SASB LEVEL II EXAM QUIDE
QUESTIONS AND ANSWERS
Recognizing Data Types Question - Answer- 1. Does the Metric yield Discrete Data?
2.Does the metric yield Continuous Data?
3.What is the possible range of performance on a topic?
Discrete Data - Answer- Data that can only take certain values. IE Number of Requests
or Number of data breaches.
Continuous data - Answer- Numerical data that is not discrete. Can include fractions,
infinitely divisible. IE: Percentages or GHG emission data
Normal distribution - Answer- A function that represents the distribution of variables as a
symmetrical bell-shaped graph.
Histogram - Answer- a bar graph depicting a frequency distribution
Normalization of Data - Answer- Use an activity metric or financial metric to normalize
data. Allows for easier comparison between companies. Makes it easier to compare a
company to industry benchmarks. Makes a dataset potentially normally distributed.
Measures of Central Tendency - Answer- Mean, Median, Mode. Mode is the highest
point on the histogram
Quantiles - Answer- Divisions of a probability distribution or frequency distribution into
equal, ordered subgroups. Can help depict company performance in top, average, and
laggards. Used in two ways:
1. To indicate data value at a given quantile: 90th percentile is 90 million.
2. Ranking of a given data value: Company's As performance was in the top quartile.
Types of Quantiles - Answer- Quartiles (4 quantiles)
Quintiles (5 quantiles)
Deciles(10 quantiles)
Percentile (100 quantiles)
Data Dispersion - Answer- the spread of values around a central tendency, such as the
mean or median
Measuring Data Dispersion - Answer- 1. Standard Deviation
2. Variance (standard Deviation Squared)
3. Mean absolute deviation (MAD), which is the average distance between each data
value and the mean
, Standard Deviation - Answer- a computed measure of how much scores vary around
the mean score. 68% , 95%, 99.7%
Outlier - Answer- A value much greater or much less than the others in a data set. Can
be understood potentially with normalization. Necessary to understand fundamental
reason for outlier.
Skewness - Answer- The extent to which cases are clustered more at one or the other
end of the distribution of a quantitative variable rather than in a symmetric pattern
around its center. If the left tail of a distribution chart is longer, the distribution is
negatively skewed; if the right side is longer, it is positively skewed
Cause of Skewness - Answer- Companies on the long-tailed end of a skewed
distribution may be outliers performing better or worse than peers, or they may have
unique issues that need to be taken into consideration to explain their over- or under-
performance. Important to understand
kurtosis distribution - Answer- A high kurtosis describes a chart with fat tails and even
distribution, while a low kurtosis refers to a chart with skinny tails and distribution
centered toward the mean. In mapping distribution on a SASB metric, high kurtosis
charts will have results clustered at the ends and evenly distributed across the rest of
the chart, while low kurtosis typically have results clustered in the middle with light
distribution at the ends.
Revenue and Skewness - Answer- As a result, a characteristic such as revenue could
impact the distribution of SASB-reported data within an industry. In one example, 80
percent of companies in an industry have sales ranging from $100 million to $10 billion,
while only a small handful of companies have revenue of $100 billion or more. In this
case, the weight of the distribution of revenue is aggregated on the left side, and there
is long right tail—the distribution is positively skewed
Regulatory Mandates and Skewness - Answer- Regulatory mandates are another
common reason for the asymmetrical distribution of sustainability performance data. In
many cases, regulations impose hard and soft caps on a company's allowable
performance.
Contextual Factor Categories to Consider - Answer- 1. Operating Context
2. Performance Context
Operating Context - Answer- Important factors related to a company's operating context
include such things as:
Business climate.
Economic climate
Societal trends.
Regulatory climate.
Geographic footprint
QUESTIONS AND ANSWERS
Recognizing Data Types Question - Answer- 1. Does the Metric yield Discrete Data?
2.Does the metric yield Continuous Data?
3.What is the possible range of performance on a topic?
Discrete Data - Answer- Data that can only take certain values. IE Number of Requests
or Number of data breaches.
Continuous data - Answer- Numerical data that is not discrete. Can include fractions,
infinitely divisible. IE: Percentages or GHG emission data
Normal distribution - Answer- A function that represents the distribution of variables as a
symmetrical bell-shaped graph.
Histogram - Answer- a bar graph depicting a frequency distribution
Normalization of Data - Answer- Use an activity metric or financial metric to normalize
data. Allows for easier comparison between companies. Makes it easier to compare a
company to industry benchmarks. Makes a dataset potentially normally distributed.
Measures of Central Tendency - Answer- Mean, Median, Mode. Mode is the highest
point on the histogram
Quantiles - Answer- Divisions of a probability distribution or frequency distribution into
equal, ordered subgroups. Can help depict company performance in top, average, and
laggards. Used in two ways:
1. To indicate data value at a given quantile: 90th percentile is 90 million.
2. Ranking of a given data value: Company's As performance was in the top quartile.
Types of Quantiles - Answer- Quartiles (4 quantiles)
Quintiles (5 quantiles)
Deciles(10 quantiles)
Percentile (100 quantiles)
Data Dispersion - Answer- the spread of values around a central tendency, such as the
mean or median
Measuring Data Dispersion - Answer- 1. Standard Deviation
2. Variance (standard Deviation Squared)
3. Mean absolute deviation (MAD), which is the average distance between each data
value and the mean
, Standard Deviation - Answer- a computed measure of how much scores vary around
the mean score. 68% , 95%, 99.7%
Outlier - Answer- A value much greater or much less than the others in a data set. Can
be understood potentially with normalization. Necessary to understand fundamental
reason for outlier.
Skewness - Answer- The extent to which cases are clustered more at one or the other
end of the distribution of a quantitative variable rather than in a symmetric pattern
around its center. If the left tail of a distribution chart is longer, the distribution is
negatively skewed; if the right side is longer, it is positively skewed
Cause of Skewness - Answer- Companies on the long-tailed end of a skewed
distribution may be outliers performing better or worse than peers, or they may have
unique issues that need to be taken into consideration to explain their over- or under-
performance. Important to understand
kurtosis distribution - Answer- A high kurtosis describes a chart with fat tails and even
distribution, while a low kurtosis refers to a chart with skinny tails and distribution
centered toward the mean. In mapping distribution on a SASB metric, high kurtosis
charts will have results clustered at the ends and evenly distributed across the rest of
the chart, while low kurtosis typically have results clustered in the middle with light
distribution at the ends.
Revenue and Skewness - Answer- As a result, a characteristic such as revenue could
impact the distribution of SASB-reported data within an industry. In one example, 80
percent of companies in an industry have sales ranging from $100 million to $10 billion,
while only a small handful of companies have revenue of $100 billion or more. In this
case, the weight of the distribution of revenue is aggregated on the left side, and there
is long right tail—the distribution is positively skewed
Regulatory Mandates and Skewness - Answer- Regulatory mandates are another
common reason for the asymmetrical distribution of sustainability performance data. In
many cases, regulations impose hard and soft caps on a company's allowable
performance.
Contextual Factor Categories to Consider - Answer- 1. Operating Context
2. Performance Context
Operating Context - Answer- Important factors related to a company's operating context
include such things as:
Business climate.
Economic climate
Societal trends.
Regulatory climate.
Geographic footprint
