Lecture Three

Question 1

Unit of Analysis:

Answer 1

In research, the group of people, things, or entities that is being investigated or studied. For example, in organizational contexts, data can be collected from students, who in turn are part of schools, which in turn are part of districts, which may have multiple sites in several counties.

Question 2

Operational Definition:

Answer 2

a description of something in terms of the operations (procedures, actions, or processes) by which it could be observed and measured. For example, the operational definition of anxiety could be in terms of a test score, withdrawal from a situation, or activation of the sympathetic nervous system. The process of creating an operational definition is known as operationalization.

Question 3

Variables

Answer 3

A variable is an inconsistent figure that changes over time, between groups, etc.

-Gender
-Personality traits
-Income in a population

Variables may be discreet, having a finite range, or continuous, with infinite possible values between each observation. Discrete variables may have two (dichotomous) or more (polytomous) possible values.

Question 4

independent variable

Answer 4

the variable that is manipulated by the researcher. This may be one or more treatment conditions manipulated prior to observing changes in the dependent variable.

Question 5

mediator variable

Answer 5

part of the process or mechanism by which an independent variable produces outcome on the dependent variable.

Question 6

moderator variable

Answer 6

influences the direction or strength of the relationship between the IV and the DV, but does not have a causal connection from the independent variable

Question 7

Scales of Measurement

Answer 7

The four scales of measurement describe which property or set of properties a phenomena adheres to, and thus determines what statistics we can use to describe and summarize the construct.

Nominal, ordinal, interval, ratio.
See table from slides

Question 8

Nominal

Answer 8

describes arbitrary labels used to identify individual categorizations (identity). Examples would include social security numbers, zip codes, or arbitrary numerical codes in research.

Examples:
- The number on an athlete’s uniform
- Your social security number
- Your Visa card number
- The city where you live

Question 9

Ordinal:

Answer 9

used to order a hierarchical series, such as rank order (first, second, third…), or percentiles. Each value is greater or less than another (magnitude), though no standard interval exists between values. For example, the difference between first and second place may not be the same as the difference between second and third.

• Order of finish in a race or -a contest
• Letter grades: A, B, C, D, or F
• Level of agreement (e.g., Likert scale): Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree

Question 10

Interval

Answer 10

contains an ordered series with equal intervals between values, but no absolute-zero point. Consider temperature in degrees Fahrenheit, a measure of heat. The difference between 10° and 15° is the same as between 90° and 95°, but a temperature of 0° does not indicate a complete absence of heat. IQ and SAT scores are interval scales.

Examples:
- Scores on the College Board’s Scholastic Aptitude Test, which measures a student’s scores on reading, writing, and math on a scale of 200 to 800
- Intelligence Quotient scores
- Dates on a calendar

Question 11

Ratio:

Answer 11

an ordered series with equal intervals between values and an absolute-zero or “starting” point. Distance from a starting point or temperature in degrees Kelvin are both ratio scales, as a score of zero indicates an absolute zero, or the complete absence of the construct being measured.

Examples:
-Height
-Income
-Distance travelled
-Time elapsed or time remaining

Question 12

Descriptive statistics

Answer 12

procedures used to summarize, organize, and simplify data, and may be used with either a sample or a population.

-Central tendency: mean, median, mode
-Variability: variance, standard deviation

Question 13

Inferential statistics

Answer 13

techniques that allow us to study a sample and then make generalizations about the population from which the sample is drawn. compares variables to each other and generalizes to the population

• T-test
• ANOVA
• Correlation

Question 14

Population

Answer 14

refers to the entire set of individuals of interest in the study.

Question 15

sample

Answer 15

the set of individuals selected to represent the population.

Sampling in the modern world
-In-person
-Phones and call or polling centers
-Web research: email, websites, consumer surveys, Amazon Mechanical Turk and apps

Question 16

probability sample

Answer 16

each member of the population has a CHANCE of being selected.

Question 17

simple random sample

Answer 17

each member of the population has an EQUAL chance of being selected.

Question 18

external validity

Answer 18

sometimes referred to as validity generalization.

Choosing a probability sample allows for generalizations to the population, thereby increasing external validity

Question 19

Probability samples:

Answer 19

• Simple random
• Cluster: groups of participants, usually location-based
• Stratified: proportional, random sample of homogeneous groups
• Systematic (every nth)
• Oversampling

Question 20

Nonprobability samples

Answer 20

better for direct exploration of topics in greater depth, qualitative studies

• Convenience
• Purposeful: individuals judged to be good sources of information
• Snowball: participants rec. other participants
• Self-selection

Question 21

Sample Size

Answer 21

• Large n increases precision; law of large numbers
• Can start with pilot study to check
• From a few hundred for a SBS article to 1,500 or more for national studies
• Minimum 100; 300+ good; 10% rule of thumb
• Depends on the number of variables, group heterogeneity and variance, and confidence
• Sample size calculators: Qualtrics

Question 22

Qualitative Samples

Answer 22

• Smaller, average approx. 20-50
• Studies are more reliant on researcher’s judgment than statistical analysis
• Not trying to generalize to population in many cases
• Saturation: when additional responses no longer add variability to the results being collected (i.e., no new themes, variables, or relationships emerge)

Question 23

Standardized vs Norm Referenced Scores

Answer 23

Standardized: everything is the same across the board

Norm Referenced: scores on a test are adjusted compared to the group

Question 24

IQ Score

Answer 24

Mean of 100, standard deviation of 15

Norm referenced

If someone gets an IQ score of 100 regardless of age it is spot on average.

Question 25

Law of Large Numbers

Answer 25

Outliers are less likely to skew data with a higher sample size and can help decrease systemic bias (but will not save you from it)

*check this definition plz