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mid 2 review- notesolution.docx

52 Pages

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PSYC 2360
Naseem Al- Aidroos

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1/21/2013 4:00:00 PM CHAPTER 6- SURVEYS & SAMPLING Common in media (see BBC story)  The story: ―Motorists turn to public transport as fuel price bites‖ – Daily Record  ―MORE than three in five drivers are turning to public transport due to high fuel prices, a survey has revealed. The survey by transport firm National Express found 61 per cent of car users are definitely or probably considering using public transport due to the rise in prices at the pumps‖ o what context was it asked in  were they asked immediately after paying for their gas o was it representative – how big was the sample etc. o ―61% probably considering‖ … what does this really tell us, doesn‘t say they actually are going to use public transport Surveys  Involve the use of self-report measured variables in descriptive research o (more often than not descriptive research, can be used for any type of research but most commonly used for descriptive research)  Can be used to collect either qualitative data or quantitative data Steps in developing surveys  1) Identify o Types of questions to use o Type of instrument to use (mail/ internet, phone, person-to-person)  2) Pilot test/ seek opinions from others  3) Work Out o What demographic info to collect o Administration procedures/ instructions Types of questions  A) Fixed-format questions o Yes-No o Forced alternatives  alternatives (forcing them to choose between 2 or more options – you are guaranteed to get a response from anyone, you may not feel comfortable choosing either of these but you are forced to choose one so you get a response but the tradeoff is that you are forcing people into a category that they may not belong to) – ―pigeon hold‖  Many things control me  Little in this world controls me o Multiple choice o Likert scales  (i.e. 1-2-3-4-5 OR very little – somewhat – a lot)  B) Free-format questions Survey methods Guide: Survey construction  1) Simple and direct o Question:  My overall feelings and thoughts about myself are predominantly favorable most of the time, leading me to feel pretty satisfied about who I am.   this could be more direct, ―on the whole, I am satisfied with myself‖, the researcher writes the question the other way because they want to make sure it covers everything and prime the reader but everyone can understand this question, you need to play a balancing game and be more specific  Have you ever suffered from auditory hallucinations?   This is a very direct question, depending on who you are asking auditory hallucinations may not be simple enough, not in everyone‘s vocabulary, ―have you ever heard voices or sounds that might not have been real?‖  2) Double-barreled questions o ―Do you believe that airbags are unsafe and expensive?‖   it is a yes or no question, but there is two questions and they may have different responses for each question, should be asking these as two separate questions  3) Avoid loaded or leading questions o Given the failure of welfare in the United States, do you feel welfare programs should be eliminated?  (scale 1-5, not at all – very much so)   You have explicitly stated that they are failing so it would be harder for them to say they shouldn‘t be eliminated you have created a context around them and this is going to cause a bias   the research may want a loaded question but you need to understand the effects of this. o Do you agree?  (1) ―A freeze in nuclear weapons should be opposed because it would do nothing to reduce the danger of thousands of nuclear weapons already in place and would leave the Soviet Union in a position of nuclear superiority.‖  (2) ―A freeze in nuclear weapons should be favored because it would begin a much-needed process to stop everyone in the world from building nuclear weapons now and reduce the possibility of nuclear war in the future.‖ o Results:  58% agreed with (1), 56% agreed with (2), and 27% agreed with both…   they are not agreeing with the actual question they are agreeing with the context around them  4) Avoid negative wording o Question:  I don‘t dislike eating ice cream every now and then.  ―I like to eat ice cream every now and then‖  Ask questions in the way people are used to  5) Manage context Sequencing questions:  To boost response rate.. o put innocuous questions first, personal questions last  To increase accuracy.. o keep similar questions together  generally it improves the accuracy of the responses, it helps people think more clearly because they don‘t have to remember all the things they were thinking about prior  To increase honesty.. o collect subjects ‗signature‘ first   if you ask people for their signature first, rather than last it makes people be more honest Fixed- vs. free-format  Schuman & Scott (1987) o Condition 1  Identify the most important problem facing America today.  pollution  the energy shortage  legalized abortion  quality of public schools  32% o Condition 2  Identify the most important problem facing America today.   