Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
67 Cards in this Set
- Front
- Back
- 3rd side (hint)
|
What are the 4 types of experiments & describe?
|
1. Laboratory - high level of control over IV - eliminates confounding variables - IV manipulated to observe affect on DV. 2. Field - natural environment of participant - experimenter has control of IV - participants may not know they are being studied. 3. Natural Experiment - IV not manipulated, occurs naturally - researcher has no control of allocation of participants. 4.Quasi - IV based on existing difference between people (no manipulation) - resembles lab or field experiment. - typically carefully planned unlike natural experiments. |
|
|
|
Difference between a field experiment and a natural experiment?
|
Field - Experimenter has control over the IV WHEREAS Natural - IV occurs naturally, not directly manipulated. |
IV
|
|
|
Evaluation of Lab Experiments
|
Strength 1. Control Over Variables - Easy to control potential confounding variables (unlike natural & field) - Leads to cause and effect easily being established 2. Replicability - Easily repeated by others to see if they obtain similar results = reliable (unlike field & natural) Limitations 3. Artificial - high levels of control = different from real life situations -difficult to generalise = lacks ecological validity (unlike field & natural) 4. Demand Characteristics -Participants try to work out aims and act accordingly - They may try to help or deliberately confound the results. |
1. Control 2. Replicability 3. Artificial 4. Demand Characteristics |
|
|
Evaluation of Field Experiments
|
Strengths 1. Improved Ecological Validity - natural setting = can be generalised to real life situations (unlike lab) 2. Reduction of Demand Characteristics - Participants unaware they are taking part in study = minimised DC Limitations 3. Less Control - difficult to control extraneous variables = decreased internal validity - difficult to replicate - difficult to establish cause and effect 4. Time Consuming -waiting process for condition to occur |
1. Ecological Validity 2. Reduced Demand Characteristics 3. Less Control 4. Time Consuming |
|
|
Evaluation of Natural Experiments
|
Strengths 1.Reduction of Demand Characteristics - Participants unaware they are taking part in study = minimised DC (unlike lab) 2. Lack of Direct Intervention - Researcher doesn't directly intervene = more opportunity to gain insight into real life behaviour Limitations 3. Loss of Control - IV not directly controlled by researcher =reduces likelihood of cause and effect being established b/c too many confounding variables. 4. Replication Impossible - situation occurs rarely -difficult to check external validity of the findings |
1. Reduction of DC 2. Lack of Direct Intervention 3. Loss of Control 4 Replication Impossible |
|
|
Evaluation of Quasi-Experiments
|
Strength 1. Replicability - often carefully planned - highly controlled 2. Useful to make comparisons between types of people - where it is impossible or impractical to manipulate the variables Limitations 3. Confounding Variables - can't randomly allocate participants - cant establish causality 4. Demand Characteristics - carried out in a lab = leads to DC |
1. Replicability 2. Useful to make comparisons between... 3. Confounding Variables 4. Demand Characteristics |
|
|
What are the 6 types of observational techniques & describe?
|
1. Naturalistic Observation - Behaviour observed in natural context. -No direct manipulation of variables. 2. Controlled Observation - Researcher attempts to control certain variables. - Observation conducted in a lab. 3. Covert - Participants unaware they are being observed 4. Overt - Participants know they are being observed. 5. Participant - Observer becomes part of the group they are studying. 6. Non-participant - Observer remains separate from the group they are observing. |
|
|
|
Evaluation of Naturalistic Observations
|
Advantages 1. High Ecological Validity - participants unaware = natural behaviour - less chance of demand characteristics 2. Study behaviour where can't manipulate variables - Data can be collected from participants who cannot be tested in other ways (3. Fewer Ethical Issue) Weaknesses 4. Observer Bias -Different observers may see different things = low-inter reliability. 5. Lack of Control - No control of variable = replication is impossible - Hard to establish external validity & more extraneous variables |
1. High Ecological Validity 2. Study behaviour where can't manipulation 4. Observer Bias 5. Lack of Control |
|
|
Evaluation of Controlled Observations
|
Advantages 1. Time Saving - Possible to manipulate situation = you don't have to wait for desired behaviour to be shown. 2. Preliminary Research - develop new hypothesis for future investigations. Weaknesses 3. Low Ecological Validity - participants behaviour may change because they know they are being watched. 4. Observer Bias - Different observers may see different things = low-inter reliability. |
1. Time Saving 2. Preliminary Research 3. Low Ecological Validity 4.Observer Bias |
|
|
Evaluation of Covert Observation
|
Strength 1. High Ecological Validity -Behaviour is more natural = pp's unaware they are being watched Limitations 2. Ethics may be questionable - invasion of privacy -lack of informed of consent |
1. High Ecological Validity 2. Ethics may be questionable |
|
|
Evaluation of Overt Observation
|
Advantages 1. More ethical than covert - can informed consent Limitations 2. Reduced Ecological Validity -pp's now they are being watched = may lead to participant reactivity. |
1. More ethical than covert 2. Reduced Ecological Validity |
|
|
Evaluation of Participant Observation
|
Strength 1. Special Insight - gain special insight into the behaviour =increasing validity of findings Limitations 2. Observer Bias |
1. Special Insight 2. Observer Bias |
|
|
Evaluation of Non-participant Observation
|
Strength 1. Objective - Observer more likely to be objective. Weakness 2. Data lacks richness |
1. objective2. data lacks objectiveness
|
|
|
What is a questionnaire?
