Empirical Research Methods Test 2

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Quantitative Research

Back 1

How much of something? Surface, many people

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Qualitative Research

Back 2

Why? Depth, less people

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Three Qualitative Assumptions

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Natural, Phenomenological, Interpretive

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Natural

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Context: everyday life circumstances affect interaction

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Phenomenological

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attempts to bracket (separate) their assumptions

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Interpretative

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Researcher and participants perspectives are woven together when reported (“giving voice”)
o NOT just the researcher, participants speak
o Exemplar quotes: group findings into themes and look for best example(s) of the theme

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Participant

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Full immersion, group members are unaware that researcher is an outsider

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Participant-Observer

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Consultant role, collecting information for a company, involved but also studying, group members are aware that researcher is an outsider

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Observer-Participant

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More removed than the last, participants know you are an outsider, not part of decision making, just sitting and observing

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Interviewer

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Talk and ask questions, not in lived experience

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Ethnography

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exploring the shared understandings necessary to be a member of a particular group (Full participant)

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Critical Ethnography

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exploring the shared understandings necessary to be a member of a particular group with a goal of reduction of and emancipation from oppression (very interested in power structures and social equality)

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Auto-Ethonography

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reporting on one’s own immersion and experiences within a group

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Ethnomethodology

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exploring the taken-for-granted, daily actions people use to socially construct their world (e.g. conversation analysis)
o I.e. what is a common day for a migrant farmer (framework/structures)

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Interviews

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are in-person surveys where you can alter the content to meet researcher needs
• Most removed from participant involvement
• Must be sensitive to setting (where to conduct?)
• Must be aware of sensitive topic areas (funnel v. inverted funnel)

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Descriptive Questions

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mini-tour: Tell me about your typical day…

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Structural

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How often do you call your spouse in a day?

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Compare/Contrast

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Which is more important to you A or B?

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Focus groups

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• Bringing people together usually 6-12 people
• Require a moderator, facilitator, or a conversation regulator
• Must be aware of those who monopolize or are hesitant
• Advantage: spurs thinking through others’ comments
• Disadvantage: Must avoid “group-think”

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Grounded Theory

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Glaser and Strauss 1967; it is a systematic methodology in the social sciences involving the discovery of theory through the analysis of data

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Analytic Induction

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looking for emerging patterns (meaning units)

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Open Coding

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(inductive): first read, passing, first attempt at looking for meaning units, group common concepts

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Axiel Coding

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(deductive): once themes are established look for specific examples to support main themes

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Constant Comparative Analysis

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thematic groupings

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Validity Checks

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Intercoder reliability, reader confirmation, cross-check with the participant

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Quantitative Analysis Questions

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• How do people typically behave?
• What is common (average)?
• What is “typical” communication?
• What do people “normally” experience?
• What happens most of the time?

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Descriptive Statistics

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summary characteristics of a data set
o Tell us about our data, lots of numbers (reduction) & reduce them
o Deductive

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Inferential Statistics

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used to generalize and make claims from the data set
o Inductive

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Central tendency

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How do people tend to group together?

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Dispersion

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how spread out are peoples' thoughts from the middle

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3 Main Summary Statistics

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Mode, Median, and Mean

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Mode (Mo)

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the most frequent (most often) value in a data set
o Bi-modal: when two modes exist
o Multimodal: when more than two modes exist

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Median (Mdn)

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divides the sample exactly in half

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Mean (M)

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o Add values in data set and divide by total number of values
o Outliers: people on the far spectrum; sometimes are dropped to determine a more accurate mean.
o M-Population often not possible b/c it requires a census

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Bell Shaped Curve

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o Even distribution around mean
o Important for generalizing

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Peaked Curve

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o Agree more, not much variation

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Negative Skew

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o Opposite of what it is—where the tail goes
o i.e. TV watching

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Positive Skew

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o Opposite of what you would assume
o i.e. US Income

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Range

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• Distance between the highest and lowest score

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Variance

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• Average distance from the mean insquared units
• Formula: sum of squares/number of scores
• Only works if it is a bell curve!
o Bell curve can be established if the Mean and Standard Deviations are available

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Standard Deviation

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how much scores vary from the mean in the original unit of measurement (= variance check)

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Standard Scores (z-scores)

