Comm150 Week Three Notes
Comm150 Week Three Notes Communication Studies 150
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Communication Studies 150
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This 10 page Class Notes was uploaded by Alyssa Notetaker on Sunday October 18, 2015. The Class Notes belongs to Communication Studies 150 at University of California - Los Angeles taught by PJ Lamberson in Fall 2015. Since its upload, it has received 51 views. For similar materials see Methodologies in Communication Research in Communication Studies at University of California - Los Angeles.
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Date Created: 10/18/15
Week Three Lecture Five Discussion The elusive obvious When you re looking for an answer possibly expecting some exotic answer you don t see the obvious 0 So don t get pigeonholed into one way of thinking AKA boys eyes look for a red crayon it s right there but he can t see it Big data 0 Huge collection of information being used to predict things 0 Possible because computer chips are now so fast and able to hold lots of information 0 Computers can now store all of human history 0 With big data can we better predict when someone is about to get sick according to what they buy in the store 0 Why Money and power stores want to predict so they can sell more stuff 0 Step one unit of analysis A certain item in the store Frequent shoppers who buy OJ and potato chips together frequent shoppers buying habits o 2 Theory Our bodies will crave certain foods when the nutrients they provide are needed especially in the initial stages of an illness 0 3 Hypothesis Because our bodies crave the nutrients available in certain foods even before someone is sick their bodies are already craving the nutrients they need and so people will be more likely to buy orange juice for its citric acid and vitamins A and C and potato chips for its salt which we crave when our adrenal glands are not properly functioning such as in an illness 0 4 Measurement How to scale what you re measuring How to quantify it Look at what s been already published the holes between the connections OJ and potato chips 0 Compare someone s food purchases to previous purchases if they buy OJ and potato chips where they don t usually it means something 0 More exactly look at people who buy both OJ and potato chips in one trip 0 Look at their past 5 trips did they buy OJ and potato chips more than once If so it s probably a normal spending habit and so discountable 0 Look at future trips for 2 weeks do they buy cold medicine OR survey those who buy OJ and potato chips a week after buying 0 5 Operationalizing de ning your concept Terms to de ne quotsicknessquot etc Is it binary nominal ordinal interval or ratio Measurement Operationalizing cont d 0 Includes nding indicators for a behavior ex number of religious artifacts in a house as indicative of level of religiosity Sampling 0 EX Read restaurant reviews and make an inference about restaurant quality Ask friends how good a class is and infer its quality Go to a rst session of a class and make an inference about the whole course Meet someone from New York and make an inference about New Yorkers How the above work as samples 0 The rst 2 What you care about is what you think but the samples show others opinions Number 3 the rst class is a sample to infer class quality 0 Fundamental property of sampling you infer something bigger from a smaller group test 0 So asking some people s opinions on restaurants for example rather than looking at all the restaurant s chef training equipment etc 0 Key characteristics We want to know about a class of similar objects or events the population We observe some of these the sample We form inferences about the population based on the sample 0 Possible errors or bias nonrandom and harder to detect in sampling Selection bias 0 Undercoverage 0 Some members of the population are inadequately represented in the sample 0 Nonresponse bias 0 Even if it s random sampling people can refuse to answer potentially leading to undercoverage 0 Voluntary response bias 0 People with extreme opinions are more likely to respond Response bias 0 Leading questions 0 Social desirability 0 Population Target population the population to which you would like your results to generalize Sample frame the set of all cases from which your sample is drawn Ideally it matches the target population 0 Sample those who are actually chosen from the sample frame The characteristics of the target population are called parameters and are usually unknown 0 Sample estimates of population parameters are statistics Key question to nd out if your sample could have selection bias does your sample frame differ systematically from your target population 0 Ex with Literally Digest who predicted Landon would beat Roosevelt their sample frame was a list of contact info of people with phones and people with registered cars 0 This is biased because it differs systematically from the target population all voters o The rich or welloff are disproportionately represented 0 Exit polls no sample frame issues as everyone you poll did vote and so is representative of voters this is why it s more accurate than pre voting polls 0 Why sample We want to be able to draw general inferences about large classes of objects or events but 0 Can t survey a whole target population not feasible 0 Time and money are limited Sometimes you can get a more accurate result by sampling than by observing the whole population 0 Ex census tries to count EVERYBODY but some people mostly white and upper class were counted twice while even more were not counted at all mostly minorities children and lowerincome o How to sample 1 De ne your population after determining question theory hypothesis units of analysis and measurement target population 0 Who are you trying to draw inferences about 0 Let your question drive your sampling and data rather than viceversa 0 Think about the population before sampling 2 Find your sample frame 0 2 ways to construct a sample frame 0 1 List all cases in the population 0 2 Provide a rule de ning membership 0 Ex target population is city telephone owners sampling frame could be a city phone book listing or quotthe set of all numbers with certain telephone exchangesquot a rule 0 Find a way to sample the population that does not leave any demographics out disproportionately 0 Ask if your sample frame differs systematically from your target population to avoid selection bias 0 Must have adequate numbers 3 Choosing your sample 0 You want it to be representative of the population ie proportionally match the target population on key characteristics 0 Problem this is almost impossible to assess we can only measure representativeness with respect to speci c characteristics 0 Instead we judge the quality of a sample on how it was obtained We ask Does it work in a way to avoid biases Ways to obtain a sample that avoids bias see types of sampling below 0 Types of sampling Probabilitv samplind All cases in the population have a known probability of being sampled Includes simple random sampling strati ed random sampling systematic random sampling cluster random sampling and multi stage sampling Simple