10-ChapterError.Rmd

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Data Quality and Inference Errors {#chap:errors}
====================

**Paul P. Biemer**


This chapter deals with inference and the errors associated with big
data. Social scientists know only too well the cost associated with bad
data---we highlighted both the classic *Literary Digest* example and the
more recent Google Flu Trends problems in
Chapter [Introduction](#chap:intro). Although the consequences are well understood,
the new types of data are so large and complex that their properties
often cannot be studied in traditional ways. In addition, the data
generating function is such that the data are often selective,
incomplete, and erroneous. Without proper data hygiene, the errors can
quickly compound. This chapter provides, for the first time, a
systematic way to think about the error framework in a big data setting.

Introduction {#sec:10-1}
------------

The Machine Learning chapter and the Bias and Fairness chapter discuss how analysis errors can lead to bad inferences and suboptimal decision making. In fact the whole workflow we depicted in chapter [Introduction](#chap:intro) -- and the decisions made along the way -- can contribute to errors. In this chapter, we will focus on frameworks that help to detect  errors in our data, highlight in general how errors can lead to incorrect inferences, and discuss some strategies to mititage the inference risk from errors.

The massive amounts of high-dimensional and unstructured data that have recently become available to social scientists, such as data from social media platforms and micro-data from administrative data sources, bring both new opportunities and new challenges. Many of the problems with these types of data are well known (see, for example, the AAPOR report by Japec et al. [@japec2015big]): this data often has selection bias, is incomplete, and erroneous. As it is processed and analyzed, new errors can be introduced in downstream operations.

These new sources of data are typically aggregated from disparate sources at various
points in time and integrated to form data sets for further analysis. The processing 
pipeline involve linking records together, transforming them to form new attributes (or
variables), documenting the actions taken (although sometimes
inadequately), and interpreting the newly created features of the data.
These activities may introduce new errors into the data set: errors that
may be either *variable* (i.e., errors that create random noise
resulting in poor reliability) or *systematic* (i.e., errors that tend
to be directional, thus exacerbating biases). Using these new sources of data in
statistically valid ways is increasingly challenging in this
environment; however, it is important for social scientists to be aware of
the error risks and the potential effects of these errors on
inferences and decision-making. The massiveness, high dimensionality,
and accelerating pace of data, combined with the risks of variable
and systematic data errors, requires new, robust approaches to data
analysis.

The core issue that is often the cause of these errors is that such data may not
be generated from instruments and methods designed to produce valid and
reliable data for scientific analysis and discovery. Rather, this is
data that are being repurposed for uses not originally intended. It has been referred 
to as "found" data or "data exhaust" because it is generated for purposes that often 
do not align with those of the data analyst. In addition to inadvertent errors, there 
are also errors from mischief in the data generation process; for example, automated 
systems have been written to generate bogus content in the social media that is
indistinguishable from legitimate or authentic data. Social scientists using this 
data must be keenly aware of these limitations and should take the necessary steps to
understand and hopefully mitigate the effects of hidden errors on their
results.

The total error paradigm {#sec:10-2}
------------------------

We now provide a framework for describing, mitigating, and interpreting
the errors in essentially any data set, be it structured or
unstructured, massive or small, static or dynamic. This framework has
been referred to as the total error framework or paradigm. We begin by
reviewing the traditional paradigm, acknowledging its limitations for
truly large and diverse data sets, and we suggest how this framework can 
be extended to encompass the new error structures described above.

### The traditional model {#sec:10-2.1}

Dealing with the risks that errors introduce in big data analysis can be
facilitated through a better understanding of the sources and nature of
those errors. Such knowledge is gained through in-depth understanding of
the data generating mechanism, the data processing/transformation
infrastructure, and the approaches used to create a specific data set or
the estimates derived from it. For survey data, this knowledge is
embodied in the well-known *total survey error* (TSE) framework that
identifies all the major sources of error contributing to data validity
and estimator accuracy
[@groves2004survey; @biemer2003; @biemer2010total]. The TSE framework
attempts to describe the nature of the error sources and what they may
suggest about how the errors could affect inference. The framework
parses the total error into bias and variance components that, in turn,
may be further subdivided into subcomponents that map the specific types
of errors to unique components of the total mean squared error. It
should be noted that, while our discussion on issues regarding inference
has quantitative analyses in mind, some of the issues discussed here are
also of interest to more qualitative uses of big data.

For surveys, the TSE framework provides useful insights regarding how data generating, reformatting, and file preparation processes affect estimation and inference, and suggest methods for either reducing the errors at their source or adjusting for their effects in the final products to produce inferences of higher quality. (Add classic TSE citations)

The traditional TSE framework is quite general in that it can be applied
to essentially any data set that conform to the format in Figure
\@ref(fig:fig10-1). However, in most practical situations it is 
quite limited because it makes no
attempt to describe how the processes that the data may have contributed
to what could be construed as data errors. In some cases, these
processes constitute a "black box," and the best approach is to attempt
to evaluate the quality of the end product. For survey data, the TSE
framework provides a fairly complete description of the error-generating
processes for survey data and survey frames [@biemer2010total]. 
But at this writing, little effort has been devoted to
enumerating the error sources, the error generating processes for big data.
and the effect of these errors on some common methods for data analysis.
Some related articles include three recent papers that discuss the some of the
issues associated with integrating multiple data sets for official statistics,
including the effects of integration on data uncertainty 
[see @Holmberg2017; @Reid2017; and @Zhang2012]. There has also been some 
effort to describe these processes for population registers and administrative
data [@wallgren2007register]. In addition, Hseih and Murphy [-@Hsieh2017]
develop an error model expressly for Twitter data.


#### Types of Errors

Many administrative data sets have a simple tabular structure, as do
survey sampling frames, population registers, and accounting
Spreadsheets. Figure \@ref(fig:fig10-1) is a representation of tabular 
data as an array consisting of rows
(records) and columns (variables), with their size denoted by $N$ and
$p$, respectively. The rows typically represent units or elements of our
target population, the columns represent characteristics, variables (or
features) of the row elements, and the cells correspond to values of the
column features for elements on the rows.

```{r fig10-1, out.width = '70%', fig.align = 'center', echo = FALSE, fig.cap = 'A typical rectangular data file format'}
knitr::include_graphics("ChapterError/figures/fig10-1.png")
```

The total error for this data set may be expressed by the following
heuristic formula:
$$\text{Total error } =\text{ Row error } + \text{ Column error }
+ \text{ Cell error}.$$

**Row error**

For the situations considered in this chapter, the row errors may be of
three types:

-   **Omissions**: Some rows are missing, which implies that elements in the target
    population are not represented on the file.

-   **Duplications**: Some population elements occupy more than one row.

-   **Erroneous inclusions**: Some rows contain elements or entities that are not part of the
    target population.

Omissions:

For survey sample data sets, omissions include members of the target
population that are either inadvertently or deliberately absent from the
frame, as well as nonsampled frame members. For other types of data, the
selectivity of the capture mechanism is a common cause of omissions. For
example, a data set consisting of people who did a Google search
in the past week can be used to make inferences about that specific population 
but if our goal was to make inferences about the larger population of internet 
users, this data set will exclude people who did not use Google Search. This 
selection bias can lead to inference errors if the people who did not use 
Google Search were different from those who did.

Such exclusions can therefore be viewed as a source of selectivity bias if 
inference is to be made about an even larger set of people, such as the 
general population. For one, persons who do not have access to the Internet 
are excluded from the data set. These exclusions may be biasing in that 
persons with Internet access may have quite different demographic characteristics 
from persons who do not have Internet access [@dutwinbuskirk2017]. The 
selectivity of big data capture is similar to frame noncoverage in survey 
sampling and can bias inferences when researchers fail to consider it and 
compensate for it in their analyses.

