Research methods advantages and disadvantages – Flashcards

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Naturalistic Observation
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Systematically observig people in their natural environment, includes conceptualisation and operationalisation of variables
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Naturalistic Observation: Advantages
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Insight into real-world behaviour, examine behaviours that can't be manipulated
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Naturalistic Observation: Disadvantages
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Time consuming, subjective interpretation
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Participant Observation
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Researcher interacts with people being observed in order to observe and record their behaviour
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Participant Observation: Advantages
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Obesrve behaviours that aren't usually accessible, unique perspective of participation
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Participant Observation: Disadvantages
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Subjective Interpretation
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Contrived Observation
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Observing behaviour that has been manipulated by the researcher
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Contrived Observation: Advantages
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Don't have to wait for behaviour to occur on its own
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Contrived Observation: Disadvantages
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Less natural than other forms of observation
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Type 1 Error
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Reject a true null hypothesis, alpha-level
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Type 2 Error
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Don't reject null hypothesis when it is false, beta-level
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Effect size
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strength of association between variables
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Effect size examples
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Pearson's correlation, Cohen's D
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Cohen's D Formula
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difference between means/standard deviation
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Confidence Intervals
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Where the true population mean is likely to lie
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Confidence Intervals: Rules
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Sample size increases - CI narrows; Higher confidence required - CI widens
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Independent Samples T-tests
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Two samples obtained from two populations to be compared
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Independent Samples T-tests: Assumptions
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Populations follow normal distribution, Equal variance, Samples sizes equal
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Independent Samples T-tests: DF
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n - 2
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Descriptive Uncertainty
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Uncertainty of random error; small sd = less uncertainty
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Interential uncertainty
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More information/less error = smaller p-value
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Bonferroni Correction
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p-value to the power of n to provide new p-value
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Independent Samples T-tests: Disadvantages
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Subject to "noise factors"
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Observer Error
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Human error in measurement process
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Observor Error: examples
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Maturation, Experimentation, Faciliatated Communication
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Environmental Error
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Changes between measurements in the environment
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Environmental Error: examples
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History
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Participant Error
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Changes in participants between measurements
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Participant Error: Examples
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Demand characteristics, expectations/placebo effect, mortality, regression to mean, instrumentation, selection differences, order/practice/fatigue, repeated testing
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Controls for Error
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Type of experimentation, pilot testing, counterbalancing, deception, debriefing and research communication
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Quasi-Experimentation
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Designs that approximate a true experiment
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N=1 Designs
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Single Case design, for unusual cases or expensive manipulations, or when it isn't necessary to have a large group
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N=1 Design: Types
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Reversal (ABA), Multiple Baseline (across subjects, behaviours, or situations)
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N=1 Design: Constraints
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Permanent or enduring manipulations cannot use reversal design, nor when it would be unethical to reverse a beneficial manipulation
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Programme Evaluation
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Research on proposed and implented programs to improve something
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Programme Evaluation: Needs assessment
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Do people need to change?
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Programme Evaluation: Programme theory assessment
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Do you have valid assumptions about the cause of the problem, and therefore why the program will work?
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Programme Evaluation: Process evaluation
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a manipulation check; Is the programming doing what it advertised it would do?
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Programme Evaluation: Outcome evaluation
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Did the programme reduce the problem?
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Programme Evaluation: Efficiency assessment
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Is the programme worth the cost?
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One Group Posttest/One group pretest-posttest
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No control group, and many threats to validity (history, maturation, testing, instrumental decay, regression toward mean, differential attrition)
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Nonequivalent control group design
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No pretest, so is hard to verify change despite the fact that there is a control group as we do not know if the two groups are equivalent or not
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Nonequivalent control group pretest-posttest
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If the pretest scores are similar, therefore the groups are comparable
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Quasi-Experimental: Statistical test
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t-tests and ANOVA
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Cross-sectional
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Data collected at one point in time on participants of different ages
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Cross-sectional: Advantages
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Can make general comments about how individuals of different ages perform
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Cohort
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Data collected at regular intervals from individuals of a specific age
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Cohort: Advantages
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Tells you whether there are general changes in time over how a certain age group is thinking or behaving
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Longitudinal
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Data colected from a single cohort at regular intervals over time
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Longitudinal: Advantages
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Obtain information about causality, and can see individual changes over time
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Longitudinal: Disadvantages
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Expensive, time-consuming, and limiting to one cohort
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Sequential
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Hybrid between cross-sectional study (at time 1), and longitudinal study (follow the cohort through subsequent times)
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Sequential: Advantages
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Can obtain data from a wide age range quickly, as well as assess change, and not limited to a single cohort
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Developmental Research
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Assessing change, short and/or long term
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Developmental Research: statistical test
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MANOVA, Regression, Equation Modelling
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Questionnaire Problems
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Awareness/memory, response set/bias, question format, sample chosen, analysis done
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Yes/No Disadvantages
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Not very sensitive
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Fill-in-the-blank Disadvantages
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Hard to code the answers
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Likert Scale Disadvantages
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Difficult to have equal distances between points
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Aspects to avoid in questionnaires
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Complexity, Technicality, Ambiguity, Double-Barreled, Negative, Emotive or Sensitive issues, Leading questions, Privacy invasion
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