Flashcards in Planning And Conduction Reseach Deck (59):
A statement that broadly points out what the research aims to accomplish and the desired outcomes of the research. It identifies the purpose of the investigation.
A question that asks about what a study intends to investigate.
Predicts that there will be no difference or no relationship between the variables being studied and the results are due to chance and are not significant in terms of supporting the idea being investigated.
Predicts that there will be a difference or a relationship between the variables being studied and the results are not due to change but are significant in terms of supporting the idea being investigated.
Predicts that nature of the effect of the independent variable will have an effect of the independent variable on the dependent variable or the direction of the relationship.
Predicts that the independent variable will have an effect on the dependent variable will have an effect on the depended variable or that there will be a relationship but the direction of the effect is not specified.
The group of people the researcher is interested in and from which the sample is drawn.
The selecting of the participant in the way that each member of the target population has an equal chance of being chosen.
Relies on initial participants recruiting others to generate additional participants. The sample is unlikely to be representative, though it is an easy way of gathering a sample if specific sample features are required.
Produced by selecting people who are most easily available at the time of the study.
Self-Selecting / Volunteer Sample
Produced by asking people to volunteer to take part in a study
Each participant takes part in every condition under test.
Strength of Repeated Measures
Few participants will be needed and individual differences will be controlled as the same participants are used in all conditions.
Weakness of Repeated Measures
Participants may suffer from boredom, order effects and practice effects. Boredom and order effects tend to result in poorer performance in the second or subsequent conditions and practice effects often lead to improved performance in the second or subsequent conditions.
Independent Measures Design
Different participants used for each level of the independent variable
Strength of Independent Measures
There is no chance of boredom, order effects or practice influencing performance in the second or subsequent conditions.
Weakness of Independent Measures
More participants will be needed, and as different people will take part in each condition, individual differences may influence the results.
Matched Participants / Pairs Design
Participants who are similar on key variables with one participant being placed in one experimental condition and the other in the other experimental conditions.
Strength of Matched Participant / Pairs Design
Allows participants to be matched on features that are important to the study. For example, Bandaranaike matched participants on aggression levels, which was an important aspect of his study into the transmission of aggression.
Weakness of Matched Participants / Pairs Design
It may be difficult to find an adequate number of participants who can be matched to the desired features.
The factor in an experiment that is manipulated, changed or compared by the researcher with the expectation that it will have an effect on the dependent variable. In a quasi experiment, this is not manipulated, as it is naturally occurring. For example, in Baron-Cohen’s study, he could not manipulate whether the participants were autistic, had Tourette Syndrome or were ‘normal’.
Dependent Variable (DV)
The factor in an experiment that is measured by the research to asses the effects of the IV. The DV is therefore any observed changes in behaviour which result from the manipulation (or natural occurrence) of the IV.
Control of Extraneous Variables
The control of any factors other than the IV that might potentially affect the DV and so influence the findings. Environmental factors such as noise, temperature, time of day, etc., if not controlled, can influence findings. Likewise, individual factors such as gender, age, occupation, etc., if not controlled, can influence findings.
Objective methods used in observation to break a continuous stream of activity into discrete recordable events. For example, if observing behaviour in a sixth form common room, behavioural categories could include talking/using a mobile phone/working on a computer/reading/playing cards etc.
Lists of behavioural categories, each with a code.
A data recording method that involved recording pre-determined behaviours at regular intervals, for example every five seconds, or taking a sample at different times of the day or month.
Data recording method that involves counting the number of behaviours in a specified time period, for example using a tally chart.
Allow participants to give full and detailed answers in their own words. They produce qualitative data, which is in depth and very detailed. However, because open questions usually give qualitative data, it is often difficult to identify trends and patterns or make comparisons between participants.
Offer a small number of explicitly stated alternative responses from which the participant must choose. There is non opportunity to expand on answers and they produce quantitative data.
Strength of Closed Questions
Results are easy to analyse, so trends and patterns can be identified and comparisons between individuals and/or groups can be made.
Numerical scales on which participants indicate the strength of some measure. They produce quantitative data.
Likely Rating Scales
Allow participants to indicate hoe much they agree or disagree by choosing an option.
Semantics Differential Rating Scale
Allow participants to choose between two extremes, rating their response towards an opposing pair of descriptive words. The partcip[ant chooses one of several numerical values.
Weakness of Rating Scales
They are very subjective and therefore do not always give valid or useful findings.
Strength of Rating Scales
The produce quantitative data, which make results easy to analyse
Data that has not been processed for use
Tally Chart / Frequency Table
A grid used in an observation which shows the possible categories of results; a tick or tally is made each time the item is scored. These can be added together to give a Total in each category.
Nominal Level Data
A level of measurement recording darts in totals of names catagories
Ordinal Level Data
A level of measurement recording data as points along a scale where gaps between the points are not necessarily equal.
Interval Level Data
A level of measurement recording data as points on a scale where the gaps between the points are equal.
Data gathered first hand from the sample
Data gathered from research conducted by another research
Measures of Central Tendancy
A mathematical way to describe a typical or average score from a data set
Measures of Dispersion
A mathematical way to describe the variance or spread of scores in a data set.
The average of the squared differences from the mean
A measure of how spread out the scores are in a data set. It is the square route of the variance.
Test of Difference - independent measures - nominal data
Test of difference - repeated measures / matched pairs - nominal data
Correlation - nominal data
Chi - Squared
Test of Difference - independent measures - ordinal data
Mann - Whitney
Test of difference - repeated measures / matched pairs - ordinal data
Correlation - ordinal data
Spearman’s Rank / Rho
Test of difference, independent measures, interval data
Test of difference, repeated measures / matched pairs, interval data
Correlation interval data
Spearman’s Rank / Rho
Type 1 Error
Rejecting the null hypothesis when it is true / accepting the alternative hypothesis when it is not true.