Chapter 4 and 5: Research foundations and populations

Question 1

Indicator

Answer 1

means by which we assign individual cases to different values of the variables

Question 2

Nominal Variable

Answer 2

Cannot be ordered or ranked
No mathematical relationship between the variables
numerical value represents differences in kinds, not in degree

Question 3

Ordinal Variable

Answer 3

Organized into categories along a continuum
No precise distance between categories
Numbers represent relative positions

Question 4

Interval/Ratio Variable

Answer 4

Can be ordered and categories are separated by a standard unit
Ratio variables can have an absolute zero
the distance between variables is the same

Question 5

Which type of variable has the highest precision and which has the lowest?

Answer 5

Highest: interval/ratio
Lowest: Nominal

Question 6

Theory-building research

Answer 6

Inductive
Seek to obtain real-world observations in order to create an explainable theory

Question 7

Theory-testing research

Answer 7

Sets out to test the hypothesis established by a theory.

Question 8

Inductive reasoning

Answer 8

using empirical evidence to draw a conclusion - to help form the definition of a concept

Question 9

Operationalization

Answer 9

the process of moving from abstract concepts to concrete measurable variables

Question 10

Correlation

Answer 10

A state in which two entities change in conjunction with the other.

Demonstrating that two concepts are correlated and have a relationship.

Question 11

Causal mechanism

Answer 11

Explains which is the cause and effect relationship between variables.

A plausible explanation of why concepts are related

Question 12

What is the difference between a causal mechanism and a correlation?

Answer 12

A causal relationship means there is one relationship to another.

Correlation simply states does not explain

Question 13

What does a hypothesis identify? What is the opposite?

Answer 13

A hypothesis identifies a relationship between variables

A null hypothesis is the opposite of this. It means there is NO relationship between the variables.

Question 14

Types of correlation?

Answer 14

Positive correlation: Both variables (IV AND DV) move in the same direction, meaning if one increases, so does the other

Negative correlation: The variables move in an opposing direction. Meaning if the iV increases, the DV would decrease.

Question 15

Name examples for each type of variable

Answer 15

Nominal: Religious affiliation
Ordinal: Poor, fair, good, excellent
Interval: Temperature
Ratio: Income (absence of income)

Question 16

Continuum

Answer 16

Ordering the values of a concept based on a dimension of low to high, less to more

Question 17

What are the 5 criteria for having a strong causal argument?

Answer 17

Correlation

Temporal order: a shared pattern in the correlation. Time sequence of events.

Absence of confounding variables: eliminate any alternative explanation the relationship of variables can be accounted for with a third variable (spurious relationship)
Affects both IV and DV simultaneously

Plausible causal mechanism: Parosmonious (easy to explain) and direct

Consistency: replication of the study

Question 18

Bivariate vs Multivariate

Answer 18

Bivariate: contains two variables
Multivariate: Contains multiple independent variables

Question 19

Intervening variable

Answer 19

comes in between the IV and DV

Question 20

What is the difference between a confounding variable and a reinforcing variable?

Answer 20

Confounding affects both, while reinforcing only affect the dependent variable

Question 21

Categorical concepts vs continuous concepts

Answer 21

Categorical concepts: variations within the concept indicate differences of kind, and cases are categorized into groups according to descriptions

Continuous concepts: variations within the concept indicate differences in degree, and the concept’s characteristics are sequentially connected and categories are placed on a continuum.

Question 22

Quantitative research

Answer 22

Uses numeric information
uses both categorical and continuous

Question 23

Qualitative research

Answer 23

uses more textual information
whether a concept is categorical or continuous

Question 24

Measurement error

Answer 24

the difference between the true value and the value obtained through measurement observed through a quantity

Question 25

non-random vs random error

Answer 25

Random error: naturally occuring, no specific cause Non-random error: faults in the measures, error in a specific direction. Creates bias in the data

Question 26

Measure of validity and types

Answer 26

Whether the measurement accurately reflects the conceptual definition. Internal and external Internal validity: trustworthy assessment of causality, able to satisfy most if not all criteria of causality External validity: the findings of the case may be used to make generalizations beyond the original study

Question 27

Population

Answer 27

A group of people that researchers want to make a generalization about.

Question 28

What are the 3 factors to consider when choosing a population?

Answer 28

unit of analysis, population parameter, sample statistic

Question 29

Unit of analysis

Answer 29

focus group of the study. Defined. Geographic and temporal qualifications

Question 30

Population parameter

Answer 30

Score of each member of the population is measured in numeric form Resultant characteristic

Question 31

Sample statistic

Answer 31

Score of a sample measured in numeric form

Question 32

What is a sampling selection method and what are the two types

Answer 32

Manner by which cases in the population are selected for inclusion in the sample Can be divided into two categories: probability and non-probability Probability sampling techniques are based on probability theory and allow researchers to use statistics to test the representativeness of their sample. Commonly used in quantitative research Non-probability sampling: opt-in panel surveys. Participants make the decision to join the panel rather than being contacted through random selection and experimental research.

Question 33

Sample size

Answer 33

number of cases involved in the sample

Question 34

Probability samples and types

Answer 34

probability ranges from 0 to 1. 0 being no relationship and 1 being a 100% chance. Random selection: Makes generalizations and pulls conclusions Simple random sample: the process by which every case in the population is listed and the sample is selected randomly from this list Systemic random sample: A selection interval is calculated based on the sample size needed. Following this, a random number is used as the selection starting point. Stratified random sampling: Breaking the population into mutually exclusive subgroups, and then randomly sampling each group Cluster Sampling: Process of dividing the population into a number of subgroups, known as clusters, and then randomly selecting clusters within which to randomly sample.

Question 35

Non-probability samples and types

Answer 35

Accidental sample (convenience sample): researchers gather data from individuals they “accidentally” encounter or who are convenient Ex: “person on the street” Self-selection: respondents themselves select whether or not to be part of the sample Ex: social media polls Limited to those who opt in Unrepresentative of a larger population Purposive sampling: researcher selection of specific cases Uses his or her judgement to select cases that will provide the most information Used in comparative research Most similar systems design: similar characteristics Most different systems design Snowball sampling: researcher begins by identifying a few cases and then getting referrals to others and continues to branch out Potential for closed loops Quota sampling: research identify a number of target subgroups, and then sets a quota number that must be met by each group

Question 36

Design weights

Answer 36

To reconstruct a representative national sample, mathematical corrections to compensate for the fact that respondents probabilities of being selected were influenced by the research design,

Question 37

sampling distribution

Answer 37

Sampling distribution: all the possible sample means for a given sample size. Created by totalling the number of combinations that present the specified sample mean

Question 38

Confidence interval

Answer 38

Confidence interval: range of values within which the population parameter is likely to fall

Question 39

Confidence level

Answer 39

level of confidence that the population parameter will fall within the confidence interval

Question 40

What is the most typical level of confidence in social science?

Answer 40

95% confidence level

Question 41

Margin of error

Answer 41

amount if sampling error, expressed as a percentage - they are willing to accept. Used in conjunction with the sample statistic plus or minus the margin of error

Question 42

Sampling error

Answer 42

Sampling error: refers to the difference between the sample statistic and population parameter A large sampling error indicates that the sample statistic deviates greatly from the population parameter A small sampling error indicates the sample statistic is close to the population parameter

Question 43

Proportionate stratified random sampling vs disproportionate

Answer 43

Proportionate stratified random sampling: generate a final sample that reflected the proportion of each set of students in the overall population Disproportionate stratified random sampling: used if a particular group of interest is small. However, this may not reflect the general population anymore