Analytical Aptitude Flashcards
(41 cards)
Aspects of analytical aptitude
- data advocacy
- data gathering
- data analysis
- evidence-based decision making
applying results of data analysis to make decisions
Evidence-Based Decision Making
6 Steps in Evidence-Based Decision Making
- Ask - translate problem into a question
- Acquire - gather info from varied sources
- Appraise - determine whether evidence is relevant, accurate, reliable, unbiased
- Aggregate - combine/organize data for analysis
- Apply - see/draw logical conclusions, develop solutions
- Assess - monitor and measure solution
How to become an HR data advocate
- develop a questioning mind
- build fluency in scientific literature of HR
- gather data on a continuous basis
- use evidence when communicating with stakeholders
- institutionalize the competency in the HR function
Objective measurements verified using statistical analysis
Quantitative Data
Subjective evaluation of actions, feelings, and behaviors
Qualitative Data
Biggest challenge when using interviews as a data source
Avoiding biases
Most important consideration when using a focus group
The participants are representative of the larger group - randomly selected
Begins discussion with core ideas, connecting and grouping similar ideas
Mind Mapping
Group categorizes data until relationships are drawn
Affinity Diagramming
Rounds of suggesting ideas, eliminating irrelevant or redundant, group agrees on importance of remaining items
Nominal Group Technique
Collect anonymous info from group, then group identifies strengths and weaknesses of ideas anonymously
Delphi Technique
Most important when using observation as a data source
the observer must be unseen
Most important when using artifacts as a data source
researcher must understand the principles of the culture
The ability of a data gathering tool to provide results that are consistent
Reliability of data
The ability of data gathering tool to measure what it is intended to
Validity of data
Used when data cannot be obtained from entire population
Statistical Sampling
What may result in data errors
Biases in sampling, selection, response, performance, and measurement
Process where incomplete sets, anomalies, errors, and gaps in data are identified and addressed
Data Cleansing
Measures of central tendency
mean, median, and mode
Median
middle value in a range of values
Mode
most frequently occurring value in a data set
Unweighted Mean
average - sum of all values divided by the number of values
Weighted Mean
When some data have more significance or effect - multiply individual values by a weighted factor