only 1% mentioned education! Frequency-scale choices can influence which answers are chosen   the options you choose will affect people‘s responses (frequency scale)  Q: How much TV do you watch per day? o  Some people don‘t even look at the numbers they just think ―I watch TV less than most people, or I am relative for the population so I am going to choose the middle one, or the lowest one…‖ Effects of question order   The questions surrounding the question will also effect responses  ―How satisfied are you with your life overall ? ‖ o Question 1:  How satisfied are you with your relationship?   people are reminded of their great relationship and they are thinking life is good, or they are reminded of their negative relationships and are in a negative frame of mind and are disappointed with their life   frames their response to the next question… o Question 2:  How satisfied are you with your life overall? Importance of context  Schwartz & Clore: Contextual factors influence survey responses o Generally speaking when you ask people how satisfied with their life, if it is sunny outside people will report higher satisfaction with their life than when it isn‘t sunny o How do you deal with context, if you remind people of the context  ask people of the weather and it negates the effects, remind them that maybe they are just upset because of the weather – make people aware of their surroundings   Proper interpretation of surveys requires knowledge of the context o Not only obvious mistakes: leading questions, double-barreled, etc. o Also: items that surround a specific question, order of questions, anything else that alters what comes to mind   more subtle things that can have an impact on what people say, any context that will alter what comes to people‘s minds when they are filling out the survey, you need to be aware of the contextual influences are if you want to take the most away from it Sampling Populations vs. samples  Example:  Does advertising causing children to smoke?  o Limitation you can only generalize to children who have never smoked, and ultimately we want to know what made those children that do smoke start smoking so we are missing the big picture, if we are trying to make a conclusion about children that is our population we need to have a sample representative of all children not just one subgroup of children (those that have never smoked)  Do art exhibits affect tourism?  o It is not a useless piece of information, but it limits the information we cannot generalize to all tourists in Chicago or all tourists everywhere Representative samples  Representativeness of the sample o  How accurately can we generalize from a sample to the population o Sample size (reliability)  large enough to be able to overcome any kind of noise   the sample size you need in order to be able to generalize to a population is independent from the population size (sample size we need is largely unrelated to the population size) o Biased sample   picking people who do not have the attributes of the population o Whenever samples are used, the researcher will never be able to know exactly the true characteristics of the population.  (this is the major limitation)  ultimately you will never know the true characteristics, our goal is for our sample to be identical to the population but smaller – but this is not possible  Representative sample o Approximately the same as the population in every important respect o Requires…  1) The existence of one or more sampling frames listing the entire population of interest and  2) All selected individuals must actually be sampled o Sampling bias   There is the potential the sample is not representative of the population  Occurs when either of these conditions is not met Sampling procedures  OVERVIEW: o Probability sampling  Know about every person in population  Can specify the population  Each person in the population have a known chance o Nonprobability sampling  Population is not completely known  A) Probability sampling o  Know about every person in population  can specify the probability that any member of the population will be included in the sample  best way to make inferences about a population  Each person in the population has a known chance of being selected o Types:  1) Simple random sampling  Each person in the population has an equal chance of being selected  Sampling frame  A complete list of all of the people in the population  The importance of random selection!!!!   it is much smarter than you are, it can save you!!  2) Systematic random sampling (obtaining randomness)  If the list of names on the sampling frame is known to be th in a random sequence, every n name can be selected  Tables of random numbers  Role dice/flip coins  Computer generated random numbers  3) Stratified sampling  Involves drawing separate samples from a set of known subgroups called strata rather than sampling from the population as a whole  E.g., What is the ideal class size for university classes?  may differ by year, course content, etc.   The year people are in may affect their answers so we may want to randomly sample some students from first year, some students from second year etc. so we know we have representation from all the categories and it is most representative of the population of the University students.  