BCOR 3030 (MANAGEMENT) EXAM 1 QUESTIONS AND
ANSWERS WITH COMPLETE SOLUTIONS VERIFIED
LATEST UPDATE
Psychological safety
the degree to which people perceive their environment as conductive to taking
interpersonal risks
- will not be penalized or judged for mistakes, asking for help, or feedback
- feel accepted or respected
- comfortable to propose new ideas, give candid feedback, etc
- Example: "how others will respond if I put myself on the line?"
Management
How individuals keep large, complex organizations operating reliably and efficiently
Organization
A group of people working together towards a common goal
Organizational Behavior
The scientific study of how individuals and groups behave in organizations
Qualitative study
Non-Numerical data coded for detailed themes
- researcher does not attempt to alter participant or their behavior
- limited generalizability
,- Nothing is manipulated therefore, no causality
- example: interviews, archival analysis of newspaper articles
Correlational (Cross-Sectional)
Quantifies the relationship between two variables without manipulating them
- researcher does not attempt to alter participant or their behavior
- nothing is manipulated, therefore, no causality
- measures patterns in data, but not causal relationships
- identifies only whether two variables as associated, not why they are.
- Example: survey research, correlations
- Advantages: study the associations of naturally occurring variables that can't be
manipulated. High in external validity - real people doing real stuff. Revolution in "big
data" from online and tracked activity
- Disadvantages: correlation does not show causation
Experimental Research
Researcher systematically controls and manipulates events
- random assignment to different conditions (independent variable) rules out plausible
alternative explanations
- Independent variable (x) - manipulated variable
- Dependent Variable (y) -measured variable
- evidence of causal relatipnship
- Not necessarily "natural" behavior
- Example: laboratory studies, field experiments
Experimental Manipulations
, Keep everything the same except for the independent variable
- Example: The experiment where researchers sent in identical resumes EXCEPT for
the name and picture. Because of this, we can infer that any observed differences
between groups (DVs) were caused by the differences on the experimental condition
(IV)
Natural Experiments
Participants are assigned to experimental and control conditions not by the researcher,
but by some process that arguably resembles random assignment
- Example: comparing individuals with male vs female children (nature randomly
assigns child gender)
- Example: comparing individuals who live in the same area but under one vs. another
state's laws
Common data Traps
always think critically about the data conclusions are based on
- we need to compare data to something (example: in iceland 67% of people with covid
have been vaccinated. However, over 70% of Iceland is vaccinated. So the 67% is not
"high" at all)
Questions to ask about Data
- Unless there was a randomized experiment, be critical of causal claims (could
causality run both directions? What third variables might be present?)
- How were the variables measured? did they really measure what they claim? Where
did the sample come from?
- Whats the appropriate baseline? When you see numbers, ask "compared to what?"
ANSWERS WITH COMPLETE SOLUTIONS VERIFIED
LATEST UPDATE
Psychological safety
the degree to which people perceive their environment as conductive to taking
interpersonal risks
- will not be penalized or judged for mistakes, asking for help, or feedback
- feel accepted or respected
- comfortable to propose new ideas, give candid feedback, etc
- Example: "how others will respond if I put myself on the line?"
Management
How individuals keep large, complex organizations operating reliably and efficiently
Organization
A group of people working together towards a common goal
Organizational Behavior
The scientific study of how individuals and groups behave in organizations
Qualitative study
Non-Numerical data coded for detailed themes
- researcher does not attempt to alter participant or their behavior
- limited generalizability
,- Nothing is manipulated therefore, no causality
- example: interviews, archival analysis of newspaper articles
Correlational (Cross-Sectional)
Quantifies the relationship between two variables without manipulating them
- researcher does not attempt to alter participant or their behavior
- nothing is manipulated, therefore, no causality
- measures patterns in data, but not causal relationships
- identifies only whether two variables as associated, not why they are.
- Example: survey research, correlations
- Advantages: study the associations of naturally occurring variables that can't be
manipulated. High in external validity - real people doing real stuff. Revolution in "big
data" from online and tracked activity
- Disadvantages: correlation does not show causation
Experimental Research
Researcher systematically controls and manipulates events
- random assignment to different conditions (independent variable) rules out plausible
alternative explanations
- Independent variable (x) - manipulated variable
- Dependent Variable (y) -measured variable
- evidence of causal relatipnship
- Not necessarily "natural" behavior
- Example: laboratory studies, field experiments
Experimental Manipulations
, Keep everything the same except for the independent variable
- Example: The experiment where researchers sent in identical resumes EXCEPT for
the name and picture. Because of this, we can infer that any observed differences
between groups (DVs) were caused by the differences on the experimental condition
(IV)
Natural Experiments
Participants are assigned to experimental and control conditions not by the researcher,
but by some process that arguably resembles random assignment
- Example: comparing individuals with male vs female children (nature randomly
assigns child gender)
- Example: comparing individuals who live in the same area but under one vs. another
state's laws
Common data Traps
always think critically about the data conclusions are based on
- we need to compare data to something (example: in iceland 67% of people with covid
have been vaccinated. However, over 70% of Iceland is vaccinated. So the 67% is not
"high" at all)
Questions to ask about Data
- Unless there was a randomized experiment, be critical of causal claims (could
causality run both directions? What third variables might be present?)
- How were the variables measured? did they really measure what they claim? Where
did the sample come from?
- Whats the appropriate baseline? When you see numbers, ask "compared to what?"
