Studied by 1 personSociological Imagination
Ability to see the connection between the larger world and our personal lives
Generalizable
can explain a broad class of events
Agency
our capacity to make our own choices and act autonomously
Basic Research
seeks to answer theoretically informed questions or resolve fundamental intellectual puzzles about social behavior
Applied research
seeks to answer a question or concrete problem in the real world or to evaluate a policy or program
Mixed-Methods Approach
a general research approach that uses qual and quant in a single study
Triangulation
the use of multiple methods to study one research question (more general than mixed methods)
Cross-Sectional Study Design
a study in which data are collected at only one point in time
Repeated cross-sectional study design/trend design
a type of longitudinal study in which data are collected at multiple time points, but from different subjects at each time point
Panel Design
a type of longitudinal study in which data are collected on the same subjects at multiple time points
Causality
a relationship where one factor or variable is dependent on another factor or variable Studies that track individuals at multiple points in time are better suited for ascertaining cause and effect than a single cross-sectional design
Units of Analysis
refers to the level of social life about which we want to generalize
Ecological fallacy
a mistake that researchers make by drawing conclusions about the micro level based on some macro level analysis
The Scientific Method
the systematic process of asking and answering questions in a rigorous and unbiased way
The Scientific Method steps (5)
Identify an important question that needs an answer
Construct a hypothesis about the answer to this question
Gather data that allow the researcher to assess the accuracy of this prediction
Analyze the data to determine whether the prediction is accurate
Draw and report conclusions
Value-Free
the goal of being objective and not biased by personal ideologies
Subjectivity
the way research is influenced by the perspectives, values, social experiences, and viewpoint of the researcher
Reflexivity
a process of attending systematically to the context of knowledge construction, especially to the effect of the researcher, at every step of the process
Descriptive Research
the what. documents or describes trends, variations, and patterns of social phenomena
Exploratory Research
tends to answer questions of how, with the goal of documenting precisely how particular processes and dynamics unfold
Explanatory Research
documents the causes and effects of social phenomena, thus addressing questions of why
Purpose of theory
describe, explain, predict, control
Empiricism
the idea that the world can be subjected to observation, which is the use of the senses to gather data about social phenomena
Inductive Approach
the process by which scientists draw up a general understanding of social phenomenon through empirical observations
Inductive Approach goal
observe patterns and the build up to explanation. Concrete to abstract; specific to general; bottom up
Deductive Approach
the translation of general theory into specific empirical analysis
Deductive Approach goal
create an argument to organize and guide empirical activities. Abstract to concrete; general to specific; top down
Mediation
the expected relation between two concepts is channeled through a third concept that links them to each other
Moderation
the strength of the association between two variables is made weaker or stronger by a third variable
Null Hypothesis
the hypothesis that no relationship between concepts exists or no difference in the dependent variable between groups exists
Spuriousness
when an apparent relation between two concepts is actually the result of some third concept (confound(er)) influencing both of them. unlikely to be a part of a theory and more likely to come up when testing a theory
Confound
a third variable that is linked to two concepts in a way that makes them appear to be related even when they are not
Conceptualization
the process of precisely defining ideas and turning them into variables
Operationalization
the process of linking the conceptualized variables to a set of procedures for measuring them. The process of identifying a plan for measurement. It involves making trade-offs between the potential benefits of using proven measures and developing novel ways to measure variables
categorical variables
Have a finite set of possible values No known distances between values Includes nominal and ordinal variables
Nominal variables
categorical variables that have catalog states or statuses that are parallel and cannot be ranked or ordered no mathematical interpretation (Ex: race, state)
Ordinal variables
categorical variables for which the categories have a natural ordering
Continuous Variables
Have an infinite set of possible values Values have fixed distances between them Includes interval and ratio variables
Interval variables
Continuous variables that have a continuum of values with meaningful distances between them, but not true zero. The values can be compared directly, but they cannot be used in proportions or mathematical operations (Ex: SAT score, temperature)
Ratio variables
Continuous variables that do have a true zero, and the distance between values can be measured, and values can be expressed as proportions (Ex: school size, income in US dollars)
Indicator
the values assigned to a variable to provide the blueprint for measurement (How many weeks/months, wages over the weeks, who you vote for)
Reductionism
a mistake that researchers make by drawing conclusions about the macro-level unit based on analysis of micro-level data
Reliability
a quality of a measure concerning how dependable it is
Validity
a quality of a measure concerning how accurate it is
Composite Variables
variables that average a set of items to measure the same concept
Cronbach's Alpha
a calculation that measures a specific kind of reliability for a composite variable Score of 0-1, conventional standard is 0.7+ for a highly reliable composite measure
Internal Reliability
the degree to which the various items in a composite variable lead to a consistent response and, therefore, are tapping into the same concept
Intercoder Reliability
reveals how much different coders or observers agree with one another when looking at the same data
Precision
a quality of measurement referring to how detailed and specific it is more categories = more precision
Robustness
a quality of an operational protocol referring to how well it works
Split-Half Method
assesses robustness by testing the similarity of results after administering one subset of an item to a sample and then another subset
Test-Retest Method
assesses robustness by administering a measure to the same sample at two different times
Pilot Testing
a method of administering some measurement protocol to a small preliminary sample of subjects as a means of assessing how well the measure works
Internal Validity of a Study
the degree to which the study establishes a causal effect of the independent variable on the dependent variable
Internal Validity of a Measure
the degree to which the measures truly and accurately capture concepts
Face Validity
a dimension concerning whether a measure looks valid
Criterion-Related Validity
concerns how closely a measure is associated with some other factor (Concurrent validity & Predictive validity)
Content Validity
concerns how well a measure encompasses the many different meanings of a single concept.