---

**Example: Google searches**

As an example, in the United States, the word "Jewish" is included in
3.2 times more Google searches than "Mormon" [@SDV2015]. This does not
mean that the Jewish population is 3.2 times larger than the Mormon
population. Other possible explanations could that Jewish people use the
Internet in higher proportions, have more questions that require using
the word "Jewish", or there could be more searches for "Jewish food" food 
than "Mormon food." Thus Google search data are more useful for relative 
comparisons than for estimating absolute levels.

---

A well-known formula in the survey literature provides a useful
expression for the so-called *coverage bias* in the mean of some
variable, $V$. Denote the mean by $\bar{V}$, and let $\bar{V}_T$ denote
the (possibly hypothetical because it may not be observable) mean of the
target population of $N_{T}$ elements, including the $N_{T}-N$ elements
that are missing from the observed data set. Then the bias due to this
*noncoverage* is $B_{NC} = \bar{V} - \bar{V}_T  = (1 -
N / N_T )(\bar{V}_C - \bar{V}_{NC})$, where $\bar{V}_C$ is the mean of
the *covered* elements (i.e., the elements in the observed data set) and
$\bar{V}_{NC}$ is the mean of the $N_{T}-N$ *noncovered* elements. Thus
we see that, to the extent that the difference between the covered and
noncovered elements is large or the fraction of missing elements
$(1 - N /
N_T)$ is large, the bias in the descriptive statistic will also be
large. As in survey research, often we can only speculate about the
sizes of these two components of bias. Nevertheless, speculation is
useful for understanding and interpreting the results of data analysis
and cautioning ourselves regarding the risks of false inference.

Duplication:

We can also expect that big data sets, such as a data set containing
Google searches during the previous week, could have the same person
represented many times. People who conducted many searches during the
data capture period would be disproportionately represented relative to
those who conducted fewer searchers. If the rows of the data set
correspond to tweets in a Twitter feed, duplication can arise when the
same tweet is retweeted or when some persons are quite active in
tweeting while others lurk and tweet much less frequently. Whether such
duplications should be regarded as "errors" depends upon the goals of
the analysis.

For example, if inference is to be made to a population of persons,
persons who tweet multiple times on a topic would be overrepresented. If
inference is to be made to the population of tweets, including retweets,
then such duplication does not bias inference. This is also common in 
domains such as healthcare or human services where certain people have 
more interactions with the systems (medical appointments, consumption of 
social services, etc.) and can be over-represented when doing analysis 
at an individual interaction level.

When it is a problem, it still may not be possible to identify
duplications in the data. Failing to account for them could generate
duplication biases in the analysis. If these unwanted duplications can
be identified, they can be removed from the data file (i.e.,
deduplication). Alternatively, if a certain number of rows, say $d$,
correspond to the same population unit, those row values can be weighted
by $1/d$ to correct the estimates for the duplications.

Erroneous inclusions:

Erroneous inclusions can also create biases. For example, Google
searches or tweets may not be generated by a person but rather by a
computer either maliciously or as part of an information-gathering or
publicity-generating routine. Likewise, some rows may not satisfy the
criteria for inclusion in an analysis---for example, an analysis by age
or gender includes some row elements not satisfying the criteria. If the
criteria can be applied accurately, the rows violating the criteria can
be excluded prior to analysis. However, with big data, some out-of-scope
elements may still be included as a result of missing or erroneous
information, and these inclusions will bias inference.

**Column error**

The most common type of column error in survey data analysis is caused
by inaccurate or erroneous labeling of the column data---an example of
metadata error. In the TSE framework, this is referred to as a
*specification* error. For example, a business register may include a
column labeled "number of employees," defined as the number of persons
in the company who received a payroll check in the month preceding.
Instead the column contains the number of persons on the payroll whether
or not they received a check in the prior month, thus including, for
example, persons on leave without pay.

When analyzing a more diverse set of data sources, such errors could happen 
because of the complexities involved in producing a data set. For example, 
data generated from an individual tweet may undergo a
number of transformations before it is included in the analysis data
set. This transformative process can be quite complex, involving parsing
phrases, identifying words, and classifying them as to subject matter
and then perhaps further classifying them as either positive or negative
expressions about some phenomenon like the economy or a political
figure. There is considerable risk of the resulting variables being
either inaccurately defined or misinterpreted by the data analyst.

---

**Example: Specification error with Twitter data**

As an example, consider a Twitter data set where the rows correspond to
tweets and one of the columns supposedly contains an indicator of
whether the tweet contained one of the following key words: marijuana,
pot, cannabis, weed, hemp, ganja, or THC. Instead, the indicator
actually corresponds to whether the tweet contained a shorter list of
words; say, either marijuana or pot. The mislabeled column is an example
of specification error which could be a biasing factor in an analysis.
For example, estimates of marijuana use based upon the indicator could
be underestimates.

---

**Cell errors**

Finally, cell errors can be of three types: content error, specification
error, or missing data. 

Content Error:
A content error occurs when the value in a cell
satisfies the column definition but still deviates from the true value,
whether or not the true value is known. For example, the value satisfies
the definition of "number of employees" but is outdated because it does
not agree with the current number of employees. Errors in sensitive data
such as drug use, prior arrests, and sexual misconduct may be
deliberate. Thus, content errors may be the result of the measurement
process, a transcription error, a data processing error (e.g., keying,
coding, editing), an imputation error, or some other cause.

Specification Error:
Specification error is just as described for column error but applied to
a cell. For example, the column is correctly defined and labeled;
however, a few companies provided values that, although otherwise highly
accurate, were nevertheless inconsistent with the required definition.

Missing data:
Missing data, as the name implies, are just empty cells. As described in 
Kreuter and Peng [@kreuter201412], data sets derived from big data are 
notoriously affected by all three types of cell error, particularly missing 
or incomplete data, perhaps because that is the most obvious deficiency.

Missing data can take two forms: missing information in a cell of a data 
matrix (referred to as *item missingness*) or missing rows (referred to 
as *unit missingness*), with the former being readily observable whereas 
the latter can be completely hidden from the analyst. Much is known from 
the survey research literature about how both types of missingness affect 
data analysis (see, for example, Little and Rubin
[@little2014statistical; @rubin1976]). Rubin [@rubin1976] introduced the
term *missing completely at random (MCAR)* to describe data where the
data that are available (say, the rows of a data set) can be considered
as a simple random sample of the inferential population (i.e., the
population to which inferences from the data analysis will be made).
Since the data set represents the population, MCAR data provide results
that are generalizable to this population.

A second possibility also exists for the reasons why data are missing.
For example, students who have high absenteeism may be missing because
they were ill on the day of the test. They may otherwise be average
performers on the test so, in this case, it has little to do with how
they would score. Thus, the values are missing for reasons related to
another variable, health, that may be available in the data set and
completely observed. Students with poor health tend to be missing test
scores, regardless of those student's performance on the test. Rubin
[@rubin1976] uses the term *missing at random (MAR)* to describe data
that are missing for reasons related to completely observed variables in
the data set. It is possible to compensate for this type of missingness
in statistical inferences by modeling the missing data mechanism.

However, most often, missing data may be related to factors that are not
represented in the data set and, thus, the missing data mechanism cannot
be adequately modeled. For example, there may be a tendency for test
scores to be missing from school administrative data files for students
who are poor academic performers. Rubin calls this form of missingness
*nonignorable*. With nonignorable missing data, the reasons for the
missing observations depend on the values that are missing. When we
suspect a nonignorable missing data mechanism, we need to use procedures
much more complex than will be described here. Little and Rubin
[@little2014statistical] and Schafer [@schafer1997analysis] discuss
methods that can be used for nonignorable missing data. Ruling out a
nonignorable response mechanism can simplify the analysis considerably.

