Foundations of Quant Analysis. Flashcards
Routine Decisions
Repetitive and follow established procedures or guidelines. They are are made in response to regular, predictable situations. These decisions do not require rigorous analysis. Example: Scheduling employee shifts.
Non-routine decisions.
More complicated than routine decisions. Decision makers lack critical information. These decisions require data gathering and careful evaluation of multiple decision options, and involvement of relevant stakeholders in the process. They are made in response of to situations that are somewhat unfamiliar but still occur occasionally. Example: Handling workplace conflict.
Novel decisions
These are taken in response to unprecedented challenges or situations. Critical information required by the decision maker does not exist because historical evidence is irrelevant. For decisions made under truly novel conditions, evidence must be generated by action, learning and experimentation. Example: Adapting to remote work during a global pandemic.
Judgement based decision making.
Relies on the personal expertise, experience, and intuition of an individual or a group of decision-makers. In this approach, decisions are made based on what the decision makers believe is the best course of action, often drawing from their knowledge and past experiences.
-Subjective
-Limited data
- Rapid decision making
Evidence based decision making.
Relies on empirical evidence, data, and research findings to inform and guide the decision making process. Sources empirical evidence relevant to the problem to be solved and organizational data (e.g., company records). This approach involves systematically gathering relevant information, analyzing it objectively, and using the findings to make informed decisions.
-Objective
-Rigorous analysis
-Informed decision making
Evidence-based practice (EBP)
and reliability of the evidence and its applicability to the situation in question.
Anecdotal data/ anecdotal evidence
refers to the information that are based on personal experiences, individual accounts, or isolated examples. It is often collected through informal means , such as personal stories, anecdotes, or individual testimonials. It lacks the systematic and rigorous approach of formal research methods, and it does not involve structured data collection or statistical analysis.
Evidence
the outcome of analyzing and interpreting data to draw meaningful conclusions. It is the result of critically evaluating data to support a particular hypothesis.
Data
Refers to the raw information collected in research, while evidence is the result of analyzing and interpreting that raw data to draw meaningful conclusions. Data provides the basis of generating evidence, and evidence serves as the support for making informed decisions or drawing valid conclusions.
Nominal level of measurement
Is used for categorical variables and involves assigning scores that are category labels. Category labels communicate whether any two individuals are the same or different in terms of the variable being measured.
Ordinal level of measurement.
Involves assigning scores so they represent the rank order of the individuals. Ranks communicate not only whether any two individuals are the same or different in terms of the variable being measured but also whether one individual is higher or lower on that variable.
E.g., a researcher wishing to measure consumers’ satisfaction with their microwave ovens. They might be asked to specify their feelings as either very satisfied, somewhat satisfied.
*Ordinal scales fail to capture important information that will be present in the other levels of measurement we examine. In particular the difference between two levels of an ordinal scale cannot be assumed to be the same as the difference between two other levels.
The interval level of measurement.
Involves assigning scores using numerical scales in which intervals have the same interpretation throughout. Consider either fahrenheit or celsius temperature scales. The difference between 30 degrees and 40degrees represent the same temperature difference between 80 and 90 degrees. This is because each 10 degree interval has the same physical meaning.
*They do not have a true zero point, even if one of the scales values happens to carry the name zero,
The ratio level of measurement
involves assigning scores in such a way that there is a true zero point that represents the complete absence of the quantity. Height measured in meters and weight measured in kg’s are good examples. So are the counts of discrete objects or events such as the number of siblings one has or the number of questions a student answer correctly in an exam.
*Think of a ratio scale as the three earlier scales rolled up in one. Like a nominal scale it provides a name or category for each object. (numbers serve as labels). Like an ordinal scale, the object are ordered in terms of the numbers. Like an interval scale, the same difference at two places on the scale has the same meaning. However, the same ratio at two places on the scale also carries the same meaning.
Kurtosis
Measures the shape of the distribution of data points in a dataset/ Measure of tailedness of a distribution. It provides information about the tails and the concentration of data values around the mean of the data set.
Mesokurtic
This is the normal distribution, which has a kurtosis of 0. A kurtosis is neither too high nor too low. It has a value of approx 3 or an excess value near 0.