Disproportionate stratified sampling  Frequently used when the strata differ in size and the researcher is interested in comparing the characteristics of the strata   If we know there are more 1 styears than 4 thyears and we want our sample strata‘s to be the same as the population so that it looks the most like the population  Oversampling  Drawing a sample that includes a larger proportion of some strata than they are actually represented in the population   If there is one strata that is under-represented and we want them to be equally represented in the results we might draw more from their strata  4) Cluster sampling   Can be used where a complete sampling frame does not exist  Breaks the population into a set of smaller groups (called clusters ) for which there are sampling frames, and randomly chooses some of the c we are looking a smaller group that we know a lot about that is probably reflective of the larger population, maybe for that smaller group we can come up with this complete sampling frame  We are interested in all undergraduate students in Canada o  how do we get a list of all these students in Canada (ethics clearance from all the institutions etc… very hard to do), each University is really a cluster that is similar to the overall population, so sample the cluster and generalize it to the larger population  To the extent that every cluster has similar attributes, the sample will be representative  If clusters are not all the same, however, than the sample will be biased  B) Nonprobability sampling o  Sampling procedure in which one cannot specify the probability that any member of the population will be included in the sample  (population is not completely known)  Accidental or convenience sample  Introduces biases – big problem when people select themselves to be part of the survey (return a magazine survey, for example) Snowball sampling  Used when the population of interest is rare or difficult to reach  One or more individuals from the population are contacted  These individuals lead the researcher to other population members Summarizing Survey Data  Histogram of scores  Summarizing the sample data o Raw data  The data collected must be transformed to be meaningfully interpreted using such techniques as:  Frequency distributions  Tables, histograms, grouped frequency distributions, stem and leaf plots, etc.  Descriptive statistics  Central tendency (mean, median, mode) and dispersion (range, variance/standard deviation)  Central tendency: Mean  Central tendency: Median  Central tendency: Mode  Normal distribution o  Data distributions that are shaped like a bell are known as normal distributions  mean, median, and mode all at the same point on the distribution. o  Skewed distributions o Outliers  extreme scores in a distribution o Skewed  distributions that are not symmetrical  positively or negatively skewed  Shapes of distributions o positively skewed the outliers are on the right side of the distribution. negatively skewed the outliers are on the left side of the distribution.  Measures of dispersion o Dispersion  Extent to which the scores are tightly clustered around or spread out away from the central tendency o Summarized using the  Range  Variance  Standard deviation  Dispersion: Range o Range is calculated based on only two data points  (i.e., the smallest and largest) o It would be better to use all data points (i.e., every participants score) to quantify dispersion  Why not calculate the average difference of each person from the mean?  Dispersion: Average distance from mean  Dispersion: Variance CHAPTER 7-NATURALISTIC RESEARCH Outline  Naturalistic research o  very useful research but has many limits.  1) Observational research  2) Archival research  Case studies Naturalistic research:  Designed to describe and measure the thoughts, feelings, and behaviours of people and animals _______________ . _________________________ Observational research: Jane Goodall  Jane Goodall‘s research with chimps o Observed & recorded behavior of chimps for many years o One of first to record tool use in non-humans Observational research  Advantages: o  o  Disadvantages: o o Observational research designs  1) Acknowledged vs. unacknowledged  2) Participant vs. observer Guidelines for systematic observation:   o Which people, times, places o Behavioural categories o Frequency of behaviour, timing, accuracy  o Event sampling o Individual sampling o Time sampling Archival research:   Analysis of existing data sources: o Statistical records  Daily temps, sports records, crime data o Survey archives o Written and mass communications  Newspapers, Internet bulletin boards Case studies:  Individual cases o Sometimes studied in naturalistic settings. o Sometimes studied outside of naturalistic settings  Often brought into clinical setting for in-depth assessment  Phineas Gage: o Brain damage left him with change in personality and deficits in reasoning  Key indication that specific parts of the brain are associated with specific functions  ffects of sleep deprivation o Peter Tripp    o Randy Gardner     Multiple memory systems o In 1980‘s researchers interested in how self concepts stored in the brain  Trait memory  I‘m open minded, I‘m a fast runner  Autobiographical memory  I tried eating oysters even though they look disgusting, I won a foot race last week o Following motorcycle accident, patient KC lost autobiographical memory, but trait memory was spared. anecdote vs case study:  what is the difference? What makes a case study scientific? o o o CHAPTER 8-HYPOTHESIS TESTING Outline  The problem: Why stats are necessary to test hypotheses  Quick review of hypothesis testing (video)  Slow review of hypothesis testing Samples and populations  Reserach findings are based on samples drawn from populations  Inferential statistics o  allows us to take the data from the sample, and make conclusions about the population. Two group means  Research question: Are there sex-related differences in alcohol consumption? o ask samples of males and females about number of drinks consumed during last week measures of central tendency  Mean, Median, Mode o How do the means of our samples compare?  