In practice, it is quite difficult to obtain empirical evidence about
whether or not the data are MCAR or MAR. Understanding the data
generation process is invaluable for specifying models that
appropriately represent the missing data mechanism and that will then be
successful in compensating for missing data in an analysis. (Schafer and
Graham [@schafer2002missing] provide a more thorough discussion of this
issue.)

One strategy for ensuring that the missing data mechanism can be
successfully modeled is to have available on the data set many variables
that may be causally related to missing data. For example, features such
as personal income are subject to high item missingness, and often the
missingness is related to income. However, less sensitive, surrogate
variables such as years of education or type of employment may be less
subject to missingness. The statistical relationship between income and
other income-related variables increases the chance that information
lost in missing variables is supplemented by other completely observed
variables. Model-based methods use the multivariate relationship between
variables to handle the missing data. Thus, the more informative the
data set, the more measures we have on important constructs, the more
successfully we can compensate for missing data using model-based
Approaches.

In the next section, we consider the impact of errors on some forms of
analysis that are common in the big data literature. We will limit the 
focus on the effects of content errors on data analysis. However, there 
are numerous resources available for studying and mitigating the effects 
of missing data on analysis such as books by Little and Rubin 
[@little2014statistical], Schafer [@schafer1997analysis], and Allison 
[@allison2001missing].

Example: Google Flu Trends {#sec:10-3}
-----------------------------------

A well-known example of the risks of bad inference is provided by the
Google Flu Trends series that uses Google searches on flu symptoms,
remedies, and other related key words to provide near-real-time
estimates of flu activity in the USA and 24 other countries^[See 
the discussion in Section 1.3.]. Compared to
CDC data, the Google Flu Trends provided remarkably accurate indicators
of flu incidence in the USA between 2009 and 2011. However, for the
2012--2013 flu seasons, the Google Flu Trends estimates were almost double 
the CDC's [@butler2013google]. Lazer et al. [@lazer2014parable] cite 
two causes of this error: big data hubris and algorithm dynamics.

Hubris occurs when the big data researcher believes that the volume of
the data compensates for any of its deficiencies, thus obviating the
need for traditional, scientific analytic approaches. As Lazer et
al. [@lazer2014parable] note, big data hubris fails to recognize that
"quantity of data does not mean that one can ignore foundational issues
of measurement and construct validity and reliability."

Algorithm dynamics refers to properties of algorithms that allow them to
adapt and "learn" as the processes generating the data change over time.
Although explanations vary, the fact remains that Google Flu Trends estimates 
were too high and by considerable margins for 100 out of 108 weeks starting
in July 2012. Lazer et al. [@lazer2014parable] also blame "blue team
dynamics," which arises when the data generating engine is modified in
such a way that the formerly highly predictive search terms eventually
failed to work. For example, when a Google user searched on "fever" or
"cough," Google's other programs started recommending searches for flu
symptoms and treatments---the very search terms the algorithm used to
predict flu. Thus, flu-related searches artificially spiked as a result
changes to the algorithm and the impact these changes had on
user behavior. In survey research, this is similar to the measurement
biases induced by interviewers who suggest to respondents who are
coughing that they might have flu, then ask the same respondents if they
think they might have flu.

Algorithm dynamic issues are not limited to Google. Platforms such as
Twitter and Facebook are also frequently modified to improve the user
experience. A key lesson provided by Google Flu Trends is that
successful analyses using big data today may fail to produce good
results tomorrow. All these platforms change their methodologies more or
less frequently, with ambiguous results for any kind of long-term study
unless highly nuanced methods are routinely used. Recommendation engines
often exacerbate effects in a certain direction, but these effects are
hard to tease out. Furthermore, other sources of error may affect Google
Flu Trends to an unknown extent. For example, selectivity may be an
important issue because the demographics of people with Internet access
are quite different from the demographic characteristics related to flu
incidence [@thompson2006epidemiology]. Thus, the "at risk" population
for influenza and the implied population based on Google searches do not
correspond. This illustrates just one type of representativeness issue
that often plagues big data analysis. In general it is an issue that
algorithms are not (publicly) measured for accuracy, since they are
often proprietary. Google Flu Trends is special in that it publicly
failed. From what we have seen, most models fail privately and often
without anyone noticing.
--------


Errors in data analysis {#sec:10-4}
----------------------------

The total error framework described above focuses on different types of errors in the data that can lead to incorrect inference. In addition to direct inference errors because of errors in the data, our analysis can also be incorrect because of  these data errors.  This section goes deeper into these common types of analysis errors when analyzing a diverse set of data sources. We begin by exploring errors that can happen under the assumption of accurate data and then go on to consider errors in three common types of analysis when data is not accurate: classification, correlation, and regression. 

Analysis errors despite accurate data

Data deficiencies represent only one set of challenges for the big data
analyst. Even if data is correct, other challenges can arise solely as 
a result of the massive size,
rapid generation, and vast dimensionality of the data [@meng2018]. 
Fan et al. [@fan2014challenges]
identify three issues--- noise accumulation, spurious correlations, and
incidental endogeneity---which will be discussed in this
section. These issues should concern social scientists even if the data
could be regarded as infallible. Content errors, missing data, and other
data deficiencies will only exacerbate these problems.

Noise accumulation**

To illustrate noise accumulation, Fan et al. [@fan2014challenges]
consider the following scenario. Suppose an analyst is interested in
classifying individuals into two categories, $C_{1}$ and $C_{2}$, based
upon the values of 1,000 variables in a big data set. Suppose further
that, unknown to the researcher, the mean value for persons in $C_{1}$
is 0 on all 1,000 variables while persons in $C_{2}$ have a mean of 3 on
the first 10 variables and 0 on all other variables. Since we are
assuming the data are error-free, a classification rule based upon the
first $m \le  10$ variables performs quite well, with little
classification error. However, as more and more variables are included
in the rule, classification error increases because the uninformative
variables (i.e., the 990 variables having no discriminating power)
eventually overwhelm the informative signals (i.e., the first 10
variables). In the Fan et al. [@fan2014challenges] example, when
$m > 200$, the accumulated noise exceeds the signal embedded in the
first 10 variables and the classification rule becomes equivalent to a
coin-flip classification rule.

---

Spurious correlations**

High dimensionality can also introduce coincidental (or *spurious*)
correlations in that many unrelated variables may be highly correlated
simply by chance, resulting in false discoveries and erroneous
inferences. The phenomenon depicted in Figure
\@ref(fig:fig10-3), is an
illustration of this. Many more examples can be found on a website and
in a book devoted to the topic [@spurious; @spurious2]. Fan et
al. [@fan2014challenges] explain this phenomenon using simulated
populations and relatively small sample sizes. They illustrate how, with
800 independent (i.e., uncorrelated) variables, the analyst has a 50%
chance of observing an absolute correlation that exceeds 0.4. Their
results suggest that there are considerable risks of false inference
associated with a purely empirical approach to predictive analytics
using high-dimensional data.

```{r fig10-3, out.width = '70%', fig.align = 'center', echo = FALSE, fig.cap = 'An illustration of coincidental correlation between two variables: stork die-off linked to human birth decline [@sies1988new]'}
knitr::include_graphics("ChapterError/figures/fig10-3.png")
```

Incidental Endogeneity**

Finally, turning to incidental endogeneity, a key assumption in
regression analysis is that the model covariates are uncorrelated with
the residual error; endogeneity refers to a violation of this
assumption. For high-dimensional models, this can occur purely by
chance---a phenomenon Fan and Liao [@fan2014endogeneity] call
*incidental endogeneity*. Incidental endogeneity leads to the modeling
of spurious variation in the outcome variables resulting in errors in
the model selection process and biases in the model predictions. The
risks of incidental endogeneity increase as the number of variables in
the model selection process grows large. Thus it is a particularly
important concern for big data analytics.