random sampling each possible combination of cases has an equal probability of being sampled Strati ed random sample Population is subdivided into exhaustive mutually exclusive categories the strata and you take a simple random sample from each strata Ex categorize men and women into separate subsets take a sample from each strata 0 Uses How many you take from each strata should re ect the target population ex US is 51 women so sample should be 51 out of the female stratum and 49 out of the male stratum proportionate strati cation Makes sure you get both men and women in the study that neither is disproportionately included or excluded Good for when your target population contains easily identi able demographics all of which you want to include Makes sure the sample is representative Most common strata age gender socioeconomic status religion nationality party membership and educational attainment Good for when you want the groups to be relatively homogeneous when you re looking at another variable O O You want each strata to be homogenous so your sample is heterogeneous includes people from each strata Increases precision for the sample size Aka it makes it more efficient don t need to look at so many people BUT you need to know more about the population like their age and gender which can make it more costly Allows the researcher to highlight a speci c subgroup within the population it ensures the presence of the key subgroup in the sample Cluster Sampling Breaks the population into clusters Select at random a sample of clusters Obtain a full list of the cases within the sampled clusters Sample from within the clusters EX 0 o o 0 List of all colleges each a cluster Randomly choose a few colleges Randomly choose 500 students at each of those few colleges to survey This example is multistage cluster sampling would be just cluster sampling if we started with the few colleges as clusters This is good for when things are basically the same across the clusters 0 So the colleges are all basically the same but within the clusters there are differences individuals are different Systematic sampling Sample every kth case the number k the sampling interval 0 Ex choose every 5th person on a list Issue with this bias 0 Ex sampling every 1st and 5th house on the block Can be all corner houses with bigger lots and bigger houses 0 Ex every 10th person on a military list But every 10th person was a sergeant o Nonprobabilitv samplind Does not control for investigator bias Statistical properties unknown cannot use theory of random sampling to statistically eliminate bias Types 0 Convenience sampling Purposive sampling Quotas O Strati ed random sampling where there are quotas for number of people in each strata Referral or snowball sampling 0 Sample error 0 O 0 Good for small populations Survey someone then they refer you to another Good for stuff like nding gang members to question no of cial list of the population How far away the number you estimate in the sample is from the truth As the sample size increases sample error decreases It gets more accurate with a larger sample 0 The distribution is less varied and more centered on the true value 0 So you want a larger sample size especially when the population is extremely varied heterogeneous If your sample distribution is really varied it s a cue that you need a bigger sample size Standard error 0 The average sample error of the sampling distribution The standard deviation of the population divided by the square root of the sample size I quotCon dence intervalquot or the range of values within the estimated population value is likely to lie c quotWe are 95 con dent the population mean is 3i 1quot Lecture Six Experimentation Basic characteristics 0 True experiments eliminate effect of exogenous variables and allow strong causal inferences eliminate confounders and reverse causality endogeneity o Tests causal relations A manipulated independent variable is followed bya measured dependent variable 0 Testing the effect of one variable on another There are two groups a control and a treatment group 0 2 groups treated exactly the same except for the one manipulation In this way we can test for causality and avoid making conclusions about something that actually just happened randomly o Inferring causality all the below have to happen be checked 0 1 Association correlation o 2 Direction of in uence ie not endogenous o 3 Elimination of rival explanations ie not spurious or confounded In an airtight experiment only one rival explanation chance Can show a low probability of that chance with a test of statistical signi cance 0 Matching v random assignment 0 Matching matching subjects on characteristics that logically seem to be related to the experimental outcome and putting one subject in the test and one in the control group 0 Random completely random or SRS strati ed etc 0 Measurement validity o The way you operationalize the experiment must capture what you want to study 0 Evaluating internal validity 0 Random assignment 0 Manipulation of independent variable 0 Measurement of the dependent variable 0 At least one control group 0 The constancy of conditions across groups except control External validity Is it generalizable Staging an experiment 0 Pretesting 0 Train the quotcastquot the interviewers testers Test the instructions cover story etc Check if the manipulation has the desired effect Revise and practice the quotscriptquot See if the subject will remain detached or if they ll experience experimental realism experience it like it s real life and so re ect the truth 0 Another level of realism mundane realism simiarity of experimental events to everyday expedences Demand characteristics cues telling subjects what is expected of them and what the experimenter hopes to nd Affects that happen because the subject knows they re being watched reactive measurement effects 0 Sometimes there s evaluation apprehension where the subject acts in a way to try to get a favorable evaluation of the experimenter 0 Sometimes want to help the experimenter or give intentionally useless or invalid responses Something else to keep an eye out for experimenter effects Subte effects due to how the experimenter believes the experiment will turn out 0 They may unintentionally communicate to subjects how they should respond to con rm a hypothesis Minimizing these experimental biases Ask the subject about their perception of the experimental environment whether it seemed like they should act a certain way etc Doubleblind technique 0 Subject recruitment 0 Acquisition of informed consent Introduction to experiment 0000 O Sometimes includes a cover story about what the study is for to avoid bias while allaying preoccupation with what it s about Random assignment Manipulation of independent variable Measuring of dependent variable Manipulation check See if any result actually happened if the manipulation caused any affect on the subject Deb e ng Discuss what happened with the subject Experimentation outside the laboratory 0 O 0 Field experiments a true experiment in a natural setting Experimental design in survey research Using units of analysis other than individuals
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