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• How much scores vary from the mean in common units (ex; p. 303)
• Taking out the variance and shifts everything to the center
• Allows researchers to be more standardized

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Interval Statistics

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used to generalize and make claims from a data set

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Estimation

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generalizing sample characteristics (sample) to population characteristics (parameters=M)

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Characteristics of a Normal Distribution

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• Center point is the exact middle & highest point
• Center point is the M, Mdn, & Mo
• Scores on both sides are symmetrical (in SD)
• As a result, value probabilities (% of population) can be estimated
• Most variables are assumed to be normally distributed in populations
o i.e. low, below average, average, above average, high
o unless known to be otherwise (i.e. income)

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Central Limits Theorem

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• For random samples of sufficiently large size, the distribution is almost always approximately normal even if smaller samples are not

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Law of large numbers

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the larger the random sample, the closer it approximates the true population and the small sampling up to 1,600-2,000 people (little error reduction beyond that)

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Random Sample

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the population parameters are “x”

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Non-Random Sample

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the sample characteristics are “x” and we believe the population parameters are like “x” (but cannot know for sure)

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Significance Testing

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• Significance testing=null hypothesis testing
• Significance Testing: the process of testing
• Based on previous research (literature review), researchers ask for questions or make predictions about differences between groups or relationships between variables

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Research Question/Two-tailed hypothesis

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explores or predicts if any difference or relationship exists between variables

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One-tailed hypothesis

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predicts a specific difference or if a relationship exists
H: Males disclose more than females

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Null Hypothesis

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states no difference or relationship exists (any found are simply random chance)
H: No difference between males and females regarding of self-disclosure

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Statistical Tests

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do NOT prove anything, they show percent of likelihood of occurring

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Type I Error

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rejecting a null hypothesis that is probably true, when generalized to the larger population it does not fit

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Type II Error

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accepting a null hypothesis that is probably false

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Statistical power

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the probability of rejecting a false null hypothesis (a correct assessment for the population)

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Difference Analysis

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examines differences between categories of an independent variable on another variable

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Nominal Variable Tests

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Chi Square

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Chi-Square Test

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examines the differences between categories of nominal variables

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One-Variable Chi-Square Test

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differences between categories of one variable
• IV=stadium food—Are preferences different for hotdogs or metts?

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Two-variable Chi-Square test

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differences between categories of two variables
• IV= Sex (Male/Female) DV (High, Low)

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Ordinal Data Tests

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Median Test

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Median Test

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differences between categories on ranked variable

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Interval/Ratio Data (Likert) Tests

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t-tests, ANOVA, Multiple Comparison Tests

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t-tests

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differences between 2 means (average) on interval/ration data

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Independent Sample t-test

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different groups (males, females) and groups are independent (nominal)

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Related Measure t-test

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same group, means at different times (pre/post tests)

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ANOVA

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(Analysis of Variance): differences between 2 or more means on interval/ratio (typically 3 or more)

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Multiple Comparison Test

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(Post hoc comparisons): are required to determine which differences are significant

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Types of Relationships between Variables

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Unrelated, Linear, Non-Linear

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Correlation

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a statistical relationship between 2 variables (i.e. indicates how similar are peoples’ shared meanings on 2 given variables (operationalized groups). NO ASSUMPTIONS OF CAUSATION ARE MADE!

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Correlation Coefficient (r)

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indicates the level of association

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Pearson's r

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calculates a correlation by comparing how much a score on a measure deviates from a mean compared to how much each score on another deviates from a sample mean. The more similar the variation (the M) the more correlation

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Correlation Strength

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.90= high, .70=strong/standard, .40=moderate, .20-.30=weak, .10=very weak

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Coefficient Determination

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o Squares correlation
o Indicates the truest strength of association (i.e. just how similar is the variation across variables)
o .81=high, .49=strong, .16-.25=moderate, .04-.09=weak, .01=very weak

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Correlation Matrix

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presenting correlations and significance levels in table format

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Partial Correlation

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partitioning out (controlling) the variance of 1 or more variables, when multiple variables are studied

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Regression

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Predicts or explains the change of one variable (criterion variable) based on the assumption of a causal relationship with a precursor (predictor) variable. Assumes causation

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F-test

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is used to calculate the statistical significance of causal relationships