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SOCY 210 final Exam Review.docx

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Queen's University
SOCY 210
Vincent F Sacco

Exam Review The Logic of sampling  The reason we collect samples is because we want to use the sample to estimate something about the population we are sampling from Two major types of sampling 1. Nonprobability sampling  For what ever reason, when the units doing have an equal chance of being selected Haphazard sampling (nonprobability studies)  A.k.a reliance on available subject  Use of those avoidable at a particular time and place  Convenient and inexpensive  Useful for pretesting questionnaires or other social measurement  Not representative of a larger population  Difficult to generalize  Example: recruiting subject on a street corner Purposive/judgmental sampling  Sample selected on the basis of your own knowledge of the population you intent to study and your research question  Useful when studying a small subset of a larger population, where member of the subpopulation are easily identified  You are focused on a particular subgroup of a larger population  Used in the case of deviant case study, in the sense of statistical deviance Snowball sampling  Already recruited responded provide the researcher with assistance in locating other members of the population under study  Useful when members of a population are particularly hard to locate Examples: sex workers, undocumented immigrants  Often used for exploratory purposes  Since difference members of the sample may know each other and share similar characteristics, representativeness is problematic Quota Sampling  Like probability sampling, it helps address the issue of representativeness of a sample  Begins with a table describing the characteristics of the target population  With this you try to assign equal proportions of people who belong to different groups to your sample E.g. is you know that 10% of all sociology students are female and international, the you select 10 female international student for a sample of 100 sociology students  For this to work, the quota frame (matrix) used much be accurate and up-to-date  We are usually not interested in the generalization of sampling 2. Probability sampling The selection of sampling units in a manner that respects the fact that each unit in a population has an equal change of being selected  This is the favorable form of sampling  The reason probability sampling is better, is because when doing an experiment, we have to believe that all the people being selected is completely random, and that it demonstrate a real and appropriate microcosm of the population The theory and logic of probability sampling  Conscious and unconscious sampling bias  The population of 100 has 44 white women, 44 white men, 6 aboriginal women and 6 aboriginal men  Lets say that I enter this population and the first 10 people I meet are the white women. I select them for my study. Because I did not select a representation of the population (say, 2 white men, 2 white women, and 3 aboriginal men) I have a biased sample. Basic probability sampling jargon  Representativeness: a sample is said to be representative of a population if its aggregate characteristics closely approximate those aggregate characteristics in the population  Basic principles: ALL member of a given population should stand an equal change of being selected for particular sample  Population: theoretically specified aggregation of the element in a study  E.g. Canadians, sociology student  Sample: Potion of the population from which information is collected  Sampling Unit: Elements considered for selection during some state of sampling  The emblements of a population that are available and have an equal change of being selected to form the sample  i.e. those in the sampling frame  Sampling frame: list of quasi list of elements form that a probability sample is selected  Complied list of individual sampling units  Mist be up to date  In some cases may be unrepresentative  A summary description of a given variable  E.g. Proportions of people, who smoke, mean income  In a population is called a parameter  The corresponding number of a population  In a sample is called a statistic Simple random sampling  Most basic method of random sampling  Research participants selected at random from a sapling frame  Computer-assisted  Table of random number th October 30 2013 Sample types  Probability vs. Non-Probability Sampling  Probability  Most common in social research, particularly quantitative  Representative of larger population  Elements of a population stand equal change of selection  More complex  Non-probability  Simple  Common in qualitative studied  May be unrepresentative  Elements of a population do not stand equal change of selection Methods of Probability Sampling The theory and logic of probability sampling Conscious and unconscious sampling bias  The population of 100 has 44 white women, 44 white men, 6 Aboriginal women, and 6 Aboriginal men  Let’s say that I enter this population and the firs 10 people I meet are the white women. I select them for my study. Because I did not select a representation of the population (say, 3 white men, 3 white women, 3 Aboriginal women, and 3 Aboriginal men), I have a biased sample Systematic sampling  Ever K element in the sampling frame is chosen for inclusion in the sample  To avoid bias, choose the first element from the list at random  The way it works is, you have a sampling frame (a list of all the items in the population) and then you would select your sample by determining a sampling interval and than going down the list and take every 10 name until you got the sample you wanted  Sampling interval: the standard distance (k) in the list between the elements selected for sample Calculated as sampling interval= population size/sample size  Sampling ratio: the proportion of elements in the study population that are actually selected  Sampling ratio= sample size/population size  Inverse of the sampling interval For SOCY 210: Sampling Interval= 193/50=3.86 Sampling Ratio= 50/193= 0.259  The problem with systematic sample is if there is a commonalty or pattern among the list of names Stratified sampling  You begin with a table describing the characteristic of the target population and dividing it into homogenous subsets (strata)  E.g. Race, gender, nationality  A proportion of individual from each strata is assigned to the sample, matching the proportion of individual from that group who belong to the population at large  More representativeness  For example if you are really concerned with the proportion of diversity in the sample is equal to actual proportion. But we want it to be completely random  So you stratify the sample, if you have a sampling frame of all the people, you than rearrange the list into strata (vertically ranked categories) for example, the women at the top and the men at the bottom. So you than go in and to a simple random sample from the first group (female) and than the second group (the male group)  It doesn’t produce error because sampling error is produces by how heterogeneous (or mixed) the population is, the greater the mix, the more changed of a sample being misrepresented Types of Sampling Design  Stratified sampling Ex. Sampling university students Step 1 Step 2 From the Step 3 university roster, Then, select by select only 15,000 university classes full-time degree Sample size is set to students be 1,000 Then, set the sampling program for a 1/15 sampling ratio A random number from 1-15 will be generated • The student having that number and every fifteenth student Step 4 thereafter will be selected in the sample. Multistage cluster sampling Cluster sampling  Used when it is either impossible impractical to complex an exhaustive list of the elements that compose a large population  Population elements may already be grouped into subpopulation, of which list actually exist  Researchers can randomly choose a sample of these subpopulations and then sample individuals that belong to those selected subpopulations  The sampling is occurring at more than one level – the provincethe institutions the people in the institutions Example of multistage cluster sampling  Dr. C. Couture wants to study the demographics of university students who belong to Fraternities across Canada  How would he gather a random sample of 500 individuals?  It would be impractical to create a sampling frame with the list of every single member of a fraternity in Canada  He can use multistage cluster sampling instead 1. Make a list of all the provinces and territories (primary sampling frame) and puck 5 at random (either through SRS or systematic sampling)  These will be your primary sampling units 2. Make a list of all the fraternities in each of theses provinces. Territories (secondary sampling frame) and pick 5 fraternities from each list  These fraternities will from part of your secondary sampling units 3. Take the list of member of each of 50 fraternities selected (tertiary sampling frame) and select 20 individuals at random from each fraternity  This will provide you with 500 subjects Random digit dialing  The difference between telephone directories and RDD techniques  Generating a sample of telephone numbers  The increasing reliance on RDD  We would generate a list of the entire telephone prefix in the telephone code 613-555- 2232  Than we take a random number table and it generates for number suffixes and attaches them to the prefixes that may or may not be actually telephone numbers  So this list of phone number and it is given to interviewers, who are given a very specific way of dealing with these numbers  This will generate as good a sample of households, as any other sampling method An Intro to probability theory (the introduction to inference) Parameter vs. Statistic  Parameter: the summary description of a given variable in a population  Statistic: The summary description of a variable in a sample  Through statistics we try to infer the true value of a population parameter  How close is the statistic to the parameter? Can we use this as an estimate?  So how do we know if it is going to be accurate? The sampling Distribution  If many independent random samples are selected from a population and a statistic calculate in each of them, the sample statistic provided by those samples will be distributed around the population parameter in a bell-shaped fashion  The bell (or normal) curve is called the sampling distribution  So if we took a very large number of samples, and calculated the statistic in each case, those statistics would form a distribution, the from of a normal distribution, or a bell curve  But we don’t ever take thousands of samples, but because we know that the parameter is going to be the middle of the curve, we can make a general assumption that all of the samples are going to be relatively accurate  Value of the statistic on the x-axis  Number of samples that yield this statistic on the y-axis  The samples will begin to group themselves around the parameter 8 Parameter ~ 5.5 7 6 5 4 3 2 1 0 $0 $1 $2 $3 $4 $5 $6 $7 $8 $9 $10  There are 10 people in the population; this is how much money they have  What is the average amount of money in a population?  If you sampled 1 person it would look like this  If we are only sampling 1 person, there is a very good chance that our sample is going to be very off  Sampling 2 people would look like this  Narrowness is really important to know  Because it would be good to know how tightly the sample is to the parameter  If we are looking at a narrow curve, it is more likely to be close to the parameter  So we want to be able to calculate the sampling distribution, and how accurate and close to the parameter is  We want to know how spread out or condensed the distributions is Standard and sampling error  We can also calculate how closely sample statistics are clustered around the true value of the population parameter  i.e. estimate the degree of error to be expected for one given sample design : the sampling error  This is the difference between ONE sample statistic and the population parameter  So on average, how close are we to the center, and to know this it will be very helpful for us to now how wide or narrow the distribution is Calculating (estimating) the S.E from a sample binomial  s.e- √ (P x Q / n)  P is the population parameter we want to calculate  E.g. the proportion of sociology students who were born in Canada  Q= 1-P  n is the number of cases in each sample  The standard error indicates how tightly sample estimates will be distributed around a population parameter  If small, the samples statistic most closely resemble the population parameter  The formula assumes we do no the population parameter Standard error  Suppose in Socy 210, 70 percent support the use of a study guide and 30 percent oppose it, what in the standard error of the sampling distribution?  √.7 X .3/50 = .064 or 6 percent  Our sample yields a figure of 67 percent in favour  We know that 95 percent of the samples of size 50 would fall between 58 and 82  A property of normal curve is that even though the standard error of the normal curve will differ from one to another, it is the case in every normal distribution; the cases will be distributed in a particular proportionate way, given the sizes of a standard sample  Each line down, is one more error away, and so the first line ever cases will be within plus or minus 1 error away, the second line will be plus or minus 2 curves away, and the 3 line is plus or minus 3 errors away  So we would be able to get the interval of where the parameter lies, because it is in plus or minus of where our errors is S.E in the normal distribution  Certain proportions of the sample statistics will often fall within a specified number of standard errors from the population parameter  Approximately 68% of sample statistics will fall within 1 and -1 s.e. of the population parameter  95% of sample statistics will fall within 2 and -2 s.e. of the population parameter  99% of sample statistics will fall within 3 and -3 s.e. of the population parameter Rule 1  First, if many independent random samples are selected from a population, the sample statistics provided by those samples will be distributed around the population parameter in a known way. Rule 2  Probability theory gives us a formula for estimating how closely the sample statistics are clustered around the true value. In other words, probability theory enables us to estimate the sampling error—the degree of error to be expected for a given sample design. This formula contains three factors: the parameter, the sample size, and the standard error (a measure of sampling error). Rule 3  The standard error is also a function of the sample size—an inverse function. As the sample size increases, the standard error decreases. Rule 4  Another general guideline is evident in the formula: Because of the square root in the formula, the standard error is reduced by half if the sample size is quadrupled. Level of confidence  We can express the accuracy of a sample statistic in terms of the level of confidence that the statistic falls within a specified interval from the parameter  The more confident we wish to be, the larger the error is  Confidence interval: A range in which we expect a sample statistic to lie for a given percentage of the time (confidence level)  E.g. 95% or 68% of the time we expect a sample statistic to fall between a ranges of number (the confidence level Calculating confidence intervals  Prof. J. Benhaim wants to estimate the proportion of McGill students who voted in the past Canadian Federal election. To do so (instead of asking every McGill student if they voted or not) she collects a random sample of 100 students. She finds that 35% of those students voted in the past federal election.  How would she go about getting a 95% confidence interval of McGill students who voted in the past federal election? Calculating Confidence Intervals 1. Calculate the standard error s.e. = √ (P x Q/ n) P= 0.35; Q= (1-0.35) = 0.65 n= 100 s.e.= √ (0.35 x 0.65/100) = √0.002275 = 0.048 2. Calculate the confidence interval 95 % c.i. = P  2 x s.e. = 0.35  2 x 0.048 = [0.254, 0.446] or between 25.4% and 44.6%  The sampling error is greatest the population is heterogeneous, or when the division is 50/50 we will have the largest sampling error 1x9=9; 2x8=16; 3x7=21, 4x6=24; 5x5=25  So we calculate the confidence error at 95%, which means that we are 95% confident that the real population parameter is between 25.4% and 44.6% Example  We want to undertake a study of Queen’s student to determine how man drank to a point of inebriation on the weekend  We decided to interview 500 people and to employ some kind of random design  Now… we know that out random sample will come from all possible samples of 500 Refer to note Qualitative field research Why Qualitative field research  Social research ‘right where it happens’  No armchair philosophy’ but direct involvement  No Artificial setting – eg. Experiments  Deeper understanding of social phenomena  Probing social processes over time as they happen  Good to study social processes over time as they happen  Data collecting AND theory generating  If you really want to understand human actions, you have to try and understand human action within its own framework  In order to really make sense of what people are doing in their worlds, you have to get back inside their worlds  Because it is unique, it comes with a lot of traps and problems  This is no necessarily the easiest thing to do  Because it is research that takes place over time, it has a remarkable type of flexibility those others to do not have Historical context  The influence of the University of Chicago and in particular the writing of Robert Park  Because he has lived the life of a newspaper reporter, he has a real sense for the complexity of the city, specifically what happens in isolated dark communities  So, as an academic he encouraged his students to just go out and explore and study Types of fields of studies Case Study Design  Common in qualitative fieldwork  Not a mode of observation, but a type of research design where attention is paid to a single instance of some social phenomenon  Se in both qualitative and quantitative research  Sometimes used as preliminary to a more elaborate study—exploratory purpose  Any study that we focus a particular fashion on a small and specific group is a case study  Christie Pitz baseball riot Extended case method  Developed by Michael Burawoy and colleagues  Used to discover flaws and modify exisiting social theories  1 step: researcher enters the field with a clear expectation of what to find  Become familiarized with existent literature  Step: notes how observations conflict with extant theory and whether they support or reject what already exist  Theory Observations=largely deductive Grounded theory approach  Derives theory from analyzing patterns and these discovered in observational data  Researcher does NOT begin with preconceived ideas—but instead allows theory to emerge from the data  Balance subjectivity/objectivity by thinking comparative, gaining multiple points of view, fathering data in a variety of ways, checking with respondents, maintain skepticism  Suppose you know absolutely nothing about catholic religious observant, so what you are doing is going in a Catholic Mast, your first pass at this would be to determine the major pieces of Church  So you come up with categories, you conceptualize them, you relate them and than you begin to understand and put together the pieces of what is going on  Shortcoming of not having structures theoretical framework are overcome by rigorous data analysis  Methodological choices guided by an alternating process of fata collections and analysis  Coding procedures assist the researcher in both systematically and creatively identifying, developing, and relating concepts  Observation Theory= largely inductive  Approaching the field without preconceptions Ethnographies  Rooted in naturalism- observing events and people in a natural setting; this is important scientific because it considers what people do not do what they say they do  Aim for deep understanding of someone’s community’s way of life  Involve  Direct first-hand observation  Note taking, photographing, drawing  Interviewing  Archival analyses  In some cases, concealing one’s real identity Preparing to hit the field: Getting In  Gather the relevant literature Discuss your field with those who have studied it or know something about it  Gaining access to the field  Identify the Gatekeeper: formal access and consent by contacting the person in charge  Seek out an Informant: establishing contact with a member who introduces you to the field  Joining the group: securing membership status being hired within an organization  In some cases deception is used Ethical concerns-balance between harm to subjects and usefulness or research  Deception requires you to play a certain role, that you are not prepared to play—you cannot slip Establishing Initial Contacts  Develop a rapport with your subjects in order to gain their trust and make them feel conformable  Your initial contact may influence you subsequent observations, the data you are able to collect, and the people you are able to reach  Deciding how much to disclose of your study to your subjects should depend on the nature and purpose of your research  As well as ethical considerations!  Voluntary participation, informed consent The various roles of the researcher  You initial contact depends on your role as a researcher  Complete observer: observes the field but does not participate in the activities that take place  Participant observer: reveals that she/he is researcher but still participates in the activities that take place in the field  Complete participant: engages fully in activities with other members Note-taking  Take full and accurate notes of your observations  If possible, take them as your make them, or as soon as you can  Do no trust your memory more than needed  Take quick notes at first, more elaborate ones later  Process is made easier by having standardized recording forms, or codes that can be easily jotted down  Notes include empirical observations AND a researcher’s interpretation of them  Should be distinguishable  You not only want to write down what happens, you also want to write what you this what happens means  Included as much detail as possible—later on decide what is relevant Leaving the field  Fieldwork should end when theory building reaches a closure  Leaving can be disruptive for both researcher and subjects  Types of exit (quick exit, slow withdraw)  Exit strategy should depend on the field and the relationships developed You would do this if:  Your position becomes intangible (people find out, are upset with you, it’s dangerous, your research is over) Strengths and weaknesses of field research  Advantages of field research  In depth understanding of social phenomena and subtle nuances in attitudes and behaviours  Flexibility  May or may no be expensive  Validity: Comprehensive measure which tap into great depth of meaning, instead of relying on concepts, detailed researchers often give detailed illustrations  Disadvantages of field research  Reliability: Measurements depend on the researcher making observations, the researcher may have an impact on the field and the data she/he collects  It involves a humanistic aspect of research that other research doesn’t have Institutional Ethnography  Developed by Dorothy Smith in conducting research from the standpoint of women  Pays close attention to the voices of subordinated groups and how their experiences are shaped by institutional practices  While looking at personal experiences, the goal is to uncover deeper institutional and structural power relations that given them  Uncover forms of oppression that are often overlooked by more traditional types of research Participatory actions research (PAR)  Critiques the distinction between researcher and subject  Challenges the usefulness of classical objectivity  The researcher becomes a resource to those being studied (typically disadvantages g
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