|
- structured set of questions - asks a large sample for views & opinions - questions can be closed or open |
|
|
|
Evaluation of Questionares |
Strengths 1. More Truthful Responses - People more willing to express themselves (than face to face interviews) - they remain anonymous = more reliable data 2. Simplicity - can be carried out w/ minimum of training accessing a large group of pp's Weaknesses 3. Problems w/ the wording of the questions - may influence the respons - not a true reflection of respondent's views. 4. Biased Samples - response rate is very low - sample is not representative of the population |
1. More Truthful Responses 2. Simplicity 3. Problems w/ the wording of the questions 4. Biased Samples |
|
|
What is an interview and what types are there?
|
- A face to face situation where an interviewer asks a series of questions to the respondent (structured), or created in response to answers (unstructured), or mix of 2 types (semi structured) |
|
|
|
Evaluation of Structured Interviews
|
Strengths 1. Replicable - Standardised q's = replicable - Answers from different pp's can be easily compared (in comparison to unstructured interviews). 2. Misunderstood questions can be explained - improves validity Limitations 3. Social Desirability - interviewees may give biased answers they think will give a favourable impression. 4. Requires Skilled Personnel - researchers need to be trained in effective interviewing skills (not need in questionnaires). |
1. Replicable 2. Misunderstood questions can be explained 3. Social Desirability 4. Requires Skilled Personnel |
|
|
Evaluation of Unstructured Interviews
|
Strengths 1. Lots of 'rich' data - w/ use of open ended questions about personal issues 2. Flexibilty - enables complex issues to be explored in further depth by tailoring questions. Limitations 3. Requires Skilled Personnel - researchers need to be trained in effective interviewing skills (not need in questionnaires). 4. More difficult to analyse the data - lack of standardised q's -large amount of data gathered.
|
1. Lots of 'rich' data2. Flexibilty3. Social Desirability4. More difficult to analyse the data |
|
|
What is a correlational analysis
|
An investigation that measures the extent of the relationship between two co-variables
|
|
|
|
3 Types of Correlation & description
|
Positive Correlation - co-variables increase or decrease together Negative Correlation - 1 variable increases while 1 decreases Zero Correlation - no relationship |
|
|
|
What is a correlation coefficient?
|
A number that tells us how closely the co-variables are related. - The stronger the correlation the closer to the correlation coefficient to +1 or -1. |
|
|
|
Evaluation of Correlations
|
Strength 1. They allow researchers to investigate situations that can't be done experimentally (when practically impossible to manipulate the IV). 2. Preliminary Research - Can indicate the trends that lead to further research using experimental means to establish causal links. Limitations 3.Cannot establish cause and effect - Only tells us that variables are related not which co-variable is causing the other 4. Third Variable Problem - There may be other unknown variables that explain why co-variables are linked |
1.Allows researchers to investigate situations that can't be done experimentally. 2. Preliminary Research 3. Cannot establish cause and effect 4. Third Variable Problem |
|
|
Difference between correlations and experiments.
|
Correlation - simply measured, no deliberate change/ Experiments - deliberately changes IV to see effect on DV Correlations - relationships are measured & cause and effect not established/ Experiments - cause & effect can be established. |
|
|
|
What is the aim of a study?
|
A general statement of what the researcher intends to investigate
|
To investigate the effect of alcohol on reaction times
|
|
|
What is a hypothesis? |
A precise testable statement made @ beginning of investigation that researcher expects to happen.
|
Alcohol consumption will significantly affect reaction times.
|
|
|
Difference between the aim and the hypothesis.
