Estimation of excess mortality

Introduction

There are a number of indicators that are useful for monitoring the evolution and the impact of a pandemic like COVID-19 in terms of fatalities. Excess mortality is considered a better indicator for monitoring the scale of the pandemic and making comparisons.Footnote 1Footnote 2 Excess mortality refers to the "mortality above what would be expected based on the non-crisis mortality rate in the population of interest."Footnote 3 Excess mortality also encompasses collateral impacts of the pandemic, such as deaths occurring because of the overwhelming of the health care system, or deaths avoided due to decreased air pollution or traffic.Footnote 4Footnote 5Footnote 6

Estimating excess deaths

There are a number of challenges associated with measures of excess deaths. The most important challenge is to properly estimate some level of expected deaths that would occur in non-Covid19 context as a comparison basis for the current counts of deaths.Footnote 1 Indeed, death is a statistically rare event, and important variations may be observed from year to year in the annual counts of deaths, in particular in the less populated provinces and in the territories. Moreover, yearly counts of deaths may be affected by changes in the composition of the population, in regard to age more particularly, and changes in mortality rates (e.g. improvement of mortality).

A second challenge is the difficulty to collect timely counts of deaths. In Canada, death data are collected by the provincial and territorial vital statistical offices. The capacity to provide death data to Statistics Canada in a timely manner varies greatly.Footnote 7 Moreover, it is possible that the pandemic imposes a burden on health care and other institutions that disturb the data collection process, although it could instead add pressure for accelerated collection. The incomplete coverage of the numbers of deaths makes it difficult to draw any conclusions on the extent of excess deaths in Canada that could be caused by the COVID-19 pandemic.

Beginning on May 13, 2020, Statistics Canada has been releasing provisional counts of excess deaths for 2020.Footnote 8 Although the data were published for transparency and as information to be tracked and updated regularly, the uncertainty associated with the baseline expected death counts and the incomplete coverage of the numbers of deaths made it difficult to draw conclusions on the extent of excess deaths in Canada that could be caused by the COVID-19 pandemic. Statistical models are used to obtain estimated death counts adjusted for incompleteness and to estimate baseline non-Covid mortality. Estimates of excess deaths are obtained by comparing adjusted counts with modeled baseline mortality for all weeks in 2020 up to July 4. A description of the models is provided in the next section.

Methodology

This section describes the distinct models used for estimation of baseline mortality and adjustment of observed death counts.

Estimating expected mortality

The model used to estimate the expected number of deaths is based on a quasi-Poisson regression model fit to weekly death count data. Adapted from an infectious disease detection algorithm developed by Farrington et al.,Footnote 9 which has been largely utilized in the context of mortality surveillance in recent yearsFootnote 10. Later modifications to the algorithm, originally implemented by Noufaily et al.Footnote 11 and further expanded by Salmon et al.,Footnote 12 that aim at addressing certain limitations of the model were also adopted in this implementation.

The model was implemented in the R programming language with the use of the surveillance package,Footnote 12 and was applied to weeklyFootnote 13 death counts (all-cause) spanning a selected reference period of approximately four years (2016-2019). Historical counts are a combination of published death data from the Canadian Vital Statistics Death Database (2016-2018) and provisional death counts (2019) coming from the National Routing System (NRS). Estimates of expected deaths are produced for all weeks of 2020 up until the week ending July 4, 2020.

An overdispersed Poisson generalized linear model with a linear time trend and a seasonal factor is fit to the data. The seasonal component aims to represent the expected pattern across weeks that repeats from year-to-year, and consists of a zero-order spline term with 11 knots, representing 10 distinct periods within a given year.Footnote 14 The 10 periods are split between a single 7-week period corresponding to the current week being estimated and the 3 preceding and subsequent weeks, and 9 other 5-week periods corresponding to the rest of the year.

