PS 372: Introduction to Political Analysis - Exam One Review

Flashcards covering key concepts for Exam One in PS 372: Introduction to Political Analysis, including the scientific method, research design, conceptualization, measurement, quantitative methods, and ethics in research.

Scientific Method

A systematic approach to inquiry in political science, central to understanding what makes an inquiry 'scientific'.

Generalizability

A key concept in scientific inquiry, referring to the extent to which findings can be applied beyond the sample or specific context of the study.

Induction

A method of reasoning in the scientific method where specific observations lead to the formulation of general theories or principles.

Observation -> Pattern (Data collection) -> tentative hypothesis -> theory

Deduction

A method of reasoning in the scientific method where general theories or principles are used to derive specific predictions or hypotheses.

Theory -> Hypothesis -> Observation (data collection) -> Confirmation or rejection of hypothesis

Normative knowledge

• is evaluative or value laden and focuses on how things should be (the way things should be)

Non-Normative knowledge

Statements about what 'is', focusing on factual or observable realities. t is concerned with factual or objective observation is non-normative/ empirical

Variables

Characteristics or attributes that can vary or take on different values in a research study.

Independent Variable

A variable that is thought to cause or influence another variable; it is often manipulated by the researcher.

Dependent Variable

A variable that is thought to be affected by the independent variable; it is the outcome being measured.

Hypotheses

Testable statements proposing a relationship between two or more variables.

Research Hypothesis

A specific, testable prediction about the expected relationship between variables in a study.

Unit of Analysis

the entity that you are studying/want to analyze/ the unit to which the concept applies

• Aggregate level analysis: a collection of individual entries and wrapping into whole

• Ecological fallacy: When aggregate level phenomenon is used to make inferences at the individual level

Concept Definitions

escribes the concepts measurable properties and specifies the unit of analysis (people, states, nations, etc.)

Levels of Measurement

The different ways variables can be categorized and scaled, including nominal, ordinal, interval, and ratio.

Nominal Level of Measurement

A level of measurement where data is categorized without any inherent order or ranking (e.g., political party affiliation). describes variables that indicate only a difference between categories.

•Education: Which school did you attend?

•Very little precision, cannot be used with many statistical tools.

Ordinal Level of Measurement

A level of measurement where data can be ranked or ordered, but the differences between ranks are not necessarily equal (e.g., social class: low, medium, high). categories may be ranked in order in addition to indicating a difference between categories.

• Education: Please indicate the highest level of education you reached (elem., high, college, more).

• A little more precision, and can be used with more statistical tools.

Interval Level of Measurement

A level of measurement where data has equal intervals between values but no true zero point (e.g., temperature in Celsius or Fahrenheit). This level allows for meaningful comparisons and arithmetic operations, making it more precise than ordinal but less than ratio.

Education: What did you score on the SAT?

Ratio Level of Measurement

A level of measurement where data has equal intervals and a true zero point, allowing for meaningful ratios (e.g., age, income).

 Education: How many years of education?

• Most precision and can be used with most statistical tools.

Operationalization

The process of defining how a concept will be measured in a research study, translating abstract ideas into measurable variables.

Validity

The extent to which a measurement accurately reflects the concept it is intended to measure.

Reliability

The consistency of a measurement, meaning it produces the same results under the same conditions on different occasions.

Surveys

A quantitative research method involving the collection of data from a sample of individuals, often through questionnaires or interviews.

Experiments

A quantitative research method involving the manipulation of one or more independent variables to observe their effect on a dependent variable, typically in a controlled setting.

Lab Experiments

Experiments conducted in a highly controlled environment, allowing for precise manipulation of variables and measurement of outcomes.

Sampling

The process of selecting a subset of individuals or cases from a larger population to participate in a research study.

Random Sampling

A sampling method where every member of the population has an equal and independent chance of being selected for the sample, ensuring representativeness.

Stratified Sampling

A sampling method where the population is divided into homogeneous subgroups (strata), and then samples are randomly drawn from each stratum.

Convenience Sampling

A non-probability sampling method where participants are selected based on their easy accessibility and willingness to participate.

Threats to Validity

Factors or conditions that can undermine the accuracy, generalizability, or causal claims of a research study.

Ethics in Research

Moral principles and guidelines that govern the conduct of research, particularly concerning the treatment of participants and the responsible use of data.

