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Midterm

Things to Remember for Methods Midterm 2.docx

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Department
Psychology
Course
PSYC 2360
Professor
Naseem Al- Aidroos
Semester
Winter

Description
Things to Remember for Methods Midterm 2 Surveys provide snapshot view of attitudes, behaviours and beliefs. Interviews: structured (follow a decision tree) or unstructured (focus group, need to be careful of bias) Questionnaires: fixed format, without supervision, cheaper Can increase the number of people that respond to your questionnaire (response rate) by making it seem brief and interesting, stressing importance of participation, offering gifts or ensuring confidentiality Representative Sampling: when sample is the same as the population in all- important aspects Probability Sampling: when everyone in the population has a known chance of being chosen to be a part of the sample  Simple Random Sampling: when sampling frame is obtained, number from 1 to however many people there are and then use random number generator to see who is included in sample  Systematic Random Sampling: when sampling frame is already in th random order, we choose a number and every n person is included in the sample  Stratified Random Sampling: when the population is split into subgroups (stratas) and sampling occurs from the stratas o Proportionate Stratified Random Sampling o Disproportionate Stratified Random Sampling (Oversampling): used when subgroup is small, over sample the number of people by sampling more and then weigh their scores less in the measure therefore it is still representative  Cluster Sampling: the population is split into clusters and the clusters themselves are then chosen for the sample  The larger the sample size, the more likely it will be representative 2 Conditions: (a) there must be at least 1 sampling frame of everyone in population of interest and (b) everyone chosen to be a part of the sample must complete the experiment. If either is not met, the sample is susceptible to sampling bias when it is not representative of the population Non Probability Sampling: often used when sampling frames cannot be obtained  Snowball Sampling: sample one individual and then as for a referral to another individual  Convenience Sampling: sample whoever is available - what we do at the University of Guelph Psychology Dept. Picturing the Data: frequency distribution, histogram, stem and leaf plot Central Tendency: mean (5% trimmed mean), median, mode (can be used for nominal data) Dispersion (spread of data): range, interquartile range (25thto 75th percentiles), and standard deviation (larger it is, the bigger the spread) Unacknowledged Participant: undercover, ethical issues, emotions can distort data Acknowledged Participant: need to be careful of reactivity Unacknowledged Observer (least reactive) and Acknowledged Observer: observe within reasonable privacy Case Studies: can provide ideas for future research, learn about cases that are unethical to examine experimentally, very limited because person is normally unique or unusual in their behaviour therefore hard to generalize Coding methods should be decided beforehand otherwise you are more likely to see things that confirm your hypothesis rather than disconfirm.  Event Frequency: how many  Event Duration: how long  Event Sampling: focus on one behaviour for entire group  Individual Sampling: focus on one individual and all of their behaviour  Time Sampling: focus on one individual for a certain amount of time and then switch The null hypothesis is that there is no correlation, no difference; it is the least interesting outcome. The alternative hypothesis is the researcher’s hypothesis and is what we turn to if we reject the null hypothesis if the null hypothesis is likely to occur less than 5% of the time. Type 1 Error (alpha): probability of rejecting the null when the null is actually true, also known as significance level and is usually set to .05 Type 2 Error (beta): probability of accepting the null when the null is actually false Power (1-beta): probability of rejecting the null when the null is actually false  We can decrease type 1 and type 2 errors by increasing power - the larger sample size you have the more likely you will find significant relationships, stronger manipulations cause this as well (two groups are more different from each other therefore more likely to find significant differences)  Power is low in social sciences (~.6) Sampling distributions is when we take an infinite number of samples of sample size ‘n’ and we calculate a mean for each, we then plot the frequency of the means to get a sampling distribution of the mean. Central Limit Theorem: (a) the sampling distribution of the mean is equal to the population mean (b) as n increases, the variability of the sampling distribution of the mean decreases and (c) the sampling distribution of the mean is normal regardless of the underlying population as long as n > 30. Effect size tells you the magnitude of the relationship (while p
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