The model can be expressed using the following log-linear configuration:

log μt=α+βt+γc(t)

where μt is the expectation of the count in week t, β is the coefficient corresponding to the linear time trend, and γc(t) the seasonal factor for week t, with c(t) indicating the period in the year that week t belongs to. Footnote 12

The quasi-Poisson model relaxes the Poisson assumption that the variance must equal the mean. Instead, E(yt)=μt, and Var(yt)=ϕtμt, where the overdispersion parameter ϕ is estimated from the model using the formula:

ϕ^=max1n-p∑i=1nwi(yi-μ^i)2μ^i,1

where n is the number of weeks used in the baseline, and p the number of parameters in the model. A value of ϕ=1 implies no overdispersion (regular Poisson model), and ϕ<1 implies underdispersion (a rare occurrence, hence the condition on ϕ^). A weight w is assigned to each of the historical observations, based on the value of its standard deviation in an unweighted model. This reduces the influence of potential outliers on the estimation of the expected counts and corresponding prediction interval.

Finally, a 95% prediction interval is computed for the expected count in week t by assuming that the count follows a negative binomial distribution with mean μt and size parameter set to μt⁄(ϕt-1).

Adjusting death counts for incompleteness

Analysis of death by date (or week) of death is inevitably distorted by delays in reporting. This necessitates appropriate correction of the observed data to estimate the number of death that have occurred but not yet reported. The data received by Statistics Canada via the NRS contain information of day of death, date of report and some demographic information (e.g. age and sex).

Reporting delays are susceptible to change over time, and this is all the more true in a time of a pandemic. For this reason, the model estimates adjustment factors that are based on recent data, and uses different period for weeks that are during the pandemic and those preceding it. Weekly counts of deaths that occurred between December 29, 2019 and March 22, 2020 were adjusted based on the distribution of reporting delays estimated from death records received prior to March 22, 2020. Deaths counts for weeks between March 22 and July 4 were adjusted based on reporting delays observed between March 22 and August 7. In some jurisdictions, the level of data completeness of death records can be very low for the most recent weeks. Weekly adjusted counts are provided only for weeks where the estimated coverage rates satisfy a minimum threshold.Footnote 15

The method used for adjusting observed death counts was originally developed by Brookmeyer and DamianoFootnote 16 to model daily counts. It was adapted here to work on a weekly scale. The model was implemented in R programming language using code sent by the authors.Footnote 17 The number of deaths occurring on week t and reported in week t+d (i.e. with a delay of d weeks), Ytd, is modeled using the following Poisson regression model:

LOG(E(Ytd))=αt+βd

where αt represents the log-transformed preliminary reported count in week t, and βd is the term representing the adjustment for underreporting. Note that the right side of the equation is in a log scale so the underreporting adjustment can be seen as a multiplicative adjustment in the original scale. The adjusted number of deaths occurring in week t is then the observed deaths count divided by the estimated probability that the lag on the death being reported is less or equal to a maximum of x weeks, with x+t being the last observable week to report deaths, i.e. x is the maximum delay time in the dataset minus the week of the death t:

Adjusted number of deaths (t)=∑d=0dmax-tYtdP(delay≤dmax-t|time=t, delay≤dmax)

Estimating excess mortality

To calculate the weekly number of excess deaths, the baseline number of deaths in the absence of the pathogen (COVID-19) is subtracted from the observed (and adjusted for reporting delay) number of deaths for the period of interest. The method involves the following steps:

  • Quasi Poisson models are fit to the weekly death counts at the provincial and territorial level from January 1st 2016 to January 1st 2020 to obtain a baseline measure of the expected mortality.
  • Baseline deaths are projected for year 2020 until July 4.
  • Adjust for reporting delays the weekly counts of deaths that occurred from December 29, 2019 to March 22, 2020 based on the distribution of reporting delays estimated from death records received prior to March 22, 2020.
  • Adjust for reporting delays the weekly counts of deaths that occurred from the week ending March 22 to July 4 based on reporting delays observed between March 22 and August 7.
  • Apply an additional correction to the counts and prediction intervals for the period for March 22 to July 4. This correction factor is the ratio of the adjusted count for the week of March 22 based on the distribution of reporting delays estimated from death records received prior to March 22, 2020 to the unadjusted count for the same week.
  • Excess mortality is defined as the adjusted observed mortality minus the baseline for the period of interest.

The 95% prediction intervals surrounding estimates of excess deaths were computed by combining the variances from the two models. An empirical distribution of excess deaths is calculated by randomly pairing 10,000 estimates (replicates) from each model, as per the bootstrap method. The bounds of the prediction intervals represent quantiles of the empirical distribution. The method assumes independence between the two processes that are weekly mortality and collection of death records, but makes no assumption about the statistical distribution of excess deaths.

