Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study

This article provides guidance on creating a data analysis plan for a quantitative study, including choosing appropriate statistics and understanding the levels of measurement. It covers topics such as variables, descriptive statistics, and inferential statistics.

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There are three kinds of lies: lies, damned lies, and statistics.
– Mark Twain1
INTRODUCTION
Statistics represent an essential part of a study because, regard-
less of the study design, investigators need to summarize the
collected information for interpretation and presentation to
others. It is therefore important for us to heed Mr Twain’s
concern when creating the data analysis plan. In fact, even before
data collection begins, we need to have a clear analysis plan
that will guide us from the initial stages of summarizing and
describing the data through to testing our hypotheses.
The purpose of this article is to help you create a data analysis
plan for a quantitative study. For those interested in conducting
qualitative research, previous articles in this Research Primer
series have provided information on the design and analysis of
such studies.2,3Information in the current article is divided into
3 main sections: an overview of terms and concepts used in data
analysis, a review of common methods used to summarize study
data, and a process to help identify relevant statistical tests. My
intention here is to introduce the main elements of data analysis
and provide a place for you to start when planning this part of
your study. Biostatistical experts, textbooks, statistical software
packages, and other resources can certainly add more breadth
and depth to this topic when you need additional information
and advice.
TERMS AND CONCEPTS USED IN DATA
ANALYSIS
When analyzing information from a quantitative study, we
are often dealing with numbers; therefore, it is important to begin
with an understanding of the source of the numbers. Let us start
with the term variable, which defines a specific item of informa-
tion collected in a study. Examples of variables include age, sex
or gender, ethnicity, exercise frequency, weight, treatment g
and blood glucose. Each variable will have a group of catego
which are referred to as values, to help describe the charact
of an individual study participant. For example, the variable
would have values of “male” and “female”.
Although variables can be defined or grouped in various
ways, I will focus on 2 methods at this introductory stage. Fir
variables can be defined according to the level of measurem
The categories in a nominal variable are names, for example
and female for the variable “sex”; white, Aboriginal, black, L
American, South Asian, and East Asian for the variable “ethn-
city”; and intervention and control for the variable “treatmen
group”. Nominal variables with only 2 categories are also ref
to as dichotomousvariables because the study group can be
divided into 2 subgroups based on information in the variabl
For example, a study sample can be split into 2 groups (patie
receiving the intervention and controls) using the dichotomo
variable “treatment group”. An ordinal variable implies that
categories can be placed in a meaningful order, as would be
case for exercise frequency (never, sometimes, often, or alw
Nominal-level and ordinal-level variables are also referred to
categorical variables, because each category in the variable
completely separated from the others. The categories for
interval variable can be placed in a meaningful order, with th
interval between consecutive categories also having meanin
Age, weight, and blood glucose can be considered as interva
variables, but also as ratio variables, because the ratio betw
values has meaning (e.g., a 15-year-old is half the age
30-year-old). Interval-level and ratio-level variables are a
referred to as continuousvariables because of the underlying
continuity among categories.
As we progress through the levels of measurement from
nominal to ratio variables, we gather more information abou
the study participant. The amount of information that a varia
provides will become important in the analysis stage, becaus
lose information when variables are reduced or aggregated—
RESEARCH PRIMER
Creating a Data Analysis Plan: What to
Consider When Choosing Statistics for a S
Scot H Simpson
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common practice that is not recommended.4 For example, if age
is reduced from a ratio-level variable (measured in years) to an
ordinal variable (categories of < 65 and≥ 65 years) we lose the
ability to make comparisons across the entire age range and
introduce error into the data analysis.4
A second method of defining variables is to consider them
as either dependent or independent. As the terms imply, the value
of a dependent variable depends on the value of other variables,
whereas the value of an independent variable does not rely on
other variables. In addition, an investigator can influence the
value of an independent variable, such as treatment-group
assignment.Independentvariablesare alsoreferredto as
predictors because we can use information from these variables to
predict the value of a dependent variable. Building on the group
of variables listed in the first paragraph of this section, blood glu-
cose could be considered a dependent variable, because its value
may depend on values of the independent variables age, sex, eth-
nicity, exercise frequency, weight, and treatment group.
