- However, sometimes data does not follow this symmetrical pattern which can result in a large proportion of scores falling below the mean (positively skewed) or after the mean (negatively skewed).
- In both instances, the mode remains at the highest point on the graph, since it is not affected by extreme scores.
What is content analysis?
- Content analysis is a type of observational technique which involves studying people indirectly, through qualitative data.
- Qualitative data collected in a range of formats can be used, such as video or audio recordings.
- Content analysis helps to classify responses in a way that is systematic, which can then allow clear conclusions to be drawn.
- It is important for researchers using content analysis to have their research questions formulated, so that they know exactly what their content analysis will focus on.
- Researchers must familiarise themselves with the data before conducting any analysis, so that they are confident that their coding system is appropriate for the task ahead.
- Content analysis is particularly helpful when conducting research that would otherwise be considered unethical.
- Any data that has already been released into the public domain is available for analysis, such as newspaper articles, meaning that explicit consent is not required.
- For material that is of a sensitive nature, such as experience of domestic violence, content analysis can also prove useful, as participants can write a report of their experience which can be used in analysis.
- This allows high quality data to be collected, even in difficult circumstances.
content analysis
what is CODING
- Coding is an important step in conducting content analysis and involves the researcher developing categories for the data to be classified.
- Qualitative data can be extensive in its nature, for example interview transcripts, and so coding can be helpful in reaching succinct conclusions about the data.
- These categories provide a framework to convert the qualitative material into quantitative data, which can then be used for further (statistical) analysis.
example of coding:
For example:
A researcher is interesting in investigating prejudice and discrimination in the media towards refugees.
In order to do this, they will follow the following procedures:
- The researcher will select a newspaper article relating to refugees.
- They will read through the text, highlighting important points of reference and annotating the margins with comments.
- Using the comments made in the margins, the researcher will categorise each excerpt according to what it contains, e.g. evidence of prejudice, discriminatory language and positive regards towards refugees.
- This process will be repeated for each newspaper article of interest identified by the researcher at the outset.
- Once all the steps above are completed for each newspaper article, the categories which emerged through the process of analysing the content are reviewed to decide if any need refining, merging or subdividing.
- With the well‐defined (operationalised) behavioural categories, the researcher returns to the original articles and tallies the occurrence of each ‘behaviour’ accordingly.
- The qualitative data has now undergone analysis to produce quantitative data which can undergo further analysis such as statistical testing, descriptive statistics and producing graphs or tables.
What is THEMATIC ANALYSIS?
- Thematic analysis is a technique that helps identify themes throughout qualitative data.
- A theme is an idea or a notion, and can be explicit (such as stating that you feel depressed) or implicit (for example, using the metaphor of a black cloud for feeling depressed).
- Thematic analysis will produce further qualitative data, but this will be much more refined.
Example THEMATIC ANALYSIS?
the example: with the researcher reviewing the articles for evidence of prejudice or discrimination against refugees
the following procedures would be followed:
- Carry out steps 1–3 as if conducting a content analysis (see above).
- Thereafter, the researcher must decide if any of the categories identified can be linked in any way, such as ‘stereotypical views’, ‘economic prejudice’ or perhaps ‘positive experiences for refugees’.
- Once the themes are successfully identified, they can then be used in shorthand to identify all aspects of the data that fit with each theme. For example, every time the researcher identifies an example within the data of a positive experience for the refugee, they might write ‘PER’ (positive experience for refugees) alongside it, so that they are able to quickly re‐identify this theme in subsequent analysis of the data.
- Once all the steps above are completed, the themes which emerged will be critically reviewed to decide their relevance.
- This process will be repeated for each newspaper article of interest identified by the researcher at the outset.
- Qualitative comparisons are drawn between major and minor themes of the analysis.