Construct Validity
how well multiple indicators are connected to some underlying factor
External Validity
concerns the degree to which the results of a study can generalize beyond the study
Probability Sample
A sample chosen via random selection Random chance is used to select participants for the sample, where each individual has a probability of being selected that can be calculated
Target Population
a group about which social scientists attempt to make generalizations They do not necessarily refer to groups or individuals. It can also include nations, etc.
Census
a study that includes data on every member of a population
Population Parameter
a number that characterizes some quantitative aspect of a population
Nonprobability Sample
a sample that is not drawn using a method of random selection
Convenience Sample
the simplest type of nonprobability sample, for which the cheapest and easiest observations are selected
Unbiased
the sample estimate is the same as the population parameter except for the difference caused by random chance
Systematic Error
a flaw built into the design of the study that causes a sample estimate to diverge from the population parameter
Margin of Error
the amount of uncertainty in an estimate; equal to the distance between the estimate and the boundary of the confidence interval
Sampling Distribution
a set of estimates that would be observed from a large number of independent samples that are all the same size and drawn using the same method
Confidence Level
the probability that an estimate includes the population parameter (typically 95%)
Confidence Interval
the range of values implied by the margin of error
Sampling Frame
a list of population members from which a probability sample is drawn
Simple Random Sample
a type of probability sample in which each individual has the same probability of being selected, and each pair of individuals has the same probability of being selected
Systematic Sample
a probability sampling strategy in which sample members are selected by using a fixed interval, such as taking every 5th person on a list of everyone in the population Each pair in this sample does not have an equal chance of selection
Cluster Sample
a probability sampling strategy in which researchers divide up the target population into groups, or 'clusters,' first selecting clusters randomly and then selecting individuals within those clusters
Advantages to cluster sampling (2)
Researchers can take probability samples when a sampling frame-a list of all population members-doesn't exist Researchers can conduct many more surveys at a much lower cost than they would incur with simple random sampling
Stratified Sample
A type of probability sampling in which the population is divided into groups with a common attribute and a random sample is chosen within each group
Oversampling
a group that is deliberately sampled at a rate higher than its frequency in the population
Weighting
determines how much sample members "count" when producing estimates
Members of stratified samples may be weighted differently, to account for the fact that the sample is no longer representative A group that is oversampled should receive less weight than other members of the sample
Variable-Oriented Research
scientists study a large number of cases, but gather only a limited amount of data (or variables) about each
Case-Oriented Research
scientists gather large amounts of data about a single case or small number of cases Choosing a single case to study is still considered sampling!
Purposive Sampling
a sampling strategy in which cases are selected on the basis of features that distinguish them from other cases
Sequential Sampling
enables researchers to make decisions about what additional data to collect based on their findings from data they've already collected
Key Informants
individuals who have intimate knowledge of a subject and are willing to share it with the researcher
Saturation
when new materials fail to yield new insights and simply reinforce what the researcher already knows
Snowball Sampling
a strategy in which the researcher starts with one respondent who meets the requirements for inclusion and asks them to recommend other people to contact
Snowball sampling pros
Useful for studying populations we would otherwise know very little about
Random Assignment
distributes individual differences equally across conditions Ensures that the only difference between the experimental group and the control group is the independent variable manipulated in the experimental group
3 advantages of experiments
The best research method to establish causality
Can uncover mechanisms that produce an outcome, explaining both if and why
Can be used to evaluate abstract theories about the social world
3 conditions for establishing causality
2 variables must be correlated
The cause (independent variable) must precede the effect (dependent variable)
The relationship between the independent variable and dependent variable must not be spurious
Concurrent validity
how closely the measure is associated with a preexisting measure
Predictive validity
how closely the measure is correlated with something it should be correlated with
Experimenter Effects
unintended changes in subjects' behavior due to cues inadvertently given by the experimenter
Selection Bias
in an experiment, unintended differences between the participants in different groups
Laboratory Experiments
take place in laboratories, giving researchers the maximum amount of control over the environment in which the experiment is conducted
strengths of Laboratory Experiments (2)
high degree of internal validity
Highly artificial setting allows researchers to assess causality and test abstract theories
Field Experiments
takes place in a natural or 'real-world' setting Often used to evaluate the success of interventions to improve educational and health outcomes
Audit Studies
a type of field experiment that assess whether the characteristics such as gender, race, and sexual orientation lead to discrimination in real labor and housing markets
Factorial Design
have two or more independent variables. This allows researchers to measure various characteristics at once
Note
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