I attached the study guide Qualitative Methods:Lecture: Content analysis, intercoder reliability, examples of content analysis your professor has done, code sheets as a way to systematize content analysisChapter 8: reactivity, primary data, secondary data, participant observation, field study, direct and indirect observation, overt and covert observation, structured and unstructured observation, ethnography, ethical issues in observation (threat to subjects in comparison to other methods), Institutional Review Boards (IRBs), the difference between an erosion measure and an accretion measure, potential problem(s) with physical trace measures in studying political phenomenaChapter 9: document analysis qualitative, quantitative or both, content analysis and its procedures, sampling frame, recording units (what are they, what if they are too small?), intercoderreliability, running record vs. episodic record (what each is, examples, advantages and disadvantages), advantages and disadvantages of archival (written) recordsSurvey ResearchLecture: most important lesson for us as consumers of surveys, sampling, population, sample, the logic of sampling (why it makes sense with the rules of statistics that a sample is a reasonable estimate of the population), confidence interval and margin of error, confidence level, types of information that questions generally askfor (knowledge, opinions, experiences, feelings), common sources of error in survey research (timing, phrasing of questions, order of questions, interpretation of responses), American Journalism Review study, Bradley effect, intangible problem in samplingdiscussed in lecture, Chapter 10: survey research vs. interviewing, survey instrument, the importance of pre-testing questionnaires, response rates, response quality, possible types of bias (leading questions, interviewer bias, etc.), ways to prevent bias in surveys, sample-population congruence, open-ended vs. close-ended questions (advantages, disadvantages, reasons to use one over the other), types of surveys (face to face, telephone, internet, etc.), potential problems with questions (leading, narrow, ambiguous, double barreled, etc.), the impact of interviewer characteristics, probing, question wording and ordering effectsStatsIntro, Distributions, Descriptive StatisticsLecture: the normal distribution, standardized (Z) scores, the bell curve, properties of the normal distribution (shape, symmetry, meaning of standard deviation, empirical rule, ability to use standardized scores), percentiles (what are they, how are they different from a percentage), t Distribution (what is it, what do we use it for?); descriptive statistics, frequency distributions, percentages as a VERY easily understood statistic, measures of central tendency and the levels of measurement to which they correspond,measures of dispersionChapter 11: response set, frequency distribution, relative frequency, descriptive statistics, trimmed mean and outliers, positive and negative skew, measures of central tendency, mode, median, mean, range, minimum and maximum, inter-quartile range, resistant measures, measures of dispersion, standard deviation, variance, types of charts and graphsChapter 12: statistical hypothesis, null hypothesis, absolute value, sampling, Type I vs. Type II error, as standard deviation increases in size what happens tothe standard error of the mean, level of statistical significance, factors that affect significance, steps for hypothesis testing, significance tests of a mean (normal distribution vs. small (t) distribution), degrees of freedom in t, finding the t Value (alpha see example in Figure 12-4), a z-score of 1.96 means what, confidence intervals and levels (what are they, why do we use them, the general form of confidence interval)Measures of RelationshipsLecture: percentage differences as the simplest way to show relationships, comparing measures of central tendency, strength of relationships (logic: the extent to which changes in one variable are accompanied by changes in another no matter what level of measurement, the basic logic is the same), Yules Q and its properties, ultimately what do we want to do? We want to reduce error! The idea for all of our measures is, ultimately, to know how much we can reduce error in our estimates of a dependent variable by knowing the values of an independent variable (or multiple independent variables), the basic equation (in words) of the measure of reduction in error, measures for nominal data (lambda, tau), measures for ordinal data (gamma, somers d), measures of relationship for interval level variables (r, r-squared), steps: start with a graph (three elements of a graph), the regression line (what does it tell us about the variables, think of it as a prediction), parts of the regression line: slope, direction, strength of relationship, what the slope (b) tells us, what the Y intercept with zero tells us, what Pearsons r and r-squared tell us, rule of thumb about a strong value of rChapter 13: levels of measurement and the statistical procedures that go with them, types of relationships (association, monotonic and linear correlation), types of correlation, what does a measure of association tell us, what do cross-tabulations show us, nominal measures of association, ordinal measures of association (what are concordant pairs, discordant pairs, tied pairs), bounded measures such as Pearsons r vary between -1 and 1, if the categories of an independent variable are across the top of a table (across the columns) then what should the percentages down each column add up to (100%), the effect of increased sample size on Chi-squaredMultiple variablesLecture: two kinds of information in multiple correlation/multiple regression (cumulative and partial), time series analysis, interpreting the strength of a relationship what dorelationship measures tell us, when are relationship measures particularly useful, Chapter 14: analyzing multivariate relationships with nominal and ordinal level data (what can you do? Dont worry about technicalities just understand that you can do this with cross-tabulation, how can you control for a third variable?), multiple linear regression (used with a dependent variable of what level of measurement?), constants (beta y when all the independent variables have a value of zero), partial regression coefficients, interaction between variables, homoscedasticity, multicollinearity and assumptions about the error terms in linear models (see helpful hints table on p 530), dummy variables, spurious relationships, standardized regression coefficients, ways in which standardized and unstandardized regression results are similar and different, logistic regression (when do we use this? It has to do with the type of dependent variable) Statistical SignificanceLecture (posted on Canvas): how statistical significance differs from strength of relationship; review of the normal distribution and standard deviation and standard errors, difference between margin of error and confidence level; Verba and Nie example, examples of different measures of statistical significance

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PS 3000 Exam Two Review Sheet

Spring 2017

This will be a multiple choice exam with 60 question, worth 100 points. Be sure to bring a Number 2

Pencil with you to the exam.

Qualitative Methods:

Lecture: Content analysis, intercoder reliability, examples of content analysis your professor has done,

code sheets as a way to systematize content analysis

Chapter 8: reactivity, primary data, secondary data, participant observation, field study, direct and

indirect observation, overt and covert observation, structured and unstructured observation, ethnography,

ethical issues in observation (threat to subjects in comparison to other methods), Institutional Review

Boards (IRBs), the difference between an erosion measure and an accretion measure, potential problem(s)

with physical trace measures in studying political phenomena

Chapter 9: document analysis qualitative, quantitative or both, content analysis and its procedures,

sampling frame, recording units (what are they, what if they are too small?), intercoder reliability, running

record vs. episodic record (what each is, examples, advantages and disadvantages), advantages and

disadvantages of archival (written) records

Survey Research

Lecture: most important lesson for us as consumers of surveys, sampling, population, sample, the logic

of sampling (why it makes sense with the rules of statistics that a sample is a reasonable estimate of the

population), confidence interval and margin of error, confidence level, types of information that questions

generally ask for (knowledge, opinions, experiences, feelings), common sources of error in survey

research (timing, phrasing of questions, order of questions, interpretation of responses), American

Journalism Review study, Bradley effect, intangible problem in sampling discussed in lecture,

Chapter 10: survey research vs. interviewing, survey instrument, the importance of pre-testing

questionnaires, response rates, response quality, possible types of bias (leading questions, interviewer

bias, etc.), ways to prevent bias in surveys, sample-population congruence, open-ended vs. close-ended

questions (advantages, disadvantages, reasons to use one over the other), types of surveys (face to face,

telephone, internet, etc.), potential problems with questions (leading, narrow, ambiguous, double barreled,

etc.), the impact of interviewer characteristics, probing, question wording and ordering effects

Stats

Intro, Distributions, Descriptive Statistics

Lecture: the normal distribution, standardized (Z) scores, the bell curve, properties of the normal

distribution (shape, symmetry, meaning of standard deviation, empirical rule, ability to use standardized

scores), percentiles (what are they, how are they different from a percentage), t Distribution (what is it,

what do we use it for?); descriptive statistics, frequency distributions, percentages as a VERY easily

understood statistic, measures of central tendency and the levels of measurement to which they

correspond, measures of dispersion

Chapter 11: response set, frequency distribution, relative frequency, descriptive statistics, trimmed mean

and outliers, positive and negative skew, measures of central tendency, mode, median, mean, range,

minimum and maximum, inter-quartile range, resistant measures, measures of dispersion, standard

deviation, variance, types of charts and graphs

Chapter 12: statistical hypothesis, null hypothesis, absolute value, sampling, Type I vs. Type II error, as

standard deviation increases in size what happens to the standard error of the mean, level of statistical

significance, factors that affect significance, steps for hypothesis testing, significance tests of a mean

(normal distribution vs. small (t) distribution), degrees of freedom in t, finding the t Value (alpha see

example in Figure 12-4), a z-score of 1.96 means what, confidence intervals and levels (what are they,

why do we use them, the general form of confidence interval)

Measures of Relationships

Lecture: percentage differences as the simplest way to show relationships, comparing measures of central

tendency, strength of relationships (logic: the extent to which changes in one variable are accompanied

by changes in another no matter what level of measurement, the basic logic is the same), Yules Q

and its properties, ultimately what do we want to do? We want to reduce error! The idea for all of our

measures is, ultimately, to know how much we can reduce error in our estimates of a dependent

variable by knowing the values of an independent variable (or multiple independent variables), the

basic equation (in words) of the measure of reduction in error, measures for nominal data (lambda, tau),

measures for ordinal data (gamma, somers d), measures of relationship for interval level variables (r, rsquared), steps: start with a graph (three elements of a graph), the regression line (what does it tell us

about the variables, think of it as a prediction), parts of the regression line: slope, direction, strength of

relationship, what the slope (b) tells us, what the Y intercept with zero tells us, what Pearsons r and rsquared tell us, rule of thumb about a strong value of r

Chapter 13: levels of measurement and the statistical procedures that go with them, types of relationships

(association, monotonic and linear correlation), types of correlation, what does a measure of association

tell us, what do cross-tabulations show us, nominal measures of association, ordinal measures of

association (what are concordant pairs, discordant pairs, tied pairs), bounded measures such as Pearsons r

vary between -1 and 1, if the categories of an independent variable are across the top of a table (across the

columns) then what should the percentages down each column add up to (100%), the effect of increased

sample size on Chi-squared

Multiple variables

Lecture: two kinds of information in multiple correlation/multiple regression (cumulative and partial),

time series analysis, interpreting the strength of a relationship what do relationship measures tell us,

when are relationship measures particularly useful, Chapter 14: analyzing multivariate relationships with

nominal and ordinal level data (what can you do? Dont worry about technicalities just understand that

you can do this with cross-tabulation, how can you control for a third variable?), multiple linear

regression (used with a dependent variable of what level of measurement?), constants (beta y when all

the independent variables have a value of zero), partial regression coefficients, interaction between

variables, homoscedasticity, multicollinearity and assumptions about the error terms in linear models (see

helpful hints table on p 530), dummy variables, spurious relationships, standardized regression

coefficients, ways in which standardized and unstandardized regression results are similar and different,

logistic regression (when do we use this? It has to do with the type of dependent variable)

Statistical Significance

Lecture (posted on Canvas): how statistical significance differs from strength of relationship; review of

the normal distribution and standard deviation and standard errors, difference between margin of error

and confidence level; Verba and Nie example, examples of different measures of statistical significance

…

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