ex. did men or women on average assume more alcohol? Measure of dispersion  Range (R = max – min) o how wide is the distribution?  Variance, standard deviation o how much variance from subject to subject? o based on this data:  ... can we conclude that on average men consume more that women at the population level?   NO! not enough information.  measure the entire population to know for sure.  why we need inferential statistics!  the variance in data from samples can obscure our results. Variance  There is variance in our sample, which implies that there is variance in our population o s ≈ population variance  sample variance is relatively equal to or population variance  (it is a good estimate)  What if we drew two new samples? o We would likely get two new means  Would the means still be different?  EXAMPLE: o Do males and females differ?  Results: average number of drinks consumed  males = 2.5  females = 1.3  Because means calculated on samples (not populations) difference between means may have happened by chance.  Final goal is to say, for example: Men consume different levels of alcohol than women, p < .05  -meaning, there is less than 5% chance that the difference we saw between these means was purely by random sampling (chance) VIDEO: hypothesis testing:  5 steps: o 1) hypothesis:  -Null hypotheiss (disprove)  -Alternative hypothesis (what your trying to prove)  -always about the population perimeters  -2 tailed test > can go in either direction (ex. = )  -1 tailed test > can go in one direction ( < ) o 2) signifigance:  -0.05 level of significgance  -Type 1 error: probability u will say null is wrong when it is correct. o 3) sample o 4) P-value  -calculate appropriate p-value  -if p-value is less than level of signifigance > reject null hypothesis  (using the p value to infer about the population means from sample means) o 5) decide Samples and populations  Research findings are based on samples drawn from populations  Inferential statistics o allow us to infer what the population is like, based on sample data  Do the differences we see in samples reflect differences in the population, or just random error? Example  Hypothesis: Frequent use of steroids is associated with lower than normal IQ o IQ tests are designed to have a mean of 100, and standard deviation of 15 in the general population. o Method Ask a sample of 20 steroid users to complete an IQ test. o Results Mean IQ was 90.  Would you expect this big a difference based on chance alone? Assessing randomness of sample statistics  How much will the sample mean vary from one sample to the next? o -IQ doesn't just vary person to person, it also varies sample to sample  Need to determine the sampling distribution o  distribution of a given statistic (e.g. mean) over repeated sampling from a population. Sampling distribution  Method 1: o collect samples from the population o calculate the mean for each sample o plot the means – distribution of sample means  Method 2: o -thanks to statistics, we know what the sampling distribution will look like based on the population mean and variance. o if we know about the population were studying (mean, SD)   use this tool to figure out what sampling distribution. Sampling distribution of the mean  example: o we know IQ has a mean of 100, SD of 15. o single sample:   has mean slightly off from the population mean (random error) o how much would we expect means to vary?   estimate sampling distribution!  tells us if the single sample is reflective of the population.  (1) the sampling distribution has the same mean as the original distribution. o (all the random errors cancel out)  (2) the standard deviation of the sample distribution is smaller than the standard deviation of the population. o (standard error of the mean > SD of the sampling distribution)  increase the number of people in the sample (sample size)..  the distribution will shrink inwards (get smaller), because it is less effected by the random variance (ex. outliers)  (3) as the sample size becomes larger, the shape of the distribution approaches a normal distribution. Central limit theorem 2  Given a population with mean μ and variance σ , the sampling distribution of the mean for sample sizes of N will: o Have sample mean equal to population mean  x = μ o have variance equal to variance / sample size. 2 2  σ = σ /N  standard error (std. deviation of the sampling distribution)  o approach the normal distribution as..   sample size (N) increases. Steroid/IQ example  Sampled 20 people. What types of sample means could we expect if we were sampling from the general population? o  Difference by chance? o o Very few instances when we would get a mean of 90 if sampling form the general population  (difference is unlikely due simply to chance)  Frequency: Counts vs. proportions o Take sample of n=50 people from general population & record IQ  What proportion of sample has an IQ less than 80?  