Validation

The computation of excess mortality requires the estimation of two greatly uncertain processes: how many deaths there should be in a given week, and how many deaths occurred that were not yet recorded at the time of estimation. The use of modelling for estimation of excess deaths aims at improving estimation but also, importantly, to reflect the uncertainty of these processes.

Validation of the models tend to show that they perform well in many regards. Expected counts tend to mimic the seasonal patterns typically observed during a year and follow the increase observed in past years (mainly due to population growth, particularly at old ages). However, because they captured over periods comprising several weeks, these seasonal patterns tend to be smoothed to some extent. For example, the application of time series models to weekly counts tend to produce more defined peaks, in particular in the month of January (likely due to influenza outbreaks). Another limitation has to do with the way prediction intervals are computed. In the model for estimating expected counts, the death counts are assumed to follow a negative binomial distribution, which is well adapted for modelling discrete counts data susceptible to present overdispersion. However, the bounds of the prediction intervals are defined as the quantiles of the negative binomial distribution, and thus do not reflect the variance due to parameter estimation. A better statistical representation would also account for uncertainty in parameter estimation.

The model for adjusting death counts was designed mainly for its capacity to capture recent trends in reporting delays. Experimentation with different time periods suggest that indeed, there have been changes in the pace at which deaths are registered in the provincial and territorial vital statistics database, at least in some provinces. However, the model assumes that there are no changes within the reference period considered. This is not guaranteed, in particular in a time of pandemic. Another limitation is that with the reference period is too short for capturing adequately potential seasonal patterns. The application of times series models to the data reveals the presence of some seasonal patterns in the coverage rates for some lags (number of days between date of death and report date). It is assumed that biases due to changes in reporting patterns are more important than those due to seasonality. Likewise, potential patterns of underreporting related to some specific days of the week, such as Sundays or holidays, were not considered.

Statistics Canada will continue to refine the methodology in an effort to better inform Canadians of the effects of the COVID-19 pandemic.

Response Rate for Sawmills, production of lumber (softwood and hardwood) by Geography

Table 1: Response Rate for Sawmills, production of lumber (softwood and hardwood) by Geography
Quantities produced (M.ft. b.m)
Geography Month
201901 201902 201903 201904 201905 201906 201907 201908 201909 201910 201911 201912
Canada 0.87 0.89 0.87 0.86 0.89 0.90 0.90 0.87 0.87 0.87 0.85 0.83
Newfoundland and Labrador 0.97 0.97 0.97 0.97 0.96 0.97 0.96 0.90 0.87 0.88 0.88 0.96
Prince Edward Island 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00
Nova Scotia 0.75 0.76 0.59 0.76 0.76 0.76 0.96 0.97 0.76 0.57 0.59 0.32
New Brunswick 0.95 0.96 0.92 0.93 0.93 0.93 0.93 0.89 0.88 0.91 0.54 0.80
Quebec 0.91 0.91 0.89 0.83 0.88 0.88 0.87 0.83 0.79 0.86 0.83 0.66
Ontario 0.80 0.79 0.78 0.79 0.75 0.79 0.79 0.78 0.77 0.76 0.80 0.77
Manitoba 0.89 0.81 0.80 0.81 0.81 0.86 0.89 0.89 0.88 0.83 0.85 0.87
Saskatchewan 0.99 0.75 0.99 0.99 1.00 0.99 0.71 0.66 0.73 0.73 0.73 0.74
Alberta 0.92 0.95 0.94 0.95 0.94 0.94 0.94 0.94 0.93 0.93 0.93 0.90
British Columbia 0.85 0.87 0.85 0.85 0.90 0.91 0.92 0.87 0.92 0.91 0.92 0.95
British Columbia Coast 0.95 0.91 0.91 0.91 0.94 0.95 0.93 0.87 0.94 0.92 0.93 0.92
British Columbia Interior 0.83 0.86 0.84 0.84 0.89 0.91 0.91 0.87 0.92 0.91 0.92 0.95
Northern Interior, British Columbia 0.88 0.88 0.80 0.80 0.86 0.92 0.92 0.89 0.92 0.93 0.93 0.98
Southern Interior, British Columbia 0.79 0.84 0.89 0.88 0.93 0.90 0.91 0.86 0.92 0.89 0.91 0.92

Data Visualization: An introduction

Catalogue number: 892000062020014

Release date: September 23, 2020 Updated: December 21, 2022

This video addresses the data visualization competency. By the end of this video, you should have a deeper understanding of data visualization and how it can be used to present data in an interesting and aesthetically pleasing way.