Statistics are mathematical formulae that are used to organize
and interpretthe informationthat is collectedthrough
variables. There are 2 general categories of statistics, descriptive
and inferential. Descriptive statistics are used to describe the
collected information, such as the range of values, their average,
and the most common category. Knowledge gained from
descriptive statistics helps investigators learn more about the
study sample. Inferential statistics are used to make comparisons
and draw conclusions from the study data. Knowledge gained
from inferential statistics allows investigators to make inferences
and generalize beyond their study sample to other groups.
Before we move on to specific descriptive and inferential
statistics, there are 2 more definitions to review. Parametric
statistics are generally used when values in an interval-level or
ratio-level variable are normally distributed (i.e., the entire group
of values has a bell-shaped curve when plotted by frequency).
These statistics are used because we can define parameters of the
data, such as the centre and width of the normally distributed
curve. In contrast, interval-level and ratio-level variables with
values that are not normally distributed, as well as nominal-level
and ordinal-level variables, are generally analyzed using nonpara-
metric statistics.
METHODS FOR SUMMARIZING STUDY
DATA: DESCRIPTIVE STATISTICS
The first step in a data analysis plan is to describe the data
collected in the study. This can be done using figures to give a
visual presentation of the data and statistics to generate numeric
descriptions of the data.
Selection of an appropriate figure to represent a particular
set of data depends on the measurement level of the variable.
Data for nominal-level and ordinal-level variables may be inter-
preted using a pie graph or bar graph. Both options allow us to
examine the relative number of participants within each cate
(by reporting the percentages within each category), wherea
bar graph can also be used to examine absolute numbers. Fo
example, we could create a pie graph to illustrate the propor
of men and women in a study sample and a bar graph to illu
the number of people who report exercising at each level of
frequency (never, sometimes, often, or always).
Interval-level and ratio-level variables may also be inter-
preted using a pie graph or bar graph; however, these types
variables often have too many categories for such grap
provide meaningful information. Instead, these variables ma
better interpreted using a histogram. Unlike a bar graph, wh
displays the frequency for each distinct category, a histogram
displays the frequency within a range of continuous categori
Information from this type of figure allows us to determine
whether the data are normally distributed. In addition to pie
graphs, bar graphs, and histograms, many other types of fig
are available for the visual representation of data. Interested
ers can find additional types of figures in the books recom-
mended in the “Further Readings” section.
Figures are also useful for visualizing comparisons betwe
variables or between subgroups within a variable (for examp
the distribution of blood glucose according to sex). Box plots
useful for summarizing information for a variable that does n
follow a normal distribution. The lower and upper limits of
the box identify the interquartile range (or 25th and 75
percentiles), while the midline indicates the median value (o
50th percentile). Scatter plots provide information on how th
categories for one continuous variable relate to categories in
ond variable; they are often helpful in the analysis of correla
In addition to using figures to present a visual description
of the data, investigators can use statistics to provide a num
description. Regardless of the measurement level, we can fin
mode by identifying the most frequent category within a var
When summarizing nominal-level and ordinal-level variables
simplest method is to report the proportion of participa
within each category.
The choice of the most appropriate descriptive statistic fo
interval-level and ratio-level variables will depend on how th
values are distributed. If the values are normally distributed,
can summarize the information using the parametric statistic
mean and standard deviation. The mean is the arithmetic av
of all values within the variable, and the standard deviation
us how widely the values are dispersed around the mean. W
values of interval-level and ratio-level variables are not norm
distributed, or we are summarizing information from an
ordinal-level variable, it may be more appropriate to use the
nonparametric statistics of median and range. The first step
identifying these descriptive statistics is to arrange study pa
pants according to the variable categories from lowest value
highest value. The range is used to report the lowest and hig
values. The median or 50th percentile is located by dividing
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313C J H P – Vol. 68, No. 4 – July–August 2015 J C P H – Vol. 68, no 4 – juillet–août 2015
number of participants into 2 groups, such that half (50%) of
the participants have values above the median and the other half
(50%) have values below the median. Similarly, the 25th
percentile is the value with 25% of the participants having values
below and 75% of the participants having values above, and the
75th percentile is the value with 75% of participants having
values below and 25% of participants having values above.
Together, the 25th and 75th percentiles define the interquartile range.