Fan et al. [@fan2014challenges] as well as a number of other authors
[@stock2002forecasting; @fan2009ultrahigh] (see, for example, Hall and
Miller [@HallMiller2009]; Fan and Liao, [@FanLiao2012]) suggest robust
statistical methods aimed at mitigating the risks of noise accumulation,
spurious correlations, and incidental endogeneity. However, as
previously noted, these issues and others are further compounded when
data errors are present in a data set. Biemer and Trewin
[@biemer1997review] show that data errors will bias the results of
traditional data analysis and inflate the variance of estimates in ways
that are difficult to evaluate or mitigate in the analysis process.

### Analysis errors resulting from inaccurate data {#sec:10-4.2}

The previous sections examined some of the issues social scientists face
as either $N$ or $p$ in Figure \@ref(fig:fig10-1) becomes extremely large. 
When row, column, and cell errors are added into the mix, these problems can be
further exacerbated. For example, noise accumulation can be expected to
accelerate when random noise (i.e., content errors) afflicts the data.
Spurious correlations that give rise to both incidental endogeneity and
coincidental correlations can render correlation analysis meaningless if
the error levels in big data are high. In this section, we
consider some of the issues that arise in classification, correlation,
and regression analysis as a result of content errors that may be either
variable or systematic.

There are various important findings in this section. First, for rare
classes, even small levels of error can impart considerable biases in
classification analysis. Second, variable errors will attenuate
correlations and regression slope coefficients; however, these effects
can be mitigated by forming meaningful aggregates of the data and
substituting these aggregates for the individual units in these
analyses. Third, unlike random noise, systematic errors can bias
correlation and regression analysis is unpredictable ways, and these
biases cannot be effectively mitigated by aggregating the data. Finally,
multilevel modeling can -- under certain circumstances -- be an important 
mitigation strategy for dealing
with systematic errors emanating from multiple data sources. These
issues will be examined in some detail in the remainder of this section.

We will start by focusing on two types of errors: variable (uncorrelated) errors and correlated errors. We’ll first describe these errors for continuous data and then extend it to categorical variables in the next section.

#### Variable (uncorrelated) and correlated error in continuous variables {#sec:10-4.2.1}

Error models are essential for understanding the effects of error on
data sets and the estimates that may be derived from them. They allow us
to concisely and precisely communicate the nature of the errors that are
being considered, the general conditions that give rise to them, how
they affect the data, how they may affect the analysis of these data,
and how their effects can be evaluated and mitigated. In the remainder
of this chapter, we focus primarily on content errors and consider two
types of error, variable errors and correlated errors, the latter a
subcategory of systematic errors.

Variable errors are sometimes referred to as *random noise* or
*uncorrelated* errors. For example, administrative databases often
contain errors from a myriad of random causes, including mistakes in
keying or other forms of data capture, errors on the part of the persons
providing the data due to confusion about the information requested,
difficulties in recalling information, the vagaries of the terms used to
request the inputs, and other system deficiencies.

Correlated errors, on the other hand, carry a systematic effect that
results in a nonzero covariance between the errors of two distinct
units. For example, quite often, an analysis data set may combine
multiple data sets from different sources and each source may impart
errors that follow a somewhat different distribution. As we shall see,
these differences in error distributions can induce correlated errors in
the merged data set. It is also possible that correlated errors are
induced from a single source as a result of different operators (e.g.,
computer programmers, data collection personnel, data editors, coders,
data capture mechanisms) handling the data. Differences in the way these
operators perform their tasks have the potential to alter the error
distributions so that data elements handled by the same operator have
errors that are correlated [@biemer2003].

These concepts may be best expressed by a simple error model. Let
$y_{rc}$ denote the cell value for variable $c$ on the $r$th unit in the
data set, and let $\varepsilon_{rc}$ denote the error associated with
this value. Suppose it can be assumed that there is a true value
underlying $y_{rc}$, which is denoted by $\mu_{rc}$. Then we can write
$$\label{eq:10-1.1}
y_{rc} = \mu_{rc} + \varepsilon_{rc}.$$

At this point, $\varepsilon_{rc}$ is not stochastic in nature because a
statistical process for generating the data has not yet been assumed.
Therefore, it is not clear what *correlated error* really means. To
remedy this problem, we can consider the hypothetical situation where
the processes generating the data set can be repeated under the same
general conditions (i.e., at the same point in time with the same
external and internal factors operating). Each time the processes are
repeated, a different set of errors may be realized. Thus, it is assumed
that although the true values, $\mu_{rc}$, are fixed, the errors,
$\varepsilon_{rc}$, can vary across the hypothetical, infinite
repetitions of the data set generating process. Let $\mbox{E}(\cdot)$
denote the expected value over all these hypothetical repetitions, and
define the variance, $\mathrm{Var}(\cdot)$, and covariance,
$\mathrm{Cov}(\cdot)$, analogously.

For the present, error correlations between variables are not
considered, and thus the subscript, $c$, is dropped to simplify the
notation. For the uncorrelated data model, we assume that
${\rm E}(y_r \vert r) = \mu_r$, ${\rm Var}(y_r \vert r) =
\sigma_\varepsilon^2$, and ${\rm Cov}(y_r ,y_s \vert r,s) = 0$, for
$r \ne s$. For the correlated data model, the latter assumption is
relaxed. To add a bit more structure to the model, suppose the data set
is the product of combining data from multiple sources (or operators)
denoted by $j = 1, 2, \ldots,
J$, and let $b_j$ denote the systematic effect of the $j$th source. Here
we also assume that, with each hypothetical repetition of the data set
generating process, these systematic effects can vary stochastically.
(It is also possible to assume the systematic effects are fixed. See,
for example, Biemer and Stokes [@BiemerStokes1991] for more details on
this model.) Thus, we assume that ${\rm E}(b_j ) = 0$,
${\rm Var}(b_j ) =
\sigma_b^2$, and ${\rm Cov}(b_j ,b_k ) = 0$ for $j \ne k$.

Finally, for the $r$th unit within the $j$th source, let
$\varepsilon_{rj} = b_j + e_{rj}$. Then it follows that
$$\label{eq:10-1.2}
\begin{array}{lcl@{\quad}l}
\mathrm{Cov}(\varepsilon_{rj} ,\varepsilon_{sk}) =
\begin{cases}
\sigma_b^2 + \sigma_\varepsilon^2 & \text{for } r = s,j = k, \\
%& =&
\sigma_\varepsilon^2&\text{for } r = s,j \ne k,  \\
% &=&
 0 & \text{for } r \ne s,j \ne k.
 \end{cases}
\end{array}$$ The case where $\sigma_b^2 = 0$ corresponds to the
uncorrelated error model (i.e., $b_j = 0$) and thus $\varepsilon_{rj}$
is purely random noise.