|
Aim - General statement/ Hypothesis - Precise and testable
|
|
|
|
2 types of hypothesis
|
Alternative -Directional - states direction the results will go -Non-directional - states there will be a difference but doesn't state the expected direction Null -statement of no difference -results are simply due to chance |
|
|
|
What is the Independent Variable?
|
Variable that is directly manipulated by experimenter to observe effects on the DV.
|
|
|
|
What is the dependent variable?
|
The variable that is measured by the experimenter.
|
|
|
|
What are extraneous variables?
|
Anything other than the IV that can have an effect on the DV.
|
|
|
|
What are confounding variables?
|
Extraneous variables that do affect the DV.
|
|
|
|
3 Types of expeRIMental designs & outline:
|
Repeated Measures Design - same participants in both conditions Independent Groups Design -Individuals randomly allocated to different conditions Matched Pairs Design -pp's closely matched & randomly allocated to one condition |
|
|
|
Evaluation of Repeated Measures Design
|
Advantage - Individual differences are eliminated = difference between performance of conditions is due to the IV -Fewer pp's required Limitation - Order effects (boredom/ fatigue) - can controlled by counter-conditioning. - Demand Characteristics because pp's in more than one condition |
|
|
|
Evaluation of Independent Group Design
|
Advantage - No order effects - pp's only in one condition - Reduced Demand Characteristics - Same material can be used in both
Disdvantage - Individual differences -More pp's required. |
|
|
|
Evaluation of Matched Pairs Design |
Advantages - No order effects -No demand characteristics -Reduced individual differences - Same material can be used Disadvantage -Difficult to match everything about pp's -Very time consuming & requires more pp's |
|
|
|
5 Methods of sampling & Outline
|
Random - Every member has an equal chance of being selected -e.g names in a hat / computer programmes also generate random lists Systematic - selecting every nth pp - e.g every 5th person Stratified - Subgroups identified - pp's obtained from each strata in proportion to occurrence in population. - Selection by random allocation Opportunity - most common method -simple approach anyone available -e.g in the street Volunteer - advertisement used - pp's volunteer themselves - incentive usually needed
|
|
|
|
Evaluation of Random Sampling
|
Advantage -Unbiased b/c pp's have equal chance Disadvantage - may not be representative -e.g. too many males |
|
|
|
Evaluation of Systematic Sampling |
Advantages - Reduced investigator bias Disadvantage - Not representative = limiting generalisability |
|
|
|
Evaluation of Stratified Sampling
|
Advantage - Avoids investigator bias - Representative = can be generalised Disadvantage -Very time consuming to identify subgroups & randomly select pp's. -If all key features not identified = not representative |
|
|
|
Evaluation of Opportunity Sampling
|
Advantage -easiest method - takes less time Disadvantage -May be unrepresentative -Researcher may consciously or unconsciously show bias - limiting generalisability. |
|
|
|
Evaluation of Volunteer Sampling
|
Advantage - easy method - less initial work than random sampling Disadvantage -Sample bias = only certain types of pp's volunteer (atypical respondents) |
|
|
|
Aims of pilot studies
|
1. Check that procedures, materials & measuring scales work. 2. Allow researcher to make changes or modification. |
|
|
|
Demand Characteristics
|
Cues in the experiment that make pp's aware of aims of study. - Leads to participant reactivity which confounds the results: - PP's try to help -PP's try to sabotage results - Display social desirability bias Solved by a Single Blind Technique |
|
|
|
Investigator effects
|
-Investigator has an effect on pp's performance -researcher's physical characteristics, cues (verbal/non-verbal) and expectations can have an effect. Can be solved by a Double Blind Design - neither pp or research know aims or use standardised instructions |
|
|
|
What is an ethical issue
|
A conflict or dilemma faced by the researcher.
|
|
|
|
What is an ethical guideline
|
A means if resolving the conflict
|
|
|
|
What is the British Psychology Society (BPS) code of ethics?
|
A quasi-legal document that instructs psychologists in the UK about what behaviour is and isn't acceptable when dealing w/ pps. |
|
|
|
6 Ethical Issues
|
1. Informed Consent -must know true aims of the study. -mustn't be coerced. 2. Deception -mustn't be deliberately misled or researchers deliberately withhold info. 3. Protection from harm - PPs should be in the same state after taking part as before. - Researchers must avoid any risk greater than everyday life. 4. Confidentiality - Personal info protected - A legal right under the Data Protection act. 5. Privacy - People don't expect to be observed by others in certain situations. 6. Right to withdraw - PPs must be free to withdraw @ any time & data destroyed |
Ian Don't Cry Roy Please Party |
|
|
How to deal with lack of informed consent
|
Prior General Consent -asking people who volunteer in research general q's before research. -Those who agree may be chosen Presumptive Consent - asking a group similar to the pps whether they would participate in the study. |
|
|
|
How to deal with deception & lack of protection from harm
|
Debriefing - After study, reveal true aims , offer opportunity to withhold data & offer appropriate support. |
|
|
|
How to deal with confidentiality
|
Anonymity - Research shouldn't use pp's names, numbers or false names instead. |
|
|
|
How to deal with privacy
|
Allow pp to withdraw their data after study
|
|
|
|
How to deal with right to withdraw |
Remind pp @ beginning, during & end of study they can withdraw.
|
|
|
|
4 General Ways to Deal With Ethical Issues
|
1. Ethical Guidelines - help establish whether research is acceptable. 2. Ethics Committee - must approve any study before it begins. 3. Cost Benefit Analysis - difficult to predict both costs & benefits prior to study. 4. Punishments- may be barred from practising psychology. |
|
|
|
What is the role of peer review?
|
- Quality control system to make sure no incorrect or faulty data enters the public arena. - Checks validity, originality & significance of research. - Helps to ensure that any research paper published in a well-respected journal is high quality. - Weeds out poorer research and allows only the best to become public. |
|
|
|
Process of peer review:
|
- Number of reviewers per research - They read the work carefully and assess all aspects of it - Then send it back to editor w/ comments & recommendations. - Research will be published, rejected or revised in some way. |
|
|
|
Evaluation of Peer Review
|
Strength - Establishing validity & accuracy or research - Suggest Improvements Weaknesses - Bias - Reviewer's theoretical view may differ from research - Failure to detect fraudulent research - fabrication (made up), falsification (altered) & plagiarism (copied) - File Drawer Phenomenon - favours positive results. |
|
|
|
Implication of Psychological Research for the Economy
|
The development of treatments for mental illness - Absence from work costs £15 bill a year -1/3 of all absences due to mild to moderate mental health. - Led to effective treatment for psychological disorders: SSRI's treats depression and OCD and return back to work. Improving Memory - Cognitive interview technique has improved amount of correct info collected from eyewitnesses. - Reduce expenses on wrongful arrests & to ensure criminals are caught. |
|
|
|
Qualitative VS Quantitative Data
|
Qualitative - Non-numerical -Subjective -Imprecise - Rich and Detailed - Low in reliability - Used for attitudes, opinions & beliefs - Real life settings WHEREAS Quantitative -Numerical -Objective - Precise -High in reliability - Used for behaviour - Artificial setting |
|
|
|
Primary Data VS Secondary Data
|
Primary Data -Authentic -Time & Effort - Expesive WHEREAS Secondary Data - Variation in quality -Minimal Effort - Cheap |
|
|
|
Measure of Central Tendency
|
Mean - average - add all, divide by number of categories. Median - Mid-point of all values Mode - Most common value (2 modes = bi-modal) |
|
|
|
Advantages & Disadvantages of central tendency
|
Mean Ad: use of all data = most powerful Disad: misrepresentative if there are extreme values Median Ad: Not affected by extremes Disad: Not as sensitive & may unrepresentative as doesn't include all values. Mode Ad: Not affected by extremes Disad: Doesn't use all the scores & not useful when there are several modes in data set. |
|
|
|
Measure of Dispersion
|
Range - take away lowest value from highest value. Standard Deviation - measure of spread of scores around the mean - larger the SD, larger spread of score |
|
|
|
Advantages & Disadvantages of measures of dispersion
|
Range Ad: Quick & easy to work out -full account of extreme values Disad: Can be distorted by extreme values Standard Deviation Ad: more sensitive measure than range - Allows for interpretation of individual scores. Disad: More complicated to calculate. - Less meaningful if data are not normally distributed. |
|
|
|
Normal Distribution...
|
- Bell curve - Mean, median & mode @ midpoint of graph - Graph is symmetrical - Expressed in SDs |
|
|
|
Skewed Distribution...
|
Positive Skew - most scores fall below the mean -data concentrated towards left - long tail to the right Negative Skew - most scores fall above mean - concentrated on the right -long tail to the left |
|
|
|
The sign test...
|
- To determine whether the difference we have found is significant. - Investigation must be looking for difference. - Must be a repeated measures design - Must be nominal data. - The S value is the lowest value of either +s or -s
|
|
