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These flashcards cover essential vocabulary and concepts derived from lecture notes on research methods in quantitative studies.

Population

A group of subjects, variables, concepts, or phenomena.

Quantitative researchers don’t usually study a population because

1. They are too big

2. Too expensive

3. Always changing

When researchers examine a population→ census

It is wrong to say we don’t/can’t study a population

Sample Population

A subset of a population used for study.

Census

An official count or survey of a population, recording various details about individuals.

Sampling Error

The degree to which a sample differs from the population.

Representative Sample

A sample that accurately reflects the population from which it is drawn.

Factorial design

Multiple IVs, for example we have two seasonings of salt and pepper and want to know which combination is bestI’m

Parameters

Numeric characteristics of a population.

The research goal

Statistics

Numeric characteristics of samples.

Random Sampling

equal chance of being selected

Probability Sampling

know the amount and number we want to chose

Simple Random Sampling

Selecting individuals from a population randomly, such as drawing names from a hat.

Systematic Random Sampling

Selecting every nth individual from the population.

Stratified Random Sampling

Selecting samples to represent known proportions of different characteristics in the population.

Subsample

A smaller group selected from a larger sample. Ex. Sex, race, social economic status

Non-Probability Sampling

Sampling methods without random selection; not ideal but often necessary.

Convenience Sampling

Selecting individuals who are easiest to recruit for a study. AKA external validity

Purposive Sampling

Samples chosen based on specific characteristics of the population.

Snowball Sampling

Recruiting participants through referrals from initial subjects.

Descriptive Surveys

Surveys designed to document current conditions or attitudes.

Analytical surveys

to explain why situations exist between two or more variables → interrelationships

descriptive

Has one variables for example ex. I am into in politics

Advantages of Surveys

Realistic settings, cost-effective, and easy to collect large data sets.

Analytical

Has two or more variables for example ex. “I support the president”

Disadvantages of Surveys

Cannot establish causal relationships, low response rates, and surface-level information.

Question Relevance

The pertinence of each question in relation to the study and respondents.

Open-Ended Questions

Questions that allow respondents to generate their own answers.

Closed-Ended Questions

Questions that provide a list of answers for respondents to choose from.

Pilot Study

A small-scale study conducted to explore core ideas before the main research.

Challenges of Open-Ended Questions

Time-consuming analysis and content analysis on responses (coding) and potential for bizarre responses.

Advantages of Closed-Ended Questions

Uniform responses that are easy to quantify.

Disadvantages of Closed-Ended Questions

May fail to cover all important responses, leading to surface-level information.

Pitfalls in Questionnaire Construction

1. don’t use one more questions and one item, if it contains and/or it’s

2. Avoid ambiguous questions

3. Make it easy to understand

4. Don’t use highly detailed questions

5. Must keep question short

6. Avoid biased, terms or words

7. Avoid leading questions

8. Avoid sensitive, threatening questions

Good experiment

• Has pilot study

• Are always ethical (informed consent)

• should contain multiple checks

• Don’t over generalize

• Are not quasi experiments designs

Confounding Variables

to remove or hold constant the effects of the confounding variables = must make constant

Internal Validity

weather manipulated IVs causes the change in Dv

We study sample

Over population

External Validity

the degree to which experimental results may be generalized

Selection Bias

A threat to validity resulting from non-random sampling.

History threats to validities

Something happening outside of laboratory

Testing Effects threats to validities

multiple exposures

Instrumentation Changes threats to validities

Variability in results due to changes in the measuring instruments.

Act of Testing threats to validities

pretest sensitization

prior knowledge threats to validities

the subjects already know what will happen in an experiment

Experiments

We must make sure that the manipulation of the IVs actually causes changes in the DVs (cause and effect)

X Notation

manipulation

O notation

Observation measurement

R notation

Random assignment

Groups with X

Experiment group

Groups without X

Control group

Manipulation Check

Verifying whether the experimental manipulation had its intended effect.

Example research questions

How prevalent is violence on tv

How are broadcast messages accurate

How am magazine ads portrayed races

Quasi

Experiment designed, lacking randomization or control.

Content Analysis

to describe and to systematically, analyze the content of messages

Post-Test Only Control Group

An experimental design with measurement taken after the treatment.

The more we control confounding variables

The better the experiment

How to control variables

Hold them constant. example participants experience the same thing

Steps in content analysis

1. Generate a research question or hypothesis

2. Define population

3. Select coding units

4. Sample messages

5. Train coders

6. Code the content

7. analyze and interpret

True Experiment

1. variable manipulation

2. Control variables

3. Random assignments of subjects to condition

Reading experimental design

When reading experimental designs each line equals a group

Interpreting data

Can we generalize beyond the content of data? = NO

In-Person Surveys

Surveys conducted face-to-face with participants.

Mailing Surveys

Surveys sent by mail for respondents to complete.

Phone Surveys

Surveys conducted over the phone, often providing real-time feedback.

Online Surveys

Surveys conducted on the internet, allowing for a broad reach.

Generation Discrepancy

Differences in responses or behavior among different age groups.

Low Response Rate

A challenge in surveys where few participants respond.

Coding units

Categories used to count the communication forms

One group pretest - posttest

O. X. O

Pretest -posttest control group

R O1 X O2

R. O3 O4

Post test only control group

R X O1

R. O2

Solo four group

R O1 X O2

R O3 O4

R X O5

R. O6

Interaction effect

Changes in the DV are due to a combination of different levels of IVs

Things we should mention and explaining a factorial design

• Factors are manipulated by IV

• Factors have levels

• Interaction