---

**Example: Speed sensor**

Suppose that, due to calibration error, the $j$th speed sensor in a
traffic pattern study underestimates the speed of vehicle traffic on a
highway by an average of 4 miles per hour. Thus, the model for this
sensor is that the speed for the $r$th vehicle recorded by this sensor
$(y_{rj})$ is the vehicle's true speed $(\mu_{rj})$ minus 4 mph
($b_{j}$) plus a random departure from $-4$ for the $r$th vehicle
($\varepsilon_{rj}$). Note that to the extent that $b_{j}$ varies across
sensors $j = 1,\ldots ,J$ in the study, $\sigma_b^2$ will be large.
Further, to the extent that ambient noise in the readings for $j$th
sensor causes variation around the values $\mu_{rc} + b_j$, then
$\sigma_\varepsilon^2$ will be large. Both sources of variation will
reduce the reliability of the measurements. However, as shown in Section
[Errors in Correlation analysis](#sec:10-4.2.4),
the systematic error component is particularly problematic for many
types of analysis.

---

#### Extending Variable and Correlated Error to Categorical Data {#sec:10-4.2.2}

For variables that are categorical, the model of the previous section is
not appropriate because the assumptions it makes about the error
structure do not hold. For example, consider the case of a binary
($0/1$) variable. Since both $y_r$ and $\mu_r$ should be either 1 or 0,
the error in equation (10.1) must assume the values of $-1$, $0$, or $1$. A
more appropriate model is the misclassification model described by
Biemer [@biemer2011latent], which we summarize here.

Let $\phi_r$ denote the probability of a false positive error (i.e.,
$\phi_r = \Pr (y_r = 1\vert \mu_r = 0)$), and let $\theta_r$ denote the
probability of a false negative error (i.e.,
$\theta_r =\Pr (y_r = 0\vert \mu_r = 1)$). Thus, the probability that
the value for row $r$ is correct is $1 - \theta_r$ if the true value is
$1$, and $1 - \phi_r$ if the true value is $0$.

As an example, suppose an analyst wishes to compute the proportion,
$P = \sum_r {y_r / N}$, of the units in the file that are classified as
$1$, and let $\pi = \sum_r {\mu_r / N}$ denote the true proportion. Then
under the assumption of uncorrelated error, Biemer [@biemer2011latent]
shows that $$\label{eq:10-1.3}
P = \pi (1 - \theta ) + (1 - \pi )\phi,$$ where
$\theta = \sum_r {\theta_r / N}$ and $\phi = \sum_r
{\phi_r / N}$.

In the classification error literature, the sensitivity of a classifier
is defined as $1 - \theta$, that is, the probability that a true
positive is correctly classified. Correspondingly, $1 - \phi$ is
referred to as the specificity of the classifier, that is, the
probability that a true negative is correctly classified. Two other
quantities that will be useful in our study of misclassification error
are the positive predictive value (PPV) and negative predictive value
(NPV) given by $$\label{eq:10-1.4}
\mathrm{PPV} = \Pr (\mu_r = 1\vert y_r = 1),\quad\mathrm{NPV} = \Pr
(\mu_r = 0\vert y_r = 0).$$ The PPV (NPV) is the probability that a
positive (negative) classification is correct.

#### Errors when analyzing rare population groups {#sec:10-4.2.3}

One of the attractions of newer sources of data such as social media 
is the ability to study rare population groups that seldom show up in 
large enough numbers in designed studies such as surveys and clinical 
trials. While this is true in theory, in practice content errors can 
affect the inferences that can be drawn from this data. We illustrate 
this using the following contrived and somewhat amusing example. The 
results in this section are particularly relevant to the approaches 
considered in Chapter [Machine Learning](#chap:ml).

---

**Example: Thinking about probabilities**

Suppose, using big data and other resources, we construct a terrorist
detector and boast that the detector is 99.9% accurate. In other words,
both the probability of a false negative (i.e., classifying a terrorist
as a nonterrorist, $\theta$) and the probability of a false positive
(i.e., classifying a nonterrorist as a terrorist, $\phi$) are 0.001.
Assume that about $1$ person in a million in the population is a
terrorist, that is, $\pi = 0.000001$ (hopefully, somewhat of an
overestimate). Your friend, Terry, steps into the machine and, to
Terry's chagrin (and your surprise) the detector declares that he is a
terrorist! What are the odds that the machine is right? The surprising
answer is only about 1 in 1000. That is, 999 times out of 1,000 times
the machine classifies a person as a terrorist, the machine will be
wrong!

---

How could such an accurate machine be wrong so often in the terrorism
example? Let us do the math.

The relevant probability is the PPV of the machine: given that the
machine classifies an individual (Terry) as a terrorist, what is the
probability the individual is truly a terrorist? Using the notation in Section 
[Extending Variable and Correlated Error to Categorical Data](#sec:10-4.2.2) 
and Bayes' rule, we can derive the PPV as
$$\begin{aligned}
\Pr (\mu_r = 1\vert y_r = 1) &=  \frac{\Pr (y_r = 1\vert \mu_r =
1)\Pr(\mu_r = 1)}{\Pr (y_r = 1)} \\
&= \frac{(1 - \theta )\pi }{\pi (1 - \theta ) + (1 - \pi )\phi } \\
&=  \frac{0.999\times 0.000001}{0.000001\times 0.999 + 0.99999\times 0.001} \\
 &\approx  0.001.\end{aligned}$$

This example calls into question whether security surveillance using
emails, phone calls, etc. can ever be successful in finding rare threats
such as terrorism since to achieve a reasonably high PPV (say, 90%)
would require a sensitivity and specificity of at least $1-10^{-7}$, or
less than 1 chance in 10 million of an error.

To generalize this approach, note that any population can be regarded as
a *mixture* of subpopulations. Mathematically, this can be written as
$$\label{eq:10-1.5}
f(y\vert \mathbf{x};{\boldsymbol \eth}) = \pi_1 f(y\vert
\mathbf{x};\eth_1 ) + \pi_2 f(y\vert \mathbf{x};\eth_2 ) +
\ldots + \pi_K f(y\vert \mathbf{x};\eth_K ),$$ where
$f(y\vert \mathbf{x}; {\boldsymbol \theta})$ denotes the population
distribution of $y$ given the vector of explanatory variables
$\mathbf{x}$ and the parameter vector ${\boldsymbol \theta
} = (\theta_1 ,\theta_2, \ldots, \theta_K )$, $\pi _k$ is the proportion
of the population in the $k$th subgroup, and $f(y\vert
\mathbf{x};\theta_k)$ is the distribution of $y$ in the $k$th subgroup.
A rare subgroup is one where $\pi_k$ is quite small (say, less than
0.01).

Table \@ref(tab:table10-1) shows the PPV for a range of rare subgroup sizes
when the sensitivity is perfect (i.e., no misclassification of true
positives) and specificity is not perfect but still high. This table
reveals the fallacy of identifying rare population subgroups using
fallible classifiers unless the accuracy of the classifier is
appropriately matched to the rarity of the subgroup. As an example, for
a 0.1% subgroup, the specificity should be at least 99.99%, even with
perfect sensitivity, to attain a 90% PPV.

Table: (\#tab:table10-1) Positive predictive value (%) for rare subgroups, 
high specificity, and perfect sensitivity

|  **$\pi_k$**  | |**Specificity** | |
|---------------|:-:|:---------------:|:-:|
| | *99%* | *99.9%* | *99.99%*|                                        
| 0.1  |           91.70   |  99.10  |   99.90|
|0.01     |       50.30  |   91.00  |   99.00|
 | 0.001  |          9.10 |    50.00  |   90.90|
  |0.0001     |      1.00  |    9.10   |  50.00|
  

#### Errors in Correlation analysis {#sec:10-4.2.4}

In Section [Errors in data analysis](#sec:10-4), we considered the problem of 
incidental correlation that occurs when an analyst correlates pairs of variables
selected from big data stores containing thousands of variables. In this
section, we discuss how errors in the data can exacerbate this problem
or even lead to failure to recognize strong associations among the
variables. We confine the discussion to the continuous variable model of Section 
[Variable (uncorrelated) and correlated error in continuous variables](#sec:10-4.2.1) 
and begin with theoretical results that help explain what happens in correlation 
analysis when the data are subject to variable and systematic errors.

