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psyb01 final exam sheet.docx

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Connie Boudens

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Surveys and Questionnaires – usually large scale, want to get information on whole population, sampling strategies are important -Questionnaire – instrument used to conduct a survey concerned with individual responses Steps in Questionnaire Development 1. List variables: background, dependent, independent 2. Operationalize variables 3. Decide how data will be analyzed “types of questions” 4. Develop wording for questions 5. Write proposed questions on index cards to facilitate editing and rearranging order 6. Pre-test the questionnaire- do they understand the questionnaire? Instructions? How long does it take? 7. Shorten list, refine questions Presence Absence Questions – respondents check off which items in a list do or do not apply to them – less commonly used (Yes/No) Single Choice Question – ask respondents which one category applies (What year are you in university?) Likert Type Questions – respondents indicate how much they agree or disagree with a statement (Strongly disagree …. Strongly agree) Graphic Rating and Non Verbal Cues – for people with low literacy skills ( …. ) Rank Ordering Questions – should be avoided or minimized because it takes time to answer Semantic Differential Scales – used for measuring the meaning of concepts (unreliable … reliable) Open Ended Questions – pros: permits details, clarification, unanticipated answers, reveals logic behind respondents’ response – cons: bias towards educated, irrelevant answers possible, coding and statistical analysis difficult, generalization or comparison difficult Caution: time consuming to answer and code, generate responses that are inconsistent, likely to be left blank, use if little is known Pitfalls to avoid – double barreled questions, hidden assumptions, question and answer don’t match, leniency bias, leading questions, alternative meanings, questions with jargon, imprecise questions, vague/ambiguous questions, social desirability, threatening questions Kinsey Technique – emphasize on continuing of the graduations between always and never Populations and Samples – inferential statistics based on inferring from a sample to a population Gerenalizability – ability to infer population characteristics based on the sample Sampling Error – difference between sample and population characteristics, reducing sampling error is the goal of any sampling technique, as sample size increases, sampling error decreases Process of Sampling Selection – Naming of population (can be people, organizations, events), restrict by age, geography, etc. Determining Population Size – depends on total population and desired confidence interval Employing Appropriate Sampling Strategies – Probability Sampling – likelihood of any member of the population being selected is known and equal Non Probability Sampling 0 likelihood of any member of the population being selected is unknown Simply Random Sampling – each member of population has an equal and independent chance of being chosen, sample should be very representative of the population, ideal in theory but difficult in practice Stratified Sampling – goal is to select a sample that is representative of population – characteristics of interest are identified (eg. Gender), individuals in population are listed separately according to their classification (eg. Females, males), proportional representation of each chart is determined (40% female, 60% male) a random sample is selected that reflects the proportions in population (4 females, 6 males) Cluster Sampling – identifying clusters of the population and randomly selection from them, the whole cluster is then used, units must be homogenous in order to avoid bias Non – probability Sampling – non random sampling refers to strategic requests for ‘volunteers’ the use of informants that snowball or hand picking respondents – selecting a sample on basis of convenience can threaten a study’s credibility Convenience and Snowball Sampling – captive or easily sampled population – not random, weak representation Quota Sampling – proportional stratified sampling is desired, but not possible Quasi Experiment – don’t meet requirements necessary for controlling the influence of extraneous variables, usually random assignment is a missing piece Features of Quasi Experiment: matching instead of randomization, time series analysis, unit of analysis often something different than people Threats to Internal Validity: history – some event affects the study out come, maturation – natural change in subjects overtime, instrument change – not using the same measurement tools or methods with each subject Testing/Repeated testing – fact of having been tested, practice effects – prior exposure to a measurement, fatigue – prior participation tires the participants Mortality – participants dropping out Regression to the mean – high/low measurements tend to be followed by measurements that are closer to the group mean Time Series Analyses – useful when you cannot randomize participants and where it is possible to obtain a series of assessments of the dependent variable at pre treatment and post treatment Single Case Research Designs – use only one case or one group to investigate a specific phenomenon – not the same as case study, uses time series design Advantage: avoid problems with group mean, can examine participants from hard to find population, can deal explicitly with individual (not group) behavior, results are easy to interpret (no stats), can focus on helping few participants Disadvantage: hard to demonstrate causality, no controls in most cases, lack of statistics, can’t really look at interaction effects, counterbalancing is problematic, problem of external validity Take multiple pre test and post test measures to overcome limitations of a one group (case), still a quasi experimental design, cannot exclude natural history as an explanation for an effect Phantom pain – attention diversion used to assess pain control (A-B design) Cocaine Abstinence (A-B-A design) – escalating reinforcement for cocaine free urine samples/ Baseline reinforcement, then withdrew reinforcement Problems with A-B-A Design – end up with baseline condition, need to add another treatment phase A-B design: not all treatment related behaviors may be reversible Multiple Baseline Design – useful in testing for a treatment effect when you believe that effect is irreversible Baseline Data are collected on – 2 or more behaviors for same individual, same behaviors for 2 or more individuals, same behaviors across 2 or more situations for same individuals Data Analysis – all pieces of the research plan must connect research questions, measures, data collection and analysis Scales to consider – nominal: in name only categories are only labels: gender, ethnicity, marital status Ordinal: response options in an order that makes sense, but difference between options are meaningless (rank of priority, 1=best, 5=worst) Interval: difference between a score of 50 and 51. But, someone with a score of 40 is not half as anxious as a person with a score of 20. Ratio: sam
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