We will go over when it should be used, and give you some examples of the different types of data visualization techniques that exist.

Data journey step
Tell the Story
Data competency
  • Data visualisation
  • Storytelling
Audience
Basic
Suggested prerequisites
N/A
Length
10:54
Cost
Free

Watch the video

Data Visualization: An introduction - Transcript

(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Data Visualization: An introduction")

Data Visualization: An introduction

Welcome to part one of a multi part series on data visualization. This video will provide an introductory overview of data visualization, and how to use it to tell your story.

Learning goals

This video addresses the data visualization competency. By the end of this video, you should have a deeper understanding of data visualization, and how it can be used to present data in an interesting and aesthetically pleasing way.

We will go over when it should be used, and we will give you some examples of the different types of data visualization techniques that exist.

Steps of the data journey

This diagram is a visual representation of the data journey. From collecting the data to cleaning, exploring, describing and understanding the data, to analyzing the data and lastly to communicating with others the story the data tell.

Step 4: Tell the story

Data visualization can occur at different steps of the data journey, depending on what you're using it for. In this video, we'll be focusing primarily on how to present data in a way that helps tell the story.

Data Visualization

(Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)

Data visualization is the graphical representation of information and data. It is a combination of art and science as it uses tools such as charts, graphs and maps to make trends and patterns that might be hidden in a large data set much easier to understand.

Why use data visualization?

But how does data visualization make trends and patterns easier to understand?

Vision is such an important part of how we experience the world. Perhaps because it's how we've always survived. How we've found food, avoided threats, created art that preserves our culture and histories. And since the brain absorbs and processes visual information faster than any other stimuli, presenting information through graphics can be incredibly effective.

So it only makes sense that as technology has evolved, so would the way we present information we're trying to share with the world.

Presenting data

(4 images where, starting from the left, an apple pie, cherry pie, blueberry pie and "other" pie are sorted with a squinting face with tongue out emoji as a 5th image on the far right.)

For example, think about the following question: What is the most popular kind of pie? If you really wanted to know the most popular type of pie in your hometown, you might decide to conduct a survey.

This survey would ask everyone in town what kind of pie is their favourite. Apple? Cherry? Blueberry? Some other flavour? And finally, an option for people who really just don't like pie at all. Once you've acquired your data, there's several ways to communicate the results.

Option 1: Text

The first option is text. You could consider creating a written report describing the figures that read something like "of the 100 people surveyed, 40 preferred apple pie. 30 preferred blueberry and 20 preferred cherry. Additionally, five people chose a flavor other than those in the list, and five said they didn't like pie at all."

Option 2: Table

(Image of a table where the left and right columns lists the different pie flavours and the count of respondents preferring said flavour, respectively: "apple = 40";"bleuberry = 30";"Cherry = 20";"Other = 5";"I don't like pie = 5";"Total = 100".)

In this situation, where we're just trying to find out the most popular pie flavor. We might decide that reading a full analysis of the results is unnecessary.

This is where the option to receive the exact same results in a table, could be preferable. When reading a table, it's all about the numbers. Here we can clearly see that most people prefer apple pie without having to take the time to read through a lot of text.

So, a good thing to note here is that when you're trying to compare more than two numbers, you will probably want to look into presenting your data in a more visual way, rather than textual.

Option 3: Visual

(A series of images with 4 apple pies; 3 bleuberry pies; 2 cherry pies & half a pie for those who like other pies and the other half for those who do not like pies.)

A third way to present the results of our pie survey is without many words or numbers at all. Option three is where data visualization comes in. From this picture it's instantly clear that apple pie is the most popular.