For any two variables in the data set, $c$ and $d$, define the
covariance between $y_{rc}$ and $y_{rd}$ as $$\label{eq:10-1.6}
\sigma_{y\vert cd} = \frac{\sum\nolimits_r {\mbox{E}(y_{rc} -
\bar{y}_c )(y_{rd} - \bar {y}_d )} }{N},$$ where the expectation is with
respect to the error distributions and the sum extends over all rows in
the data set. Let
$$\sigma_{\mu \vert cd} = \frac{\sum\nolimits_r {(\mu_{rc} - \bar{\mu
}_c } )(\mu_{rd} - \bar{\mu }_d )}{N}$$ denote the *population*
covariance. (The population is defined as the set of all units
corresponding to the rows of the data set.) For any variable $c$, define
the variance components $$\sigma_{y\vert c}^2 = \frac{\sum_r {(y_{rc} -
\bar{y}_c )^2}}{N},\quad \sigma_{\mu \vert c}^2 =\frac{%
\sum_r {(\mu_{rc} - \bar {\mu }_c } )^2}{N},$$ and let $$R_c
= \frac{\sigma_{\mu \vert c}^2}{\sigma_{\mu \vert c}^2 +
\sigma_{b\vert c}^2 + \sigma_{\varepsilon \vert c}^2},\quad
\rho_c = \frac{\sigma_{b\vert c}^2}{\sigma_{\mu \vert c}^2 +
\sigma_{b\vert c}^2 + \sigma _{\varepsilon \vert c}^2},$$ with analogous
definitions for $d$. The ratio $R_{c}$ is known as the *reliability
ratio*, and $\rho_c$ will be referred to as the *intra-source
correlation*. Note that the reliability ratio is the proportion of total
variance that is due to the variation of true values in the data set. If
there were no errors, either variable or systematic, then this ratio
would be 1. To the extent that errors exist in the data, $R_{c}$ will be
less than 1.

Likewise, $\rho_c$ is also a ratio of variance components that reflects
the proportion of total variance that is due to systematic errors with
biases that vary by data source. A value of $\rho_c$ that exceeds 0
indicates the presence of systematic error variation in the data. As we
shall see, even small values of $\rho_c$ can cause big problems in
correlation analysis.

Using the results in Biemer and Trewin [@biemer1997review], it can be
shown that the correlation between $y_{rc}$ and $y_{rd}$, defined as
$\rho_{y\vert cd} = \sigma_{y\vert cd} / \sigma_{y\vert c} \sigma_{y\vert d}$, can be expressed as
$$\label{eq:10-1.7} \rho_{y\vert cd} = \sqrt {R_c R_d } \rho_{\mu \vert cd} + \sqrt {\rho_c \rho_d }.$$
Note that if there are no errors (i.e., when
$\sigma_{b\vert
c}^2 = \sigma_{\varepsilon \vert c}^2 = 0$), then $R_c = 1$,
$\rho_c =0$, and the correlation between $y_{c}$ and $y_{d}$ is just the
population correlation.

Let us consider the implications of these results first without
systematic errors (i.e., only variable errors) and then with the effects
of systematic errors.

**Variable errors only**

If the only errors are due to random noise, then the additive term on
the right in equation (10.2) is 0 and $\rho_{y\vert cd} = \sqrt {R_c R_d }
\rho _{\mu \vert cd}$, which says that the correlation is attenuated by
the product of the root reliability ratios. For example, suppose
$R_c = R_d = 0.8$, which is considered excellent reliability. Then the
observed correlation in the data will be about 80% of the true
correlation; that is, correlation is attenuated by random noise. Thus,
$\sqrt {R_c R_d }$ will be referred to as the *attenuation factor* for
the correlation between two variables.

Quite often in the analysis of big data, the correlations being explored
are for aggregate measures, as in Figure \@ref(fig:fig10-3). Therefore,
suppose that, rather than being a single element, $y_{rc}$ and $y_{rd}$
are the means of $n_{rc}$ and $n_{rd}$ independent elements,
respectively. For example, $y_{rc}$ and $y_{rd}$ may be the average rate
of inflation and the average price of oil, respectively, for the $r$th
year, for $r = 1,\ldots ,N$ years. Aggregated data are less affected by variable 
errors because, as we sum up the values in a data set, the positive and
negative values of the random noise components combine and cancel each
other under our assumption that $\mathrm{E}(\varepsilon_{rc} ) = 0$. In
addition, the variance of the mean of the errors is of order
$O(n_{rc}^{ - 1} )$.

To simplify the result for the purposes of our discussion, suppose
$n_{rc} = n_c$, that is, each aggregate is based upon the same sample
size. It can be shown that
equation (10.2) still applies if we replace $R_c$ by its
aggregated data counterpart denoted by
$R_c^A = \sigma_{\mu \vert c}^2 / (\sigma_{\mu \vert
c}^2 + \sigma_{\varepsilon \vert c}^2 / n_c )$. Note that $R_c^A$
converges to 1 as $n_c$ increases, which means that $\rho _{y\vert cd}$
will converge to $\rho_{\mu \vert cd}$. Figure
\@ref(fig:fig10-4) illustrates
the speed at which this convergence occurs.

In this figure, we assume $n_c = n_d = n$ and vary $n$ from 0 to 60. We
set the reliability ratios for both variables to 0.5 (which is
considered to be a "fair" reliability) and assume a population
correlation of $\rho_{\mu \vert cd} = 0.5$. For $n$ in the range
$[2,10]$, the attenuation is pronounced. However, above 10 the
correlation is quite close to the population value. Attenuation is
negligible when $n > 30$. These results suggest that variable error can
be mitigated by aggregating like elements that can be assumed to have
independent errors.

```{r fig10-4, out.width = '70%', fig.align = 'center', echo = FALSE, fig.cap='Correlation as a function of sample size'}
knitr::include_graphics("ChapterError/figures/fig10-4.png")
```

**Both variable and systematic errors**

If both systematic and variable errors contaminate the data, the
additive term on the right in
equation (10.2) is positive. For aggregate data, the reliability
ratio takes the form $$\label{eq:10-1.8}
R_c^A = \frac{{\sigma_{\mu |c}^2}}{{\sigma_{\mu |c}^2 + \sigma
_{b|c}^2 + n_c^{ - 1}\sigma_{\varepsilon |c}^2}},$$ which converges not
to 1 as in the case of variable error only, but to
$\sigma_{\mu \vert c}^2 / (\sigma_{\mu \vert c}^2 +
\sigma_{b\vert c}^2)$, which will be less than 1. Thus, some attenuation
is possible regardless of the number of elements in the aggregate. In
addition, the intra-source correlation takes the form
$$\label{eq:10-1.9}
\rho_c^A = \frac{{\sigma_{b|c}^2}}{{\sigma_{\mu |c}^2 +
\sigma_{b|c}^2 + n_c^{ - 1}\sigma_{\varepsilon |c}^2}},$$ which
converges to $\rho_c^A = \sigma_{b|c}^2/(\sigma_{\mu |c}^2
+ \sigma _{b|c}^2)$, or approximately to $1 - R_c^A$ for large $n_c$. Thus, the
systematic effects may still adversely affect correlation analysis without
regard to the number of elements comprising the aggregates.

