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SHRM CP > Critical Evaluation > Flashcards

Critical Evaluation Flashcards

1
Q

Statistical method that examines data from different points in time to determine if a variance is an isolated event or if it is part of a longer trend.

A

Trend analysis

  • Trend analysis describes patterns in the past and projects future conditions based on those patterns.*
  • For example, HR can see when declines in certain types of degrees will become problematic for the organization and require a strategic response.*
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2
Q

Value that occurs most frequently in a set of data.

A

Mode

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3
Q

Technique in which participants each suggest ideas through a series of rounds and then discuss the items, eliminate redundancies and irrelevancies, and agree on the importance of the remaining items.

A

Nominal group technique (NGT)

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4
Q

Average of data that adds factors to reflect the importance of different values.

A

Weighted mean

  • useful in situations where there are significant outliers in the spread of data*
  • Example: HR could use this in assesing job candidates when some req. like certain degrees or credentials are considered to be more important than others*
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5
Q

Small group of invited persons (typically six to twelve) who actively participate in a structured discussion, led by a facilitator, for the purpose of eliciting their input.

A

Focus group

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6
Q

Raw average of data that gives equal weight to all values, with no regard for other factors.

A

Unweighted mean

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7
Q

Type of analysis that starts with a result and then works backward to identify fundamental cause.

A

Root-cause analysis

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8
Q

Comparing the sizes of two variables to produce an index or percentage; commonly used to analyze financial statements.

A

Ratio analysis

Net profit margin, for example, is a ratio that compares net revenue with costs. Many commonly used HR metrics are ratios, such as the turnover rate (comparing the number of terminations or resignations in a time period with the average number of employees in that period).

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9
Q

Average score or value.

A

Mean

Mean is synonymous with average

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10
Q

Middle value in a range of values.

A

Median (50th percentile, middle value in a range of values)

Preferred measure of central tendency

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11
Q

Statistical method used to test the possible effects of altering the details of a strategy to see if the likely outcome can be improved.

A

Scenario/what-if analysis

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12
Q

Distance of any data point from the center of a distribution when data is distributed in a “normal” or expected pattern.

Often shown as a “bell curve”

A

Standard deviation

68% of data lies within one standard deviation

Example of it’s use on page 279 or Frequency Distributions

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13
Q

Data-sorting technique in which group members add related ideas and indicate logical connections, eventually grouping similar ideas.

A

Mind mapping

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14
Q

Statistical method for identifying the degree of difference between planned and actual performance or outcomes.

A

Variance analysis

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15
Q

Technique that progressively collects information from a group of anonymous respondents.

A

Delphi technique

members work as a group without ever meeting

prevents “group think”

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16
Q

Data-sorting technique in which a group categorizes and subcategorizes data until relationships are clearly drawn.

A

Affinity diagramming

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17
Q

Statistical method used to determine whether a relationship exists between variables and the strength of the relationship.

A

Regression analysis

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18
Q

EBDM

A

evidence-based decision making

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19
Q

Steps in Evidence-Based Decision Making

A

Ask ~ when faced w/a problem, translate the situation into a Q that can be answered through info gathering.

Aquire ~ Gather info from varied sources

Appraise ~ Is the evidence relevant, valid, reliable, accurate, complete, and unbiased?

Aggregate ~ Combine + organize data for analysis

Apply ~ Use data to draw conclusions, develop solutions, win support for and take action

Assess ~ Monitor the solution

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20
Q

Consists of objective measurements that can be verified and used in statistical analysis—for example, the number of employees in an organization, the number of female employees in the organization, or the average number of hires each quarter.

A

Quantitative data

21
Q

Involves a subjective evaluation of actions, feelings, or behaviors.

A

Qualitative data

22
Q

Graphically depicts as portions or slices of a circle the constituents that comprise 100% of a data group. Textual data information can be included in callouts or in an attached table for more precise communication.

A

Pie chart

23
Q

Graphically depicts the sorting of data into groups arranged in the shape of a statistical distribution, showing a central tendency and dispersion around that tendency. This appears as columns of varying heights or lengths. Can include a comparative referent, such as a target or range of values. They can also be designed to show comparisons over time (usually through multiple columns for each category).

A

Histogram

24
Q

Plots data points on two axes. The horizontal axis usually represents time, while the vertical axis represents volume.