Add up all bars left of 80, and divide by N.  We can convert the histogram to proportions of sample.  Now sum of all of the bars = 1.  (if it was counts, the sum would add up to the sample size) o gives us a normal curve   area under the curve = 1  Normal distribution o  Ex. within 1 standard deviation of the mean = 2/3rds of the data  Difference by chance? o  Area under the curve to the left f 90 is much less than .05, so the probability that our observed difference (90 vs. 100) happened by chance is p< .05  This is significant, less than 5% chance we would see this difference purely form sampling error.  Summary: IQ/steroid example o Is IQ of steroid users less than IQ of general population?  Collected sample and found μ Steroid 90, which is less than μ General = 100 o Do steroid users have lower IQ, or does difference simply reflect sampling error? o Assume there is no difference and generate the sampling distribution  Null hypothesis o Determine likelihood of getting observed difference (or larger), simply by chance  Likelihood < 5%, so reject null hypothesis.  sampling distributions o we knew the population variance (15 ), and so we..   computed MEAN of our sample   compared against sampling distribution of the MEAN o If you don‘t know population variance, and want to compare two means, need slight adjustment.   compute T-STATISTIC base on your samples.   compared against sampling distribution of T-STATISTIC  If you are comparing multiple groups simultaneously o compute F-STATISTIC base on your samples. o  compared against sampling distribution of F-STATISTIC. The null hypothesis   Assumes observed data do not differ from what would be expected on the basis of chance o null hypothesis = H 0 o Alternate hypothesis= H 1  To reject null hypothesis, o  observed data must deviate more than what would normally be expected under the sampling distribution. p and  (Alpha)   We assume that all observations come from same parent population  p = probability that observed difference could have occurred simply by chance. o (likely hood we'd see alpha by chance)   = the arbitrary threshold at which the investigator is willing to discount the role of chance as an explanation for an observed group difference o (ex. alpha=.05) Statistical decisions  When p ≤ alpha, o  chance could not be the explanation of the observed group difference.  This leaves:  1) systematic error  2) true difference in the population  (something we conclude)   The observed difference in IQ between steroid users and the general population is statistically significant (p < .05).  When p > alpha, o chance could not be ruled out as the explanation of the difference.   The observed difference was not statistically significant (p > .05). We don’t know whether there is an IQ difference or not. p-Values   tell us how likely we would see the data, if our hypothesis is INCORRECT. o (probability we would see the data by chance)  Two-sided p-values o Used to test research hypotheses o Two-sided p-values take into consideration that unusual outcomes may occur in more than one way (―prediction free test‖)  One-sided p-values o Can be used in some special cases  only one direction (ex. men drink more than women.) One-tailed tests  ex. do men drink more than women on average. o men drink 1 more on average..  -is this a likely conclusion due to purely chance? o 5% of the graph can exceed the difference you found.  less than 5% of a chance of seeing a larger difference in one direction. (can't account the other direction to chance) Two-tailed tests o split the "5%" chance into 2.5% on both sides.  this pushes the cut-off a bit.  (need to find a slightly larger difference) One-tailed vs. two-tailed tests   both tell us whether the effect is ignificant or not.  Use a one-tailed test.. o  when you have a specific reason to believe the effect will be in a particular direction, and you do not care if the effect is in the opposite direction.  One-tailed tests will always result in smaller p values, giving a greater chance of reaching significance for your directional hypothesis. (have more power)  Otherwise.. o  use a two-tailed test.  The decision of whether to perform one-tailed or two-tailed tests must be made prior to data collection. o or everyone would know what tail to target, and would do a one-tailed test (more powerful) Type I error   Occurs when we reject the null hypothesis when it is true o (saying there is a difference when there really isn't)  Likelihood is set to alpha (.05 usually)  (probability of making a type 1 error is the P-value)  5% is reasonably low probability of being wrong, could set lower. Type II error  Incorrectly accepting the null hypothesis when there really is a difference o (saying there isn't a difference, when there really is)  The probability of a type to error is given the label Beta ()  refers to Type II error (as alpha refers to Type I error) Decision Tree How sure are we of our d
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