EVALUATION: content analysis + Thematic Analysis
▪ There is the possibility that content analysis can produce findings that are very subjective. For example, the researcher may interpret some things said in an interview in a completely different manner from how they were intended, due to their own preconceptions, judgements or biases. Cultural differences may contribute to inconsistent interpretation of behaviour coding since language may be translated and therefore interpreted differently by someone of a different nationality. As a result, the validity of findings from a content analysis can be questioned since it may not have been measuring what it intended to with accuracy.
▪ A strength of both content analysis and thematic analysis is high ecological validity. Much of the analysis that takes place within these research methods are basing their conclusions on observations of real‐life behaviour and written and visual communications. For example, analysis can take place on books people have read or programmes that people have watched on television. Since records of these qualitative sources remain, replication of the content/thematic analysis can be conducted. If results were found to be consistent on re‐analysis then they would be said to be reliable.
What is meant by OBJECTIVE?
OBJECTIVE MEANS:
that they must not let their personal opinions, judgements or biases interfere with the data.
- Laboratory experiments are the most objective method within the psychology discipline because of the high level of control that is exerted over the variables.
- On the other hand, a natural experiment, by its very nature, cannot exert control over the manipulation of independent variables and is often viewed as less objective.
- Similarly, the observational and content analysis methods can fall victim to objectivity issues since the behavioural categories assigned are at the personal discretion of the investigator.
What is meant by EMPIRICAL METHODS?
- Empirical methods refer to the idea that knowledge is gained from direct experiences in an objective, systematic and controlled manner to produce quantitative data.
- It suggests that we cannot create knowledge based on belief alone, and therefore any theory will need to be empirically tested and verified in order to be considered scientific.
- Adopting an empirical approach reduces the opportunity for researchers to make unfounded claims about phenomena based on subjective opinion.
WHAT IS MEANT BY REPLICABILITY?
Replicability:
refers to the ability to conduct research again and achieve consistent results.
- If the findings can truly be generalised, and thus be truly valid, psychologists would expect that any replication of a study using the same standardised procedures would produce similar findings and reach the same conclusions.
What is meant by FALSIFIABILITY?
Falsifiability (Popper, 1934) refers to the idea that a research hypothesis could be proved wrong.
- Scientific research can never be ‘proven’ to be true, only subjected to research attempts to prove them as false.
- For this reason, all investigations have a null hypothesis which suggests that any difference or relationship found is due to chance.
- An example within psychology, for its lack of falsifiability is the Freudian psychodynamic approach. A central principle of this approach is the notion of the Oedipus complex, which occurs for boys during childhood whereby they must resolve an unconscious sexual desire for the opposite‐sex parent in order to develop the final element of their psyche: the superego. If a male individual refutes the idea that he will have gone through this stage of psychosexual development in his youth, psychodynamic theorists would counter this with the supposition that they were in denial (a defence mechanism) which is another facet of the theory.
- Popper argued that if falsification cannot be achieved, the theory cannot have derived from a true scientific discipline, which should instead be regarded as a pseudoscience.
- Therefore, the psychodynamic approach casts doubt on the scientific rigour of psychology when considered as a whole.
describe THEORY CONSTRUCTION?
- A theory is a set of principles that intend to explain certain behaviours or events.
- However, to construct a theory, evidence to support this notion needs to be collected first, since the empirical method does not allow knowledge to be based solely on beliefs.
- If a researcher suspects something to be true, they need to devise an experiment that will allow them to examine their ideas.
- If they start to discover patterns or trends in their research then a theory can be constructed.
- This is called the inductive process and is sometimes referred to as the ‘bottom up’ approach.
- Thereafter, researchers can make predictions about what they expect to happen – a hypothesis.
How do you design a hypothesis?
- When designing a hypothesis, it must be objective and measurable so that at the end of the investigation a clear decision can be made as to whether results have supported or refuted the hypothesis.
- If findings support the hypothesis, then the theory will have been strengthened; if it is refuted, then it is likely that alterations will be made to the theory accordingly.