For example, consider the illustration in
Figure \@ref(fig:fig10-4) with
$n_c = n_d =
n$, reliability ratios (excluding systematic effects) set at $0.5$ and
population correlation at $\rho_{\mu \vert cd} = 0.5$. In this scenario,
let $\rho_c = \rho_d = 0.25$. Figure \@ref(fig:fig10-5) shows the
correlation as a function of the sample size with systematic errors compared 
to the correlation without systematic errors. Correlation with systematic 
errors is both inflated and attenuated. However, at the assumed level of 
intra-source variation, the inflation factor overwhelms the attenuation 
factors and the result is a much inflated value of the correlation across 
all aggregate sizes.

```{r fig10-5, out.width = '70%', fig.align = 'center', echo = FALSE, fig.cap='Correlation as a function of sample size'}
knitr::include_graphics("ChapterError/figures/fig10-5.png")
```

To summarize these findings, correlation analysis is attenuated by
variable errors, which can lead to null findings when conducting a
correlation analysis and the failure to identify associations that exist
in the data. Combined with systematic errors that may arise when data
are extracted and combined from multiple sources, correlation analysis
can be unpredictable because both attenuation and inflation of
correlations can occur. Aggregating data mitigates the effects of
variable error but may have little effect on systematic errors.

#### Errors in Regression analysis {#sec:10-4.2.5}

The effects of variable errors on regression coefficients are well known
[@cochran1968errors; @fuller1991regression; @biemer1997review]. The
effects of systematic errors on regression have been less studied. We
review some results for both types of errors in this section.

Consider the simple situation where we are interested in computing the
population slope and intercept coefficients given by
$$\label{eq:10-1.10}
b = \frac{\sum_r {(y_r - \bar{y})(x_r - \bar{x})} }{\sum_r {(x_r
- \bar{x})^2} }\quad\mbox{and}\quad b_0 = \bar{y} - b\bar{x},$$ where,
as before, the sum extends over all rows in the data set. When $x$ is
subject to variable errors, it can be shown that the observed regression
coefficient will be attenuated from its error-free counterpart. Let
$R_x$ denote the reliability ratio for $x$. Then $$\label{eq:10-1.11}
b = R_x B,$$ where
$B = \sum_r {(y_r - \bar{y})(\mu_{r\vert x} - \bar{\mu }_x
)} / \sum_r {(\mu_{r\vert x} - \bar{\mu }_x )^2}$ is the population
slope coefficient, with $x_r = \mu_{r\vert x} +
\varepsilon_{r\vert x}$, where $\varepsilon_{r\vert x}$ is the variable
error with mean 0 and variance $\sigma_{\varepsilon\vert x}^2$. It can
also be shown that $\mbox{Bias}(b_0 ) \approx B(1 - R_x )\bar{\mu }_x$.

As an illustration of these effects, consider the regressions displayed
in Figure \@ref(fig:fig10-6), which are based upon contrived data. The
regression on the left is the population (true) regression with a slope
of $1.05$ and an intercept of $-0.61$. The regression on the left uses
the same $y$- and $x$-values. The only difference is that normal error
was added to the $x$-values, resulting in a reliability ratio of $0.73$.
As the theory predicted, the slope was attenuated toward $0$ in direct
proportion to the reliability, $R_{x}$. As random error is added to the
$x$-values, reliability is reduced and the fitted slope will approach
$0$.

```{r fig10-6, out.width = '70%', fig.align = 'center', echo = FALSE, fig.cap = 'Regression of *y* on *x* with and without variable error. On the left is the population regression with no error in the *x* variable. On the right, variable error was added to the *x*-values with a reliability ratio of 0.73. Note its attenuated slope, which is very near the theoretical value of 0.77'}
knitr::include_graphics("ChapterError/figures/fig10-6.png")
```

When the dependent variable, $y$, only is subject to variable error, the
regression deteriorates, but the expected values of the slope and
intercept coefficients are still equal to true to their population
values. To see this, suppose $y_r = \mu_{y\vert
r} + \varepsilon_{y\vert r}$, where $\mu_{r\vert y}$ denotes the
error-free value of $y_r$ and $\varepsilon_{r\vert y}$ is the associated
variable error with variance $\sigma _{\varepsilon
\vert y}^2$. The regression of $y$ on $x$ can now be rewritten as
$$\label{eq:10-1.12}
\mu_{y\vert r} = b_0 + bx_r + e_r - \varepsilon _{r\vert y},$$ where
$e_r$ is the usual regression residual error with mean $0$ and variance
$\sigma_e^2$, which is assumed to be uncorrelated with
$\varepsilon_{r\vert y}$. Letting ${e}' = e_r -
\varepsilon_{r\vert y}$, it follows that the regression in
equation (10.3) is equivalent to the previously considered
regression of $y$ on $x$ where $y$ is not subject to error, but now the
residual variance is increased by the additive term, that is,
$\sigma_{e}^{\prime2} = \sigma_{\varepsilon \vert y}^2 +
\sigma_e^2$.

Chai [@chai1971correlated] considers the case of systematic errors in
the regression variables that may induce correlations both within and
between variables in the regression. He shows that, in the presence of
systematic errors in the independent variable, the bias in the slope
coefficient may either attenuate the slope or increase its magnitude in
ways that cannot be predicted without extensive knowledge of the error
properties. Thus, like the results from correlation analysis, systematic
errors greatly increase the complexity of the bias effects and their
effects on inference can be quite severe.

One approach for dealing with systematic error at the source level in
regression analysis is to model it using, for example, random effects
[@hox2010multilevel]. In brief, a random effects model specifies
$y_{ijk} =
\beta_{0i}^\ast + \beta x_{ijk} + \varepsilon_{ijk}$, where
${\varepsilon }'_{ijk} = b_i + \varepsilon_{ijk}$ and
$\mathrm{Var}({\varepsilon }'_{ijk} ) = \sigma_b^2 + \sigma_{\varepsilon
\vert j}^2$. The next section considers other mitigation strategies that
attempt to eliminate the error rather than model it.

Detecting and Compensating for Data Errors {#sec:10-5}
-------------------------------------------------------------------

For survey data and other *designed* data collections, error mitigation^[Data errors further complicate analysis and exacerbate the analytical problems. There are essentially
three solutions: prevention, remediation, and the choice of analysis methodology.]
begins at the data generation stage by incorporating design strategies
that generate high-quality data that are at least adequate for the
purposes of the data users. For example, missing data can be mitigated
by repeated follow-up of nonrespondents, questionnaires can be perfected
through pretesting and experimentation, interviewers can be trained in
the effective methods for obtaining highly accurate responses, and
computer-assisted interviewing instruments can be programmed to correct
errors in the data as they are generated. For data where the data
generation process is often outside the purview of the data collectors,
as noted in Section [Introduction](#sec:10-1), there is limited opportunity to 
address deficiencies in the data generation process. Instead, error mitigation
must necessarily begin at the data processing stage. We illustrate this error 
mitigation process using two types of techniques - data editing and cleaning.

Data editing is a set of methodologies for identifying and correcting 
(or transforming) anomalies
in the data. It often involves verifying that various relationships
among related variables of the data set are plausible and, if they are
not, attempting to make them so. Editing is typically a rule-based
approach where rules can apply to a particular variable, a combination
of variables, or an aggregate value that is the sum over all the rows or
a subset of the rows in a data set. Recently, data mining and machine
learning techniques have been applied to data editing with excellent
results (see Chandola et al. [@chandola2009anomaly] for a review).
Tree-based methods such as classification and regression trees and
random forests are particularly useful for creating editing rules for
anomaly identification and resolution [@petrakos2004new]. However, some
human review may be necessary to resolve the most complex situations.