Types of data visualization

(Simplified image of a series of different types of data visualizations: (left) Graphics; Charts; Maps; Tables; Pictographs; Infographics; Dashboards (Right).)

There are many different ways of presenting data visually, such as, graphs, charts, maps, tables pictographs, infographs and dashboards. On the next few slides will look at what each one is best at showing.

Scatter plot

(Text on screen: Showing relationship between two things)

(Image of a Scatter plot on display with the titltle on top:"Total revenue from of ice-cream sales, 2019 ($CAD)".The vertical(y) and horizontal(x) axis represent the proportion of the revenue ($CAN) and temperture (Celsius), respectively.)

A scatter plot is great for showing the relationship between two values. In this graph we can clearly see the relationship between temperature on the horizontal axis and ice cream sales on the vertical axis. We can see how ice cream revenues increased with increasing temperatures.

Line graph

(Text on screen: Showing trends through time)

(Image of a line graph on display with the titltle on top:"Canada's official poverty line".The vertical(y) and horizontal(x) axis represent the proportion of the population (%) and year (year), respectively.)

A line graph is a good way to show how something changes over time. This one shows how Canada's official poverty line has been declining in recent years from 12.1% in 2015 to 8.7% in 2018.

Bar Chart

(Text on screen: Showing a comparison between several things)

(Image of a bar chart on display with the titltle on top:"Cannabis use in the past three months by age, Canada - Fourth quarter 2019".The vertical(y) and horizontal(x) axis represent the proportion of cannabis users (%) and age group (year), respectively. The left most bar to the right most bar, represent the age groups: "15 to 24"; "25 to 34"; "35 to 44"; "45 to 54"; "55 to 64" and "65 and over".)

A bar chart is better when you want to compare different groups of things. Here we compare the use of cannabis among Canadians by age group. The chart clearly shows that cannabis use is higher among those in the younger age groups compared with older age groups.

Pie chart

(Text on screen: Showing the composition of a whole)

(Image of a circular pie chart tittled on the top: Six provinces cultivated "vinifera and french hybrid" grapes for winemaking in 2018. The pie chart is composed of 3 asymetric slices.)

A pie chart is the perfect tool for showing the composition of a whole, or the distribution of something. Here, we see that in 2018 Ontario produced more grapes for winemaking than all the other provinces combined.

Maps

(Text on Screen: Putting data into geographical context)

(Image of the map of Canada where each province has a different gradient of bleu representing the unemployment rate where the darker bleus represent a higher unemployment rate in percentage points. Dark regions are areas with no data.)

Here is an example of a map being used as data visualization. It shows how the job vacancy rate differs across provinces. The job vacancy rate for each province in Canada is indicated by the shading on the map.

Tables

(Text on Screen: Used to show many categories, and provide more detail and precision than many other data visualization methods)

(Image of a table where the left most column represents the age group; the middle and right major columns represent "All families with children" and "Total children in all families", respectively. Both major columns contain sub columns representing the years 2015; 2016 and 2017.)

Tables are used to show many categories and provide more detail and precision than many other data visualization methods. In this table we see the number of families with children compared to the total number of children in all families for different age ranges of children.

Pictographs

(Text on Screen: Simple but instantly interpretable)

((Reuse of the pie survey) A series of images with 4 apple pies; 3 bleuberry pies; 2 cherry pies & half a pie for those who like other pies and the other half for those who do not like pies.)

This data visualization from the pie example is a pictograph. A pictograph is the representation of data using images. This is one of the simplest ways to represent statistical data. The popularity of different kinds of pie is represented by the number of pies. In this pictograph, each pie represents 10 individuals. While a pictograph has very low precision, our brains interpret the message instantly.

Infographics

(Text on Screen: Used to tell a comprehensive data story)

(An image containing an infograph Titled: "Family matters - information on the splitting of householde tasks. Who does what ?". Infograph contains facts and conclusions on the subject mater.)

An infographic is several data visualizations put together to tell a more comprehensive data story. Typically, an infographic portrays the state of something at a particular point in time. Like a poster.

In this example, several data points are put together to tell a story about who does the chores in a family. From this infographic we learn that some chores are done equally by men and women, like dishes, shopping and organizing the social life. While laundry and meal prep are more likely to be done by women, outdoor work is most likely done by men.

