3.7.A/B/C/D Data & Analytics Flashcards Preview

CPTD Exam Spring 2021 > 3.7.A/B/C/D Data & Analytics > Flashcards

Flashcards in 3.7.A/B/C/D Data & Analytics Deck (32)
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1

True or False?

TD professionals should have an understanding of how data driven their organization is prior to planning projects and selecting or using data visualization techniques.

True

2

Five factors define a data-driven organization. Name 4 of the 5.

-a strong company culture
-an experimentation mindset and objectively learn from failures
-a digital technology influence
-a focus on the future
-are organizationally agile (Sinar 2018).

3.7.6.1 Presenting Data to Stakeholders

3

Which visualization techniques can be used to show distribution of a single variable?

columns, histogram, scatter chart, bar chart

3.7.6.2 What to Display

4

Which visualization techniques can be used to show relationship?

bubble charts, scatter chart

3.7.6.2 What to Display

5

Which visualization techniques can be used to show comparison?

bars and columns, timeline, line chart, scatter plots

3.7.6.2 What to Display

6

Which visualization techniques can be used to show distribution of multiple variables?

heat maps, bubble charts

3.7.6.2 What to Display

7

Which visualization techniques can be used to show connection?

relationship or connection maps, heat maps, Venn diagrams

3.7.6.2 What to Display

8

Which visualization techniques can be used to show composition of the whole?

pie chart, stacked bar chart

3.7.6.2 What to Display

9

Which visualization techniques can be used to show location?

maps, building diagrams, processes

3.7.6.2 What to Display

10

3.7.B Skill statement:

I. Developing a People Analytics Plan

TD professionals should be skilled in identifying stakeholder requirements so they can develop a people analytics plan.

3.7.3 Skill in Identifying Stakeholders' Needs, Goals, Requirements, Questions, and Objectives to Develop a Framework and/or Plan for Data Analysis

11

When working with a stakeholder, what is their desired purpose?

When working with a stakeholder, the purpose is what the stakeholder wants and needs to know—their goal, need, or requirement. This forms the framework for the data analysis plan.

3.7.3.1 The Stakeholder’s Desired Purpose

12

When conducting a stakeholder analysis, what are some the three ways that a TDP might segment the stakeholder group?

*hierarchy, such as team leads, department heads, or directors

*function or department, such as sales, marketing, or operations

*decision-making authority, which differs from hierarchy; for example, if there is a unique situation where the stakeholder group has responsibility across departments (Anand 2017).

3.7.3.2 Conduct Stakeholder Analysis

13

3.7.C Skill statement:

I. Analyzing Data and Interpreting Results

TD professionals should be skilled in analyzing results so they can identify trends and relationships among variables. They do this in two steps: analyzing data and interpreting what it means.

3.7.4 Skill in Analyzing and Interpreting Results of Data Analyses to Identify Patterns, Trends, and Relationships Among Variables

14

Once questions have been asked and the right data collected, TD professionals should use a deeper data analysis to identify useful information and initial conclusions. What types of data analysis makes sense as a good place to start?

* plotting it out to find correlations or creating a pivot table that allows data to be sorted and filtered using different variables.

*calculate the mean, maximum, minimum and standard deviation of the data.

3.7.4.1 Process for Data Analysis

15

There are 5 most common pitfalls of poor data analysis. What are they?

jumping to conclusions—or worse, starting with the conclusion

unconscious bias

overusing the mean and avoiding the mode and median

incorrectly defining the sample size

hypothesis testing without accounting for the Hawthorne effect or placebo effect.
[See 2.8.6.2]

3.7.4.2 Pitfalls of Initial Data Analysis

16

What is the Hawthorne effect?

The more visible the observation process, the less reliable the data are.

the alteration of behavior by the subjects of a study due to their awareness of being observed.

17

What is the placebo effect?

the placebo effect, describes an actual physiological or psychological response to an inert intervention (behavioral or pharmacological) regardless of observation

18

When making sense of quantitative and qualitative information, which data set is best to begin working with as a starting point?