PROCESS TO IDENTIFY RELEVANT
STATISTICAL TESTS: INFERENTIAL STATISTICS
One caveat about the information provided in this section:
selecting the most appropriate inferential statistic for a specific
study should be a combination of following these suggestions,
seeking advice from experts, and discussing with your co-inves-
tigators. My intention here is to give you a place to start a
conversation with your colleagues about the options available as
you develop your data analysis plan.
There are 3 key questions to consider when selecting an
appropriate inferential statistic for a study: What is the research
question? What is the study design? and What is the level of
measurement? It is important for investigators to carefully
consider these questions when developing the study protocol and
creating the analysis plan. The figures that accompany these
questions show decision trees that will help you to narrow down
the list of inferential statistics that would be relevant to a pa
ular study. Appendix 1 provides brief definitions of the infere
statistics named in these figures. Additional information, suc
the formulae for various inferential statistics, can be obtaine
from textbooks, statistical software packages, and biostatist
What Is the Research Question?
The first step in identifying relevant inferential statistics
a study is to consider the type of research question being as
You can find more details about the different types of resear
questions in a previous article in this Research Primer series
covered questions and hypotheses.5 A relational question seeks
information about the relationship among variables; in t
situation, investigators will be interested in determining whe
there is an association (Figure 1). A causal question seeks in
mation about the effect of an intervention on an outcome; in
this situation, the investigator will be interested in determini
whether there is a difference (Figure 2).
What Is the Study Design?
When considering a question of association, investigators
will be interested in measuring the relationship between vari
(Figure 1). A study designed to determine whether ther
consensus among different raters will be measuring agreem
For example, an investigator may be interested in determini
Figure 1. Decision tree to identify inferential statistics for an association.
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whether 2 raters, using the same assessment tool, arrive at the
same score. Correlation analyses examine the strength of a
relationship or connection between 2 variables, like age and
blood glucose. Regression analyses also examine the strength of a
relationship or connection; however, in this type of analysis, one
variable is considered an outcome (or dependent variable) and
the other variable is considered a predictor (or independent vari-
able). Regression analyses often consider the influence of
multiple predictors on an outcome at the same time. For
example, an investigator may be interested in examining the
association between a treatment and blood glucose, while also
considering other factors, like age, sex, ethnicity, exercise
frequency, and weight.
When considering a question of difference, investigators
must first determine how many groups they will be comparing.
In some cases, investigators may be interested in comparing the
characteristic of one group with that of an external reference
group. For example, is the mean age of study participants similar
to the mean age of all people in the target group? If more than
one group is involved, then investigators must also determine
whether there is an underlying connection between the sets of
values (or samples) to be compared. Samples are considered
independent or unpaired when the information is taken from
ferent groups. For example, we could use an unpaired t test
compare the mean age between 2 independent samples, suc
as the intervention and control groups in a study. Samples a
considered related or paired if the information is taken from
same group of people, for example, measurement of bl
glucose at the beginning and end of a study. Because blood
glucose is measured in the same people at both time points,
could use a paired t test to determine whether there has bee
significant change in blood glucose.
What Is the Level of Measurement?
As described in the first section of this article, variables c
be grouped according to the level of measurement (nominal,
ordinal, or interval). In most cases, the independent variable
an inferential statistic will be nominal; therefore, investigato
need to know the level of measurement for the depend
variable before they can select the relevant inferential statis
Two exceptions to this consideration are correlation analyses
regression analyses (Figure 1). Because a correlation an
measures the strength of association between 2 variables, w
Figure 2. Decision tree to identify inferential statistics for measuring a difference.
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315C J H P – Vol. 68, No. 4 – July–August 2015 J C P H – Vol. 68, no 4 – juillet–août 2015
to consider the level of measurement for both variables. Regres-
sion analyses can consider multiple independent variables, often
with a variety of measurement levels. However, for these analyses,
investigators still need to consider the level of measurement for
the dependent variable.
Selection of inferential statistics to test interval-level variables
must include consideration of how the data are distributed. An
underlying assumption for parametric tests is that the data
approximate a normal distribution. When the data are not
normally distributed, information derived from a parametric test
may be wrong.6 When the assumption of normality is violated
(for example, when the data are skewed), then investigators
should use a nonparametric test. If the data are normally distrib-
uted, then investigators can use a parametric test.
ADDITIONAL CONSIDERATIONS
What Is the Level of Significance?
An inferential statistic is used to calculate a p value, the prob-
ability of obtaining the observed data by chance. Investigators
can then compare this p value against a prespecified level of sig-
nificance, which is often chosen to be 0.05. This level of
significance represents a 1 in 20 chance that the observation is
wrong, which is considered an acceptable level of error.
What Are the Most Commonly Used Statistics?
In 1983, Emerson and Colditz7 reported the first review of
statistics used in original research articles published in the New
England Journal of Medicine. This review of statistics used in the
journal was updated in 1989 and 2005,8 and this type of analysis
has been replicated in many other journals.9-13
Collectively, these
reviews have identified 2 important observations. First, the overall
sophistication of statistical methodology used and reported in
studies has grown over time, with survival analyses and multi-
variable regression analyses becoming much more common. The
second observation is that, despite this trend, 1 in 4 articles
describe no statistical methods or report only simple descriptive
statistics. When inferential statistics are used, the most common
are t tests, contingency table tests (for example, ␹2 test and Fisher
exact test), and simple correlation and regression analyses. This
information is important for educators, investigators, reviewers,
and readers because it suggests that a good foundational knowl-
edge of descriptive statistics and common inferential statistics
will enable us to correctly evaluate the majority of research
articles.11-13However, to fully take advantage of all research
publishedin high-impactjournals,we needto become
acquainted with some of the more complex methods, such as
multivariable regression analyses.8,13
What Are Some Additional Resources?
As an investigator and Associate Editor with CJHP, I have
often relied on the advice of colleagues to help create my own
analysis plans and review the plans of others. Biostatistician
a wealth of knowledge in the field of statistical analysis and
provide advice on the correct selection, application, and inte
pretation of these methods. Colleagues who have “been ther
done that” with their own data analysis plans are also valuab
sources of information. Identify these individuals and consult
with them early and often as you develop your analysis plan
Another important resource to consider when creating yo
analysis plan is textbooks. Numerous statistical textbooks ar
available, differing in levels of complexity and scope. The titl
listed in the “Further Reading” section are just a few sugges
I encourage interested readers to look through these and oth
books to find resources that best fit their needs. However, on
crucial book that I highly recommend to anyone wanting to b
an investigator or peer reviewer is Lang and Secic’s Ho
Report Statistics in Medicine (see “Further Reading”). As the
implies, this book covers a wide range of statistics used in m
research and provides numerous examples of how to correct
report the results.
CONCLUSIONS
When it comes to creating an analysis plan for your proje
I recommend following the sage advice of Douglas Adams in
Hitchhiker’s Guide to the Galaxy: Don’t panic!14 Begin with
simple methods to summarize and visualize your data, then
the key questions and decision trees provided in this article
identify relevant statistical tests. Information in this article w
give you and your co-investigators a place to start discussing
elements necessary for developing an analysis plan. But do n
stop there! Use advice from biostatisticians and more experi
colleagues, as well as information in textbooks, to help creat
your analysis plan and choose the most appropriate statistic
for your study. Making careful, informed decisions about the
statistics to use in your study should reduce the risk of confi
Mr Twain’s concern.
References
1. Twain M (Kiskis MJ, editor). Mark Twain’s own autobiography: the chap
from the North American review.2nd ed. Madison (WI): University of
Wisconsin Press; 2010. 318 p.
2. Austin Z, Sutton J. Qualitative research: getting started. Can J Hosp Pha
2014;67(6):436-40.
3. Sutton J, Austin Z. Qualitative research: data collection, analysis,
management. Can J Hosp Pharm. 2015;68(3):226-31.
4. Dawson NV, Weiss R. Dichotomizing continuous variables in statistical
analysis: a practice to avoid. Med Decis Making. 2012;32(2):225-6.
5. Tully MP. Research: articulating questions, generating hypotheses, and
choosing study designs. Can J Hosp Pharm. 2014;67(1):31-4.
6. Harwell MR. Choosing between parametric and nonparametric tests. J
Couns Dev. 1988;67(1):35-8.
7. Emerson JD, Colditz GA. Use of statistical analysis in the New England
Journal of Medicine. N Engl J Med. 1983;309(12):709-13.
8. Horton NJ, Switzer SS. Statistical methods in the journal. N Engl J Med.
2005;353(18):1977-9.
9. Guyatt G, Jaeschke R, Heddle N, Cook D, Shannon H, Walter S. Basic
statistics for clinicians: 1. Hypothesis testing. CMAJ. 1995;152(1):27-32
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C J H P – Vol. 68, No. 4 – July–August 2015 J C P H – Vol. 68, no 4 – juillet–août 2015316
Appendix 1. Glossary of statistical terms*(part 1 of 2)
ANOVA (analysis of variance): Parametric statistic used to
compare the means of 3 or more groups that are defined by
1 or more variables.
• 1-way ANOVA: Uses 1 variable to define the groups for
comparing means. This is similar to the Student t test when
comparing the means of 2 groups.
• Kruskall–Wallis 1-way ANOVA: Nonparametric alternative
for the 1-way ANOVA. Used to determine the difference in
medians between 3 or more groups.
• n-way ANOVA: Uses 2 or more variables to define groups
when comparing means. Also called a “between-subjects
factorial ANOVA”.
• Repeated-measures ANOVA: A method for analyzing
whether the means of 3 or more measures from the same
group of participants are different.
• Freidman ANOVA: Nonparametric alternative for the
repeated-measures ANOVA. It is often used to compare
rankings and preferences that are measured 3 or more times.
Binomial test: Used to determine whether the observed pro-
portion is significantly different from a known or hypothesized
proportion. The variable is dichotomous (nominal-level data
with 2 options).
Biserial correlation (rank or point): Correlation technique
when one of the variables is dichotomous (or measured at
the nominal level).
Chi-square (␹2) test: Nonparametric test used to determine
whether a statistically significant association exists between
rows and columns in a contingency table.
• Fisher exact: Variation of chi-square that accounts for cell
counts < 5.
• McNemar: Variation of chi-square that tests statistical
significance of changes in 2 paired measurements of
dichotomous variables.
• Cochran Q: An extension of the McNemar test that provides
a method for testing for differences between 3 or more
matched sets of frequencies or proportions. Often used as
a measure of heterogeneity in meta-analyses.
Descriptive statistics: Numeric or graphic summaries (or
descriptions) of a variable.
Inferential statistics: Measures the difference between 2
variables or subgroups of a variable. Allows the investigator
to make inferences about another group on the basis of
information generated from the study data.
Kappa (␹): Measures the degree of nonrandom agreement
between observers or measurements for the same nominal-
level variable.
Kendall tau (␹): Nonparametric alternative for the Spearman
correlation. Used when measuring the relationship between
2 ranked (or ordinal-level data) variables.
Mann–Whitney U test: Nonparametric alternative for the
independent t test. One variable is dichotomous (e.g., group
A versus group B) and the other variable is either ordinal or
interval.
Pearson correlation: Parametric test used to determine
whether an association exists between 2 variables measured
at the interval or ratio level.
Phi (␹): Used when both variables in a correlation analysis are
dichotomous.
Runs test: Used to determine whether a series of data occurs
from a random process.
Spearman rank correlation: Nonparametric alternative for
the Pearson correlation coefficient. Used when the assump-
tions for Pearson correlation are violated (e.g., data are not
normally distributed) or one of the variables is measured at the
ordinal level.
t test: Parametric statistical test for comparing the means of
2 independent groups.
• 1-sample: Used to determine whether the mean of a sample
is significantly different from a known or hypothesized
value.
continued on page 317
10. Goldin J, Zhu W, Sayre JW. A review of the statistical analysis used in papers
published in Clinical Radiology and British Journal of Radiology. Clin Radiol.
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Pediatrics: a moving target. Pediatrics. 2007;119(6):1083-8.
13. Taback N, Krzyzanowska MK. A survey of abstracts of high-impact clinical
journals indicated most statistical methods presented are summary statistics.
J Clin Epidemiol. 2008;61(3):277-81.
14. Adams D. The hitchhiker's guide to the galaxy. London (UK): Pan Books;
1979.
Further Reading
Devor J, Peck R. Statistics: the exploration and analysis of data. 7th ed. Boston
(MA): Brooks/Cole Cengage Learning; 2012.
Lang TA, Secic M. How to report statistics in medicine: annotated guidelines for
authors, editors, and reviewers. 2nd ed. Philadelphia (PA): American College of
Physicians; 2006.
Mendenhall W, Beaver RJ, Beaver BM. Introduction to probability and statistics.
13th ed. Belmont (CA): Brooks/Cole Cengage Learning; 2009.
Norman GR, Streiner DL. PDQ statistics. 3rd ed. Hamilton (ON): B.C. Decker
2003.
Plichta SB, Kelvin E. Munro’s statistical methods for health care research. 6
Philadelphia (PA): Wolters Kluwer Health/Lippincott, Williams & Wilkins; 201
Scot H Simpson, BSP, PharmD, MSc, is Professor and Associate Dean,
Research and Graduate Studies, Faculty of Pharmacy and Pharmaceutical
Sciences, University of Alberta, Edmonton, Alberta. He is also an Associate
Editor with the CJHP.
Competing interests: None declared.
Address correspondence to:
Scot H Simpson
Faculty of Pharmacy and Pharmaceutical Sciences
3126 Dentistry/Pharmacy
University of Alberta
Edmonton AB T6G 2N8
e-mail: [e-mail]
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317C J H P – Vol. 68, No. 4 – July–August 2015 J C P H – Vol. 68, no 4 – juillet–août 2015
This article is the 12th in the CJHP Research Primer Series, an ini-
tiative of the CJHP Editorial Board and the CSHP Research Com-
mittee. The planned 2-year series is intended to appeal to
relatively inexperienced researchers, with the goal of building re-
search capacity among practising pharmacists. The articles, pre-
senting simple but rigorous guidance to encourage and support
novice researchers, are being solicited from authors with appro-
priate expertise.
Previous articles in this series:
Bond CM. The research jigsaw: how to get started. Can J Hosp
Pharm. 2014;67(1):28-30.
Tully MP. Research: articulating questions, generating
hypotheses, and choosing study designs. Can J Hosp Pharm.
2014;67(1):31-4.
Loewen P. Ethical issues in pharmacy practice research: an
introductory guide. Can J Hosp Pharm. 2014;67(2):133-7.
Tsuyuki RT. Designing pharmacy practice research trials. Can J
Hosp Pharm. 2014;67(3):226-9.
Bresee LC. An introduction to developing surveys for pharmacy
practice research. Can J Hosp Pharm. 2014;67(4):286-91.
Gamble JM. An introduction to the fundamentals of cohort and
case–control studies. Can J Hosp Pharm. 2014;67(5):366-72.
Austin Z, Sutton J. Qualitative research: getting started. Can J
Hosp Pharm. 2014;67(6):436-40.
Houle S. An introduction to the fundamentals of randomized con-
trolled trials in pharmacy research. Can J Hosp Pharm. 2014;
68(1):28-32.
Charrois TL. Systematic reviews: What do you need to know to
get started? Can J Hosp Pharm. 2014;68(2):144-8.
Sutton J, Austin Z. Qualitative research: data collection, analysis,
and management. Can J Hosp Pharm. 2014;68(3):226-31.
Cadarette SM, Wong L. An introduction to health care adminis-
trative data. Can J Hosp Pharm. 2014;68(3):232-7.
Appendix 1. Glossary of statistical terms*(part 2 of 2)
• Independent-samples t test (also referred to as the
Student t test): Used when the independent variable is a
nominal-level variable that identifies 2 groups and the
dependent variable is an interval-level variable.
• Paired: Used to compare 2 pairs of scores between 2 groups
(e.g., baseline and follow-up blood pressure in the
intervention and control groups).
Wilcoxon rank–sum test: Nonparametric alternative to the
independent t test based solely on the order in which observa-
tions from the 2 samples fall. Similar to the Mann–Whitney
U test.
Wilcoxon signed-rank test: Nonparametric alternative to the
paired t test. The differences between matched pairs are com-
puted and ranked. This test compares the sum of the negative
differences and the sum of the positive differences.
*Sources
Lang TA, Secic M. How to report statistics in medicine:
annotated guidelines for authors, editors, and reviewers. 2nd
ed. Philadelphia (PA): American College of Physicians; 2006.
Norman GR, Streiner DL. PDQ statistics. 3rd ed. Hamilton (ON):
B.C. Decker; 2003.
Plichta SB, Kelvin E. Munro’s statistical methods for health care
research. 6th ed. Philadelphia (PA): Wolters Kluwer Health/
Lippincott, Williams & Wilkins; 2013.
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