Finally, the infographic reveals that the distribution of tasks depends on who's in the labor force at the time.

Dashboards

(Text on Screen: Used to inform business decisions and are updated at regular intervals)

(An image containing a dashboard where tables, charts and graphics to display several issues related to human resources)

A dashboard is several data visualizations put together, often to inform business decisions. Dashboards are usually updated regularly and show changes over time. The colour, size, and position of the individual graphics are used strategically to focus attention on different aspects.

This dashboard for example uses tables, charts and graphics to display information to manage human resources.

How to choose the right visualization

The right visualization depends on several factors.

What type of data do you have? Are their relationships in the data? Or are they changing over time? Are you making comparisons or showing the composition of something? And who's your audience? What story do you want to tell them? Are differences by geographic region important to them? How much precision do they want or need? Is your audience making business decisions based on the information you're sharing? Or, is it simply to inform?

On the previous slides you saw some different types of data visualizations and what each one can be used for.

Recap of Key points

(Text on Screen: Data visualization is the graphical representation of information and data.; Vision is an important part of how we experience the world.; There are many different ways of presenting data visually.)

In this video, you learned that data visualization is the graphical representation of information and data.

A picture truly is worth 1000 words. Just make sure you choose the right picture to accurately represent your data and effectively get your message across. Watch for more videos in this series featuring good practices for data visualization.

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The Data Journey: What you need to know for successful navigation

Catalogue number: 892000062020007

Release date: September 23, 2020 Updated: October 22, 2021

In this video you will learn about the steps and activities in the data journey, as well as the foundation supporting it.

No previous knowledge is required. The data journey represents the key stages of the data process. The journey is not necessarily linear. It is intended to represent the different steps and activities that could be undertaken to produce meaningful information from data. Not everyone who uses data will do all of these steps.

No previous knowledge is necessary.

Data journey step
Foundation
Data competency
  • Data discovery
  • Data management and organization
Audience
Basic
Suggested prerequisites
N/A
Length
04:37
Cost
Free

Watch the video

The Data Journey: What you need to know for successful navigation - Transcript

(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "The Data Journey: What you need to know for successful navigation")

Data 101: Data Journey

The training videos in this series are organized around a data journey. This video tells you what you need to know for successful navigation.

Learning goals

In this video you'll learn about the steps and activities in the data journey as well as the foundation supporting it.

No previous knowledge is required.

Steps of a data journey

(Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)

The data journey represents the key stages of the data process. The journey is not necessarily linear. It is intended to represent the different steps and activities that could be undertaken to produce meaningful information from data.

Not everyone who uses data will do all of these steps, for example. You might already have gathered and cleaned data ready for analysis. Therefore you might only need to do the last two steps.

Step 1: Define, find and gather

(Diagram of the Steps of the data journey with an emphasis on "Define, find, gather".)

(Text on screen: Showing relationship between two things)

The first step is to define the question you need to answer or data gap you need to fill. Next is to find the right data to answer that question, or fill that data gap. If such data doesn't exist, you may need to figure out a way to gather it, like through a new survey, for example. In this first step you will use one or more of the following competencies: data discover, data gathering and/or data management and organization.

Step 2: Explore, clean and describe

(Diagram of the Steps of the data journey with an emphasis on "Explore, clean, describe".)

Once you have defined the need and found the data, the next thing is to get to know it. If you're already familiar with the data, then you might know what to expect. On the other hand, if the data is new to you, then you should spend some time exploring the formats variables and looking for errors and missing values. It may be necessary to clean the data before using it for analysis. It is important to document what you found and what you did to clean the data.

The product at the end of this step is data ready for analysis. In this step you will use one or more of the following competencies: data cleaning and or data exploration.

Step 3: Analyse and model

(Diagram of the Steps of the data journey with an emphasis on "Analyze, model".)

If you were doing analysis to describe a phenomenon, draw conclusions about a population or make predictions about future events, then your data journey continues. The purpose of doing analysis and modeling is to use statistical techniques to turn the data into information to provide meaningful insights that address your previously determined information needs. In this step, you'll use one or more of the following competencies: data analysis, data modeling and/or evaluating decisions based on data.