For larger amounts of data, the identification of data anomalies could result in
possibly billions of edit failures. Even if only a tiny proportion of
these required some form of manual review for resolution, the task could
still require the inspection of tens or hundreds of thousands of query
edits, which would be infeasible for most applications. Thus,
micro-editing must necessarily be a completely automated process unless
it can be confined to a relatively small subset of the data. As an
example, a representative (random) subset of the data set could be
edited using manual editing for purposes of evaluating the error levels
for the larger data set, or possibly to be used as a training data set,
benchmark, or reference distribution for further processing, including
recursive learning.

To complement fully automated micro-editing, data editing  involving large 
amounts of data usually involves *top-down* or *macro-editing* approaches. 
For such approaches, analysts and systems inspect aggregated data for 
conformance to some benchmark values or data distributions that are known 
from either training data or prior experience. When unexpected or suspicious
aggregates are identified, the analyst can "drill down" into the data to
discover and, if possible, remove the discrepancy by either altering the
value at the source (usually a micro-data element) or delete the
edit-failed value.

There are a variety of methods that may be effective in macro-editing.
Some of these are based upon data mining [@natarajan2010data], machine
learning [@Clarke2014], cluster analysis
[@duan2009cluster; @he2003discovering], and various data visualization
tools such as treemaps
[@johnson1991tree; @shneiderman1992tree; @tennekes2011top] and
tableplots [@tennekes2013visualizing; @puts2015finding; @Tennekes2012].
We further explore tableplots below.

### TablePlots

Like other visualization techniques examined in Chapter [Information Visualization](#chap:viz), the tableplot has the ability to summarize a
large multivariate data set in a single plot [@malik2010interactive]. In
editing data, it can be used to detect outliers and unusual data
patterns. Software for implementing this technique has been written in R
and is available from the Comprehensive R Archive Network
(<https://cran.r-project.org/>). Figure
\@ref(fig:fig10-7) shows an example. The key idea is that micro-aggregates of two related
variables should have similar data patterns. Inconsistent data patterns
may signal errors in one of the aggregates that can be investigated and
corrected in the editing process to improve data quality. The tableplot
uses bar charts created for the micro-aggregates to identify these
inconsistent data patterns.

Each column in the tableplot represents some variable in the data table,
and each row is a "bin" containing a subset of the data. A statistic
such as the mean or total is computed for the values in a bin and is
displayed as a bar (for continuous variables) or as a stacked bar for
categorical variables.

```{r fig10-7, out.width = '70%', fig.align = 'center', echo = FALSE, fig.cap = 'Comparison of tableplots for the Dutch Structural Business Statistics Survey for five variables before and after editing. Row bins with high missing and unknown numeric values are represented by lighter colored bars'}
knitr::include_graphics("ChapterError/figures/fig10-7.png")
```

The sequence of steps typically involved in producing a tableplot is as
follows:

1.  Sort the records in the data set by the key variable.

2.  Divide the sorted data set into $B$ bins containing the same number
    of rows.

3.  For continuous variables, compute the statistic to be compared
    across variables for each row bin, say $T_{b}$, for $b =
    1,\ldots ,B$, for each continuous variable, $V$, ignoring missing
    values. The level of missingness for $V$ may be represented by the
    color or brightness of the bar. For categorical variables with $K$
    categories, compute the proportion in the $k$th category, denoted by
    $P_{bk}$. Missing values are assigned to a new ($K+1$)th category
    ("missing").

4.  For continuous variables, plot the $B$ values $T_{b}$ as a bar
    chart. For categorical variables, plot the $B$ proportions $P_{bk}$
    as astacked bar chart.

Typically, $T_{b}$ is the mean, but other statistics such as the median
or range could be plotted if they aid in the outlier identification
process. For highly skewed distributions, Tennekes and de Jonge
[@tennekes2011top] suggest transforming $T_{b}$ by the log function to
better capture the range of values in the data set. In that case,
negative values can be plotted as $\log(-T_{b})$ to the left of the
origin and zero values can be plotted on the origin line. For
categorical variables, each bar in the stack should be displayed using
contrasting colors so that the divisions between categories are
apparent.

Tableplots appear to be well suited for studying the distributions of
variable values, the correlation between variables, and the occurrence
and selectivity of missing values. Because they can help visualize
massive, multivariate data sets, they seem particularly well suited for
big data. Currently, the R implementation of tableplot is limited to $2$
billion records.

The tableplot in Figure \@ref(fig:fig10-7) is taken from Tennekes and de Jonge
[@tennekes2011top] for the annual Dutch Structural Business Statistics
survey, a survey of approximately 58,000 business units annually. Topics
covered in the questionnaire include turnover, number of employed
persons, total purchases, and financial results. Figure
\@ref(fig:fig10-7) was
created by sorting on the first column, viz., log(turnover), and
dividing the 57,621 observed units into 100 bins, so that each row bin
contains approximately 576 records. To aid the comparisons between
unedited and edited data, the two tableplots are displayed side by side,
with the unedited graph on the left and the edited graph on the right.
All variables were transformed by the log function.

The unedited tableplot reveals that all four of the variables in the
comparison with log(turnover) show some distortion by large values for
some row bins. In particular, log(employees) has some fairly large
nonconforming bins with considerable discrepancies. In addition, that
variable suffers from a large number of missing values, as indicated by
the brightness of the bar color. All in all, there are obvious data
quality issues in the unprocessed data set for all four of these
variables that should be dealt with in the subsequent processing steps.

The edited tableplot reveals the effect of the data checking and editing
strategy used in the editing process. Notice the much darker color for
the number of employees for the graph on the left compared to same graph
on the right. In addition, the lack of data in the lowest part of the
turnover column has been somewhat improved. The distributions for the
graph on the right appear smoother and are less jagged.

Summary {#sec:10-6}
-------
As social scientists, we are deeply concerned with making sure that the inferences we make from our analysis are valid. Since many of the newer data sources we are using are not collected or generated from instruments and methods designed to produce valid and reliable data for scientific analysis and discovery, they can lead to inference errors. This chapter described different types of errors that we encounter to make us aware of these limitations and take the necessary steps to understand and hopefully mitigate the effects of hidden errors on our results.

In addition to describing the types of errors, this chapter also gives an example of a solution to clean up the data before analysis. Another option that was not discussed is the possibility of using analytical techniques that attempt to model errors and compensate for them in the analysis. Such techniques include the use of latent class analysis for classification error [@biemer2011latent], multilevel modeling of systematic errors from multiple sources [@hox2010multilevel], and Bayesian statistics for partitioning massive data sets across multiple machines and then combining the results [@ibrahim2000power; @scott2013bayes].

While this chapter has focused on the accuracy of the data and the validity of the inference, other data quality dimensions such as timeliness, comparability, coherence, and relevance that we have not considered in this chapter are also important. For example, timeliness often competes with accuracy because achieving acceptable levels of the latter often requires greater expenditures of resources and time. In fact, some applications of data analysis prefer results that are less accurate for the sake of timeliness. Biemer and Lyberg [@biemer2003] discuss these and other issues in some detail. 

It is important to understand that we will rarely, if ever, get perfect data for our analysis. Every data source will have some limitation - some will be inaccurate, some will become stale,  and some will have sample bias. The key is to 1) be aware of the limitations of each data source, 2) incorporate that awareness in to the analysis that is being done with it, and 3) understand what type of inference errors it can lead to in order to appropriately communicate the results and make sound decisions.

Resources
---------

The American Association of Public Opinion Research has a number of
resources on its website [@aaporWeb]. See, in particular, its report on
big data [@japec2015big].

The *Journal of Official Statistics* [@JOSweb] is a standard resource
with many relevant articles. There is also an annual international
conference on the total survey error framework, supported by major
survey organizations [@TSEweb].