PUP 424 Exam 1 Questions & Answers
2025/2026
Data - ANSWERS-facts about objects obtained through observations, experiments and
measurements
-to be considered factual, bias must minimized and the data is consistent (regardless of who
collects it)
Data vs. information - ANSWERS-Information is how we interpret data (an easy-to-understand
summary) while data is factual
data collection --> data aggregation --> data analysis --> information
Conceptually defined variables vs. operationalizing variables - ANSWERS-conceptually defined
variables: articulated words to facilitate understanding
-operationalizing variables: determined how conceptually defined variables will be measured
Primary vs. secondary data collection - ANSWERSprimary data collection: data collected from
experiments, observations, surveys, and interviews (FIRSTHAND)
secondary data collection: data that is collected from another source (e.g. the Census)
(SECONDHAND)
Qualitative data - ANSWERSdescriptive information (ex: "she regularly travels by car")
Quantitative data - ANSWERSnumeric information (discrete & continuous)
Discrete vs. continuous data - ANSWERSdiscrete: integers normally having to do with counts (ex:
she travels by car 4 times a week)
,continuous: she travels 13.4 miles by car per week
4 levels of measuring data variation - ANSWERS-nominal level: shows the descriptive difference
among variable values (NOMINAL=NAME)
-ordinal level: variable values are expressed in qualitative categories that can be ranked
(ORDINAL=ORDER)
-interval level: variable values are expressed numerically with meaningful distance between
values (INTERVAL=DISTANCE BETWEEN)
-ratio level: has a meaningful zero
***can convert higher levels to lower ones (e.g. ration to nominal) but not the reverse***
independent vs dependent variable - ANSWERS-independent: what is manipulated/does the
explaining
-dependent: variable being explained/observed
Types of research (experiments) - ANSWERS-True experiment: classic five-step experiment
(hypothesis--> variable assignment --> measurement of dependent variable --> intro. of ind.
variable --> second measurement of dep. variable)
-Natural experiment: experiments going on around us that are not conducted but are simply
evaluated (e.g. policy analysis)
-Naturalistic experiments: attempt to collect data under natural conditions in the field (e.g.
sitting at a green light to see how quickly you're honked at)
Rule on unit of analysis selection - ANSWERS- always collect data on the smallest-level unit of
analysis as possible since you can scale up but not vice versa
, Ecological fallacy - ANSWERS-ecological fallacy: drawing conclusions about an individual based
on group data
instrument related errors in research:
-instrument validity (i.e. appropriateness of instrument to measure)
-reliability (i.e. getting the same answer consistently)
-precision (decimal points)
-accuracy (whether or not the instrument is measuring correctly)
3 questions to ask when analyzing cause and effect (after validity of experiment is established) -
ANSWERS-are the variables correlated, even weakly? (if yes, there's a possibility of a cause and
effect relationship)
-is the correlation spurious (i.e. the result of another variable?) (if yes, then no possibility of
cause and effect relationship)
-is there a logical sequence (i.e. cause precedes effect)?
-if yes to all of these, there is a potential cause-effect relationship
universe, population & sample - ANSWERS-universe: total potential body of data
-population: relevant part of universe whose parameters you want to estimate
-sample: part of population selected for data analysis (better for larger populations)
Probabilistic sampling vs. non-probabilistic sampling - ANSWERS-probabilistic sampling:
unbiased, random drawing taken from a population with an equal chance of being chosen (can
infer information about the population from that sampling)
***reduce prob. sample error by increasing the size of the sample***
-non-probabilistic sampling: can't infer information about overall population
2025/2026
Data - ANSWERS-facts about objects obtained through observations, experiments and
measurements
-to be considered factual, bias must minimized and the data is consistent (regardless of who
collects it)
Data vs. information - ANSWERS-Information is how we interpret data (an easy-to-understand
summary) while data is factual
data collection --> data aggregation --> data analysis --> information
Conceptually defined variables vs. operationalizing variables - ANSWERS-conceptually defined
variables: articulated words to facilitate understanding
-operationalizing variables: determined how conceptually defined variables will be measured
Primary vs. secondary data collection - ANSWERSprimary data collection: data collected from
experiments, observations, surveys, and interviews (FIRSTHAND)
secondary data collection: data that is collected from another source (e.g. the Census)
(SECONDHAND)
Qualitative data - ANSWERSdescriptive information (ex: "she regularly travels by car")
Quantitative data - ANSWERSnumeric information (discrete & continuous)
Discrete vs. continuous data - ANSWERSdiscrete: integers normally having to do with counts (ex:
she travels by car 4 times a week)
,continuous: she travels 13.4 miles by car per week
4 levels of measuring data variation - ANSWERS-nominal level: shows the descriptive difference
among variable values (NOMINAL=NAME)
-ordinal level: variable values are expressed in qualitative categories that can be ranked
(ORDINAL=ORDER)
-interval level: variable values are expressed numerically with meaningful distance between
values (INTERVAL=DISTANCE BETWEEN)
-ratio level: has a meaningful zero
***can convert higher levels to lower ones (e.g. ration to nominal) but not the reverse***
independent vs dependent variable - ANSWERS-independent: what is manipulated/does the
explaining
-dependent: variable being explained/observed
Types of research (experiments) - ANSWERS-True experiment: classic five-step experiment
(hypothesis--> variable assignment --> measurement of dependent variable --> intro. of ind.
variable --> second measurement of dep. variable)
-Natural experiment: experiments going on around us that are not conducted but are simply
evaluated (e.g. policy analysis)
-Naturalistic experiments: attempt to collect data under natural conditions in the field (e.g.
sitting at a green light to see how quickly you're honked at)
Rule on unit of analysis selection - ANSWERS- always collect data on the smallest-level unit of
analysis as possible since you can scale up but not vice versa
, Ecological fallacy - ANSWERS-ecological fallacy: drawing conclusions about an individual based
on group data
instrument related errors in research:
-instrument validity (i.e. appropriateness of instrument to measure)
-reliability (i.e. getting the same answer consistently)
-precision (decimal points)
-accuracy (whether or not the instrument is measuring correctly)
3 questions to ask when analyzing cause and effect (after validity of experiment is established) -
ANSWERS-are the variables correlated, even weakly? (if yes, there's a possibility of a cause and
effect relationship)
-is the correlation spurious (i.e. the result of another variable?) (if yes, then no possibility of
cause and effect relationship)
-is there a logical sequence (i.e. cause precedes effect)?
-if yes to all of these, there is a potential cause-effect relationship
universe, population & sample - ANSWERS-universe: total potential body of data
-population: relevant part of universe whose parameters you want to estimate
-sample: part of population selected for data analysis (better for larger populations)
Probabilistic sampling vs. non-probabilistic sampling - ANSWERS-probabilistic sampling:
unbiased, random drawing taken from a population with an equal chance of being chosen (can
infer information about the population from that sampling)
***reduce prob. sample error by increasing the size of the sample***
-non-probabilistic sampling: can't infer information about overall population