Step 4: Tell the story

(Diagram of the Steps of the data journey with an emphasis on "Tell the story".)

The statistical information that comes from analysis and modeling is easier to digest if it is presented in some sort of story. It could be a research paper, an infographic, a briefing for management, or some combination of these and other data presentation methods. In this step, you'll use one or more of the following competencies: data interpretation, data visualization and/or storytelling.

Build your data journey on a solid foundation

(Diagram of the Steps of the data journey. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)

In order to successfully follow the steps of the data journey, it is essential to build your work on a solid foundation of stewardship, metadata, standards and quality.

Stewardship encompases all activities to govern, safeguard and protect data.

Metadata should describe all the processing and manipulation that the data has undergone.

Standard methods, practices and classifications should be applied throughout.

Quality should be proactively managed throughout the process and relevant quality indicators should accompany all deliverables.

Recap of key points

The data journey steps are: defined, find, gather; explore clean, describe; analyzing, model, and tell the story. Not everyone who uses data will do all these steps themselves. For example, you might get already gathered and clean data ready for analysis. The data journey is supported throughout by a foundation of stewardship, metadata, standards and quality.

Further learning

You are welcome to watch the videos in any order you choose. If you're not sure where to go next, we recommend Types of Data and Gathering Data.

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Types of Data: Understanding and exploring data

Catalogue number: 892000062020004

Release date: September 23, 2020 Updated: November 25, 2021

In this video, you will learn about data and statistical information, and explore the different types of data. After watching this video, you will be able to identify categorical and quantitative data, nominal and ordinal data, and discrete and continuous data.

Data journey step
Explore, clean, describe
Data competency
Data gathering
Audience
Basic
Suggested prerequisites
N/A
Length
10:58
Cost
Free

Watch the video

Types of Data: Understanding and exploring data - Transcript

(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Data types")

Types of data: Understanding and exploring data

It's important to define the different types of data and understand them in order to choose the appropriate method for analyzing data and presenting the results.

Learning goals

In this video, you will learn about data and statistical information, and explore the different types of data. After completing this video, you will be able to identify categorical and quantitative data, nominal and ordinal data, and discrete and continuous data. This video is intended for learners who want to acquire a basic understanding of data's concepts and types.

Steps of a data journey

(Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)

This diagram is a visual representation of the data journey from collecting the data to cleaning, exploring, describing and understanding the data to analyzing the data, and Lastly to communicating with others the story the data tell.

Step 2: Explore, clean and describe

(Diagram of the Steps of the data journey with an emphasis on Step 2 - explore, clean, describe.)

Exploring the different types of data is part of the explore, clean and describe step of the data journey. Understanding the various data types will help with the analyze and model steps.

Difference between data and statistical information: Data

Data are the raw material for making information. It can be, for example, in the form of numbers, texts, observations, or recordings. Data can be structured, meaning that they are organized into predefined categories or concepts such as lists, tables, datasets, databases or spreadsheets.

Data can also be unstructured, which means they're not organized. Unstructured data need to be processed or parsed to become structured before any further work can be done on them.

A paragraph of text is an example of unstructured data, since the main ideas have to be extracted or the phrases have to be parsed into smaller segments to use the text as data.

Satellite images are another example of unstructured data. The images have to be interpreted, encoded, such as type of crop or type of building.

Difference between data and statistical information: Statistical information

When we apply statistical methods to data, we produce statistical information such as means, totals, ratios, percentiles, frequency distributions, and parameter estimates. Data have meaning and value, but they're difficult to identify. Statistical methods are a way of summarizing the data so that the meaning becomes clear.

Turning data into statistical information

Statistical methods are applied to data to derive meaning or find relationships. The end product is statistical information which is interpreted and used to increase knowledge about the topic in question.

Data types

(Image of a tree diagram of the different types of data. the root of the tree diagram is "data" that branches out into "categorical"and "quantitative" data. Categorical data branches out into "nominal" and "ordinal" categorical data. Quantitative data branches out into "discrete" and "continuous" quantitative data.)

Data can be divided into 2 main categories. Categorical and quantitative. Categorical data can be further subdivided into nominal and ordinal data. Quantitative data can be discrete or continuous and are also known as numerical data. These concepts are explored further in the next few slides.