A

Trend diagram

25
Q

Ranks categories of data, 80% of problems are caused by 20% of causes

A

Pareto Chart

26
Q

Plots data points against two variables that form the chart’s x and y axes. Each axis is scaled. The pattern formed by the plotted data describes the correlation between the two variables:

A

Scatter diagram

  • A scatter diagram shows possible relationships between two variables.*
  • For example, if an HR professional wants to find out if there is a relationship between years of education and amount of income, he or she could create a scatter diagram with the years of education placed on one axis and the amount of income on the other.*
27
Q

A sample may not represent the general population. For example, HR is studying the effects of an engagement strategy on retention. The sample, however, contains a higher proportion of older workers than are in the organization’s workforce.

A

Sampling bias

28
Q

When participants are not randomly assigned to control and experimental groups. (Controlled studies assign participants to a control group that does not experience the intervention or condition being tested and one or more experimental groups that do experience the intervention or condition.)

Bias can also occur when researchers choose to enroll only certain types of participants. In a study testing the effects of a remote working policy, for example, the researchers enroll only employees who have been rated highly by their supervisors.

A

Selection bias

29
Q

This is the inverse of selection bias. The researchers invite a representative sample to join a study, but the group that accepts or responds is not representative. For example, HR invites all employees to participate in a survey to determine new benefits. The group that responds is composed disproportionately of young parents.

A

Response bias

30
Q

Participants in a controlled study behave differently because they are being studied. A famous example of this is the Hawthorne Works experiment. The experiment was designed to measure the effect of improvement in various factory conditions on worker productivity, but the increases in productivity were only temporary and appeared to be related primarily to the experiment’s design. The workers appreciated the increased attention to their welfare.

A

Performance bias

31
Q

Raters are measuring incorrectly, either unintentionally (because of lack of training or difficult measurement procedures) or intentionally (the result of some type of personal bias).

A

Measurement bias

32
Q

How to become an HR data advocate

A

1) Develop a questioning mind (why is X happening?)
2) Build fluency in the scientific literature for HR (regularly scan resources to ID new and reliable sources of date)
3) Gather data on a continuious basis (efficacy + efficiency of legacy systems, shareholder interests)
4) Use evidence when communicating w/stakeholders (include data to support recommendations)
5) Institutionalize the competency in the HR function (est. journal study groups, talk groups)

33
Q

6 Q’s to ask when assessing validity of data sources

A

Does the source have authority?

What are the source’s possible biases?

Are the sources for data used in a publication clearly cited?

Are the facts relevant?

Is the data current?

If the data is being offered as proof of an argument, is the argument itself sound?

34
Q

Name some examples of data sources

A
35
Q

Effective interviewing tips for individuals

A

Convenient time + location

Reasonable interview length of time

Confidence respected

Neutral and non-judgemental reactions

Start with safe questions to build rapport

36
Q

Focus group tools

A

Mind-mapping

Affinty diagramming

Nominal group technique

Delphi technique

37
Q

Ways to conduct more effective focus groups

A

Free from distractions + interruptions

Refreshments + breaks

Planned objectives clearly defined

Context that a focus group would occur

Good facilitator

Recorder/note taker

38
Q

Refers to the collection, organization and analysis of large amounts of numerical data

A

Statistics

39
Q

The process of sorting data in different ways to provide a more accurate and in-depth understanding of what the data is showing

A

Descriptive statistics

40
Q

Quartiles and percentiles

A

Quartiles divide a data set into quarters (for example: Q1, Q2, Q3, Q4)

A percentile indicates the proportion of the dataset at a certain percentage

41
Q

Name data analysis approaches

A

Variance, ratio, trend, regression, root-cause and scenario

42
Q

Name the various graphic analysis tools used to support and visualize the results of an analysis

A

Pie chart, histogram, trend diagram, pareto chart and scatter diagram

43
Q

Interview advantages and challenges

A
44
Q

Survey/questionnaire advantages and challenges

A
45
Q

Observation advantages and challenges

A
46
Q

Existing data advantages and challenges

A
47
Q

Artifacts advantages and challenges

A
48
Q

Reliabilty + Validity definitions and examples

A
49
Q

Data analysis methods

A

SHRM CP (26 decks)

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