study guide Qualitative Methods

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), Yule’s 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, somer’s 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 Pearson’s 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 Pearson’s 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? Don’t 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), Yule’s 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, somer’s 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 Pearson’s 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 Pearson’s 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? Don’t 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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