Categorical data

Categorical data represent characteristics such as gender languages, spoken type of diseases or clothing sizes.

For example, the languages spoken by a particular person could be French, English, German and Spanish. The categories are referred to as classes or classifications. Every possible value for a characteristic should be in one and only one category.

Categorical data: Nominal

When the categories have no inherent order, the data are called nominal. The data values in this situation are labels.

Examples of categories are types of diseases or languages spoken. Nominal data can be analyzed in summarized using frequencies, proportions, percentages, cross tabulations, and the mode, and they can be visualized using pie charts and bar graphs.

Categorical data: Ordinal

Ordinal values represent categorical data that can be ordered. Ordinal data are very similar to nominal data, but as the name implies, order is important. The categories follow some logical order such as size is categorized as small, medium and large. Similarly to nominal data, ordinal data can be analyzed, summarized and visualized. However, ordinal data can also be described using percentiles, medians and modes. If the ordinal data are numeric, interquartile ranges can also be used.

For example, you could look at the interquartile range of exam scores that are in percentages and arranged from lowest to highest, but it would not make any sense to try to find the interquartile range of clothing sizes that go from extra small to extra large. For an example of when to use interquartile range, check out the video on exploring measures of dispersion.

Quantitative data

Quantitative data, also called numerical data, can be either discrete or continuous. When the data values are distinct and separate, and they can take on certain values only, they're called discrete data. Discrete data can be only counted, not measured.

For example, the number of sheep on a farm. continuous data, on the other hand, represent measurements, not counts. Continuous data can take on an infinite number of values, but for practical reasons continuous data are measured using a discrete scale. Distance is an example of continuous data. It is continuous and that you could keep adding or removing small and the distance would change. However, centimeters or kilometers are used to measure distance on a discrete scale.

Exemple: How old are the people in a community ?

Let's look at an example of working with different types of data. Let's say we want to know how old the people in a community are so that we can plan appropriate services and activities for them. In our example we have the birth dates of the people in a particular community. Because time can be divided in an infinite number of ways, for example, every second or millisecond it is a continuous variable. However, for practical reasons, a hospital usually records the year, month, day, hour and minute of birth. For administrative purposes, we usually just report the year, month and day of birth, which means we're using a discrete representation of a continuous variable. To determine someone's age from their date of birth, we calculate the time between the current date and their date of birth. For convenience sake, let's round their age to the nearest year, which is also a discrete value.

If our community is very small, we could look at all the ages on a list and be able to interpret them. However, if there are a lot of people, it would be very hard to look at a list of ages and say anything meaningful about them, especially if they were in no particular order. When converting age data into statistical information, it's common practice to group the ages into categories. Let's use ranges of 10 years for our example. Now the data are ordinal because there is a particular order to the age categories.

Exemple: How old are the people in a community ?

(Image of a table where the left column called "Age category" representing the different age groups and the right column represents the "count of people". The following is the table content:

  • 0 to 10 years: 5
  • 11 to 20 years: 12
  • 21 to 30 years: 25
  • 31 to 40 years: 30
  • 41 to 50 years: 23
  • 51 to 60 years: 14
  • 61 to 70 years: 3
  • 71 to 80 years: 0
  • 81 years and older: 0)

Let's use the same example. Now that we have age categories, we want to know how many people are in each category. The statistical method we apply to the ordinal data produces a frequency distribution which is shown in the table on the right. Now it becomes quite clear that the community is relatively young. This table is statistical information that can be used by community planners and organizers to plan services and activities that are age appropriate for the community members. It's much easier to interpret the statistical information in this table than it would be to interpret a long list of birth dates.

Quantitative data: Be careful with Zero

There's one very important value to be careful with, in quantitative data. The value of 0, sometimes 0, means there is none of something. For example, zero apples means there are no apples. Sometimes 0 does mean something. For example, zero degrees Celsius means it's cold outside, not that there is no temperature. In some cases, negative values are valid. For example, if I have -$5, it means I owe $5. However, sometimes negative values are not valid. For example, there can't be minus five sheep on a farm. Be mindful of the meaning of 0 when working with quantitative data.

Quantitative data: Basic statistics

There are many basