P2 Research Methods Flashcards
Aim
The aim of the study is what the study is trying to find out
Hypothesis
The hypothesis should show the independent and dependent variable
Directional Hypothesis
Can only have one result
Non Directional Hypothesis
Can be a number of different results
Independent Variable
Some event that is directly manipulated by an experimenter in order to test its effect on other variables - the dependent variable
Dependent Variable
The data that’s been collected from the experiment
Operationalise
Put into operation or use / Turning concepts into measurable observations
Extraneous Variable
Any variable that you’re not investigating that can potentially affect the outcome of your research study.
Standardised Procedure
The process in which procedures used in research are kept the same
Confounding Variable
Those that affect other variables in a way that produces distorted associations between two variables
Control
The perception that one has the ability, resources or opportunities to get positive outcomes or avoid negative effects through one’s own actions
External Validity
This refers to the extent to which the findings can be generalised to other populations / how well the test performance predicts behaviour in the real world.
Internal Validity
This refers to the extent to which the researcher can be sure that the change in DV was due to the manipulation in the change of the IV.
Mundane Realism
This refers to how experiments mirror the real world
Laboratory Experiments
An experiment conducted in a controlled environment, usually high validity because of the high levels of control. However it often has low ecological validity.
Independent Groups
They are totally separate groups being tested on, each group experience one level of the IV (e.g either caffeine or no caffeine)
Strengths of Independent Groups:
-Increased external validity as more participants are needed
-Reduces demand characteristics (if participants don’t complete the experiment twice, they might not guess the purpose of the experiment, therefore change their behaviour)
-Less time consuming
Limitations of Independent Groups:
-Cannot control participant variables (the different abilities / characteristics
-Needs more participants
How to deal with limitations for Independent Groups:
-Randomly allocate participants (names in a hat)
Matched Pairs
Participants are selected from two separate groups depending on what the experiment is such as athletic ability etc. The pairs would then complete independently on either condition
Strengths of Matched Pairs:
-Avoids order effects (there would be no practice effect as participants only partakes in one condition
-Reduces the influence of confounding variables
Limitations of Matched Pairs:
-Very time consuming
-Not possible to control all participant variables (only match on variables known to be relevant)
How to deal with limitations for Matched Pairs:
-Restrict the number of variables to match on to make it easier
Repeated Measures
Both groups experience both of the independent variables (caffeine and no caffeine)
Strengths of Repeated Measure:
-This helps to keep the validity of the results higher, while still allowing smaller groups.
-The experiment will be more efficient
Limitations of Repeated Measure:
-Order effects (order of conditions may affect performance - boredom effect/ practice affect)
-Demand characteristics (may guess the aim of experiment based on second test)
How to deal with limitations of Repeated Measures:
-Researchers may use different tests to avoid practice effect (words on a memory test)
-Use counterbalancing (both groups complete the test in different orders)
Pilot Study
A small scale trial run of a study to test any aspects of the design, with a view to making improvements
Experimental Design
A set of procedures used to control the influence of factors such as participant variables in an experiment ( independent groups / matched pairs / repeated measures )
Counterbalancing
An experimental technique used to overcome order effects when using a repeated measures design. Counterbalancing ensures that each condition is tested first or second.
Order Effect
In a repeated measure design, an extraneous variable can arise from the order in which the conditions are presented:
Practice Effect - an improvement in performance on a task due to repetition (familiarity)
Fatigue Effect - a decrease in performance due to repetition (boredom or tiredness)
Random Allocation
Allocating participants to experimental groups or conditions using random techniques ( flipping a coin )
Demand Characteristics
A cue that makes participants unconsciously aware of the aims of the study or helps participants work out what the researcher expects to find.
Investigator Effects
Anything that an investigator does that has an effect on a participants performance in a study other than what was intended
Single Blind Design
When the participants in the clinical trial do not know if they are receiving a placebo or the real treatment
Double Blind Design
When neither the participant or the experimenter know which group the participants belong to.
Experimental Realism
The extent to which situations created in experiments are real and impactful to participants.
Social Desirability Bias
This occurs when participants give answers to questions they believe will make them look good to others, concealing their true opinions.
Closed Questions
Questions that have a predetermined range of answers from which respondents select one
Open Questions
Questions that invite respondents to provide their own answers rather than select one of those provided
Filler Questions
Questions to put in questionnaires, interviews or test to disguise the aim of the study by hiding important questions in amongst irrelevant questions.
Qualitative Data
Information in words that cannot be counted. More subjective opinions
Quantitative Data
Information that represents how much or how long, or how many etc there are of something. Data that can be collected
Primary Data
Information observed or collected directly from first hand experiences
Secondary Data
Information used in a research study that was collected for a purpose other than the current one. Data that was already collected
Levels of measurement
Tells you how precisely variables are recorded
Nominal Data
A type of qualitative data which groups variables into categories
Ordinal Data
Data which is placed into some kind of order or scale
Mean
This measure involves adding up all the data items and dividing by the number of data items. It is the most sensitive measure of central tendency but can be distorted by one or two extreme values
Median
This measure is the middle value in an ordered list
Mode
This is the most common data item.
Range
This measurement is the arithmetic distance between the top and bottom values in a set of data
Standard Deviation
This is a more precise measure of dispersion than the range. It is a measure of the average distance between each data item above and below the mean, ignoring any plus or minus values
Strengths of Mean:
It uses all the data scores
Limitations of Mean:
It can be unrepresentative
Strength of Median:
- It can be used in ordinal data
- Unaffected by anomalies
Limitations of Median:
- Doesn’t work well on small sets of data
- Not as powerful as mean
Strengths of Mode:
- Useful when data is in categories
- Shows clearly most common score
Limitations of Mode:
- Small changes can make big differences
Strengths of Range:
- Quick to calculate
- Gives us a basic measure of how much the data varies
Limitations of Range:
- Tells us nothing about data in the middle
- Affected by extreme values
- Doesn’t take into account the number of scores
Strengths of Standard Deviation
- Allows us to make statements about probability
- More precise measure of dispersion as all the data is used not just highest and lowest like the range
Limitations of Standard Deviation:
- Affected by extreme values
- Not quick and easy to calculate
Interval Data
A data type which is measured along a scale, in which each point is placed at equal distance from another
Ratio Data
Numerical values where the difference between points is standardised and the data is quantitative.
Bar Chart
Bar charts display categorical variables
Histogram
Histograms visualise quantitative or numerical data
Normal Distribution
An arrangement of data that is symmetrical and forms a bell shaped pattern where mean, median and mode falls in the centre at the highest peak
Negative Skewed Distribution
When distribution has a few extreme scores toward the low end.
The left tail is longer
Positive Skewed Distribution
When a distribution has a few extreme scores towards the high end.
The right tail is longer
Skewed Distribution
Where frequency data is not spread evenly (normally distributed).
Intervening variable
A variable that handles the change in the dependent variable due to the change in the independent variable
Scatter-gram
Used for measuring the relationship between two variables
Positive Correlation
A relationship between two variables that move in the same direction
Negative Correlation
When two variables are related and as one variables increases the other decreases
Meta Analysis
A researcher looks at the findings from a number of different studies and produces a statistic to represent the overall effect
Case Study
A research investigation that involves a detailed study of a single individual / event
Content Analysis
A kind of observational study in which behaviour is observed indirectly in written or verbal material such as interviews, books, diaries, conversations
Peer Review
The practice of using independent experts to assess the quality and validity of scientific research.