Quantitative data

3.7.4.3 Interpret Results

19

True or False?

When working with numbers, TD professionals will want to turn most of their data into percentages.

True

When working with numbers, it is easier to make comparisons of percentages than whole numbers, so TD professionals will want to turn most of their data into percentages.

3.7.4.3 Interpret Results

20

When intepreting results, what is the best way to use qualitative data?

Qualitative data should be compiled after the numbers are quantified. This will be used to provide a rationale for the quantitative data message.

21

What is one way a TDP can provide context and meaning to their analysis?

benchmarks

Potential benchmarks include a comparison to the last survey, other organization’s data, or best practices.

3.7.4.3 Interpret Results

22

If a TDP wanted to make comparisons within the data, what technique would they use?

A) qualitative data

B) mean, median, and mode

C) cause-and-effect

D) cross-tabulation

D) cross-tabulation

Crosstab or cross-tabulation is a multidimensional table that records the frequency of respondents that have specific characteristics defined in each table cell. These tables show valuable data about the relationships of all the variables to one another and help to analyze cause-and-effect or complementary relationships. For example, the cross-tab table between a question about age and a question about professional development might lead to a conclusion that 20 percent of employees over age 50 want more professional development opportunities. [See 3.7.4.4]

3.7.4.3 Interpret Results

23

True or False?

As TD professionals interpret their analysis, they must remember that it is possible to prove a hypothesis true.

False

As TD professionals interpret their analysis, they must remember that it is not possible to prove a hypothesis true. Instead, it can only fail to reject the hypothesis. This means that no matter how much data is collected; chance could always interfere with the results.


3.7.4.3 Interpret Results

24

Although the numbers are important, listeners will want to know the story the data tells. TD professionals can create a story by:

(4 possible answers)

*using the percentages to create a narrative

*providing context with the statistics, such as comparing to a previous year

*showing which benchmarks were used for comparison when interpreting results

*including quotes from open ended questions or interviews, if possible, to help interpret numbers.

3.7.4.4 Using Data Visualization to Tell the Story

25

Two guiding principles for using crosstab tables to show a pictorial comparison of the results of two or more questions are scaling and integrity. What do scaling and integrity mean?

scaling shows proportions and relationships, while integrity focuses on the presentation's truthfulness and accuracy. [See 3.7.4.3]

3.7.4.4 Using Data Visualization to Tell the Story

26

Using analytics along a progressive spectrum, TD professionals can use four different analyses to measure the business value and effectiveness of various training programs and other initiatives. Name these four different analyses.

Descriptive analytics

Diagnostic analytics

Predictive analytics

Prescriptive analytics

3.7.4.5 The Analytic Spectrum

27

Describe descriptive analytics and what it is used for.

to explain what happened

Descriptive analytics. Use it to explain what happened. This is the analysis TD professionals are most familiar with, such as assessment scores, summary activities, opinions, satisfaction, and evaluation surveys.

3.7.4.5 The Analytic Spectrum

28

Describe diagnostic analytics and what it is used for.

to explain why something happened

Diagnostic analytics. Use it to explain why something happened using a variety of techniques, such as data mining and data discovery. It provides correlations for focusing on the reason something did or did not happen as expected. It can save time by knowing where to apply and concentrate next steps.

3.7.4.5 The Analytic Spectrum

29

Describe predictive analytics and what it is used for.

to predict what will happen in the future

Predictive analytics. Use both descriptive and diagnostic data to predict what will happen in the future. By having a sense of what the data has uncovered, TD professionals can take this information one step farther and build models that prescribe support to increase success.

3.7.4.5 The Analytic Spectrum

30

Describe prescriptive analytics and what it is used for.

to show how to make something happen

Prescriptive analytics. Use it to show how to make something happen. It offers the best opportunity to influence a different outcome. This is the least developed analytic because each organization has different requirements. TD professionals can use prescriptive analytics to personalize learning by matching a learner’s preferences to make something happen.

3.7.4.5 The Analytic Spectrum

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