Human Subjects

Individuals who participate in research studies, requiring special ethical considerations to ensure their rights, safety, and well-being.

Selection Bias

A bias that occurs when the process of selecting participants for a study results in a sample that is not representative of the target population.

Snowball Sample

A non-probability sampling method where initial participants recruit additional participants from their own networks, often used for hard-to-reach populations.

In data- Each row is an 

• a person, state, country, etc.) horizontal

Each column contains

information about each observation across a set of items (variables) vertical

• Scalar:

• An object (number) with a single value

• Vector

• List of values (numbers)

• Concatenate

• Bring together

Is poli science, science?

The unity of all science consists alone in its method, not in its material. Politics uses the methods of rhetoric and advocacy to persuade people to adopt a certain point of view.

Political science uses the scientific method to uncover the true state of the world.

1. Form a hypothesis

2. Gather Data

3. Test the hypothesis

4. Reject or accept the hypothesis 

Frequency Distributions

– A table that shows the number of observations having each value of a variable.

– May include other statistics like the relative frequency proportion, percentage, missing values or odds ratios.

Descriptive Statistics

–Describing a large amount of data with just one number

Two classes of descriptive statistics

–Central tendency

–Dispersion

Measures of central tendency

–Describe the typical case in a data set or distribution

–Three statistics

•Mode

•Median

•Mean

Mode

–Indicates the most common observation

–Simply count the number of times you observe each value

–Mode is resistance to outliers

•By definition, the mode cannot be an outlier

•Describes only a single value in the data

Median

 Describes the middle value in an ordered set of values

– Important to rank order the observations first

– With an even number of observations, average the two middle value

– Resistant to outliers—by definition, median is not an outlier

– Only includes one value

Mean

–Describes the average value

– Mean is not resistant to outliers

• Outliers will pull the mean up or down, sometimes significantly

– Computed using all values

Three tests of reliability: 

Test-retest method: applying the same test to the same observations after a period of time and then comparing the results of the different measurements 

 

Alternative form method

• two different measures of the same concept administered to the same respondents at different times before the scores are compared.

–Split-halves method

• divide a multi-item measure into two measures with both of the new measures applied at the same time. 

Interitem association–

• similarity of outcomes of multiple measures of a concept to demonstrate the validity of the entire measurement scheme.

Experiments:

1. Sample 

2. Assign sample to treatment group and control group

3. Pre test

4. Intervention

5. Post-test

6. Conclusion

Type 1 error 

(false positive): Rejecting the null hypothesis when it's actually true

Type 2 error

(false negative): Accepting the null hyp when its actually false

Hypothesis template

[Independent variable] will have a [positive/negative/no] effect on [Dependent variable] in [

country/region/population], based on [theory, prior studies, or political context]

Simple random sample

Leaves the sample composition to chance. Selection can be driven by a lottery, a random number generator, or any other method that guarantees an equal chance of selection. *Most often used

Stratified sample

Drawn from a population that has been subdivided into two or more strata based on a single characteristic. Elements are selected from each strata in proportion to each strata’s representation in the entire population

Systematic sample

Generated by selecting elements from a list of the population at a predetermined interval. The start point for selection must be chosen at random or the list be randomized- or the list will not be representative. The population list must be random. Starting from the top and picking every 5th or so 

Cluster sample

Group elements for an initial sampling frameSamples drawn from increasingly narrow groups (countries, then cities, then blocks) until the final sample of elements is drawn from the smallest group (individuals living in each household)

Convenience sample

Non-random .Include elements that are easy or convenient for the investigator-like college students in samples collected on college campuses. Convenience because its convenient to gain information on participants

Snowball sample

non-random. relies on elements in the target population to identify other elements in the population for inclusion in the sample and is particularly useful when studying hard to locate or identify populations *networking approach to sampling. Very susceptible to bias

Purposive sample:

utilized when conducting a case study, in depth analysis of an issue where you are only focusing on two or three examples. Used to study diverse and limited number of observations. 

Most similar systems design 

Goal is to select cases where across independent variables there are similarities that matter. 

Null hypothesis testing typically proceeds in five steps:

1. Propose a research hypothesis (which implies a null hypothesis).

2. Set the significance level (usually .05).

3. Estimate relevant population parameters using sample data.

4. Calculate the confidence interval or P-value.

• 5. Reach a conclusion about the null hypothesis.