- Conversely, there is the deductive process of theory construction which works from the more general ideas to the more specific and is informally referred to as a ‘top‐down’ approach.
- Here, the psychologist may begin with a theory relating to a topic of interest.
- This will then be narrowed down into a more specific hypothesis which can be tested empirically.
- Any data gathered from testing the hypothesis in this way will then be used to adjust the predictions.
What is meant by RELIABITY:
- Reliability is a measure of consistency.
- For example, if you are using a tape measure, you expect to get the same results every time you measure a certain object. If the results are not consistent, then the measure is not reliable.
- In psychology, the expectations are the same; if researchers are using a questionnaire to measure levels of depression, they want to ensure that the measure is consistent between participants and over time.
What is TEST-RETEST RELIABILITY
- One straightforward way of testing whether a tool is reliable is using the test‐retest method.
- Quite simply, the same person or group of people are asked to undertake the research measure, e.g. a questionnaire, on different occasions.
- When using the test‐retest method, it is important to remember that the same group of participants are being studies twice, so researchers need to be aware of any potential demand characteristics.
- For example, if the same measure is given twice in one day, there is a strong chance that participants will be able to recall the responses they gave in the first test, and so psychologists could be testing their memory rather than the reliability of their measure.
- On the other hand, it is also important to make sure that there is not too much time between each test.
- For example, if psychologists are testing a measure of depression, and question the participants a year apart, it is possible that they may have recovered in that time, and so they give completely different responses for that reason, rather than that the questionnaire is not reliable.
After the measure has been completed on two separate occasions, the two scores are then correlated.
* If the correlation is shown to be significant, then the measure is deemed to have good reliability.
* A perfect correlation is 1, and so the closer the score is to this, the stronger the reliability of the measure, but a correlation of over +0.8 is also perfectly acceptable and seen as a good indication of reliability.
What is meant by INTER-OBSERVER RELIABILTY
- Inter‐observer reliability refers to the extent to which two or more observers are observing and recording behaviour in a consistent way.
- This is a particularly useful way of ensuring reliability in situations where there is a risk of subjectivity.
- For example, if a psychologist was making a diagnosis for a mental health condition, it would be a good idea for someone else to also make a diagnosis to check that they are both in agreement.
- In psychology studies where behavioural categories are being applied, inter‐observer reliability is also important to make sure that the categories are being used in the correct manner.
- Psychologists would observe the same situation or event separately, and then their observations (or scores) would be correlated to see whether they are suitably similar.
Improving reliability in questionaires:
For questionnaires, it will be possible to identify which questions that are having the biggest impact upon the reliability, and adjust them as necessary. If it is deemed that they are important items that must remain in the questionnaire, then rewriting them in a manner that reduces the potential for them to be incorrectly interpreted may be enough. For example, if the item in question is an open question, it may be possible to change it into a closed question, reducing possible responses and thereby limiting potential ambiguity.
Improving reliability in interviews
- ensure that the same interviewer conducts all the inverviews to reduce researcher bias and if the same interviewer can’t be used then training should be provided to prevent potential variables.
- some researchers may ask questions that are leding or open to interpretation so dont do that
- changing the structure form unstructured to structured will limit the researcher bias
Improving reliability in experiments
- Lab experiments have the highest reliability because it can highly control the independant variables which makes them easier to replicate by following the sandardised procedures
- to improvethe reliability within experiments, the researchers can control the extranaous variables to prevent them from becomming confounding
improving reliability in observations
- observations lack objectivity because it relies on the researchers interpretations
- if behavioural categories are being used, it is important that the researcher is applying them accurately and not being subjective in their observations
- you can improve reliability by operationalising the behavioural categories, this means that the categories needs to be clear and specific on what constitutues the behaviour you are looking for: there should be no overlap between the categories so that it is not left for interpretation
what is meant by validity
validity refers to whether something is true or legitimate
Validity -pg 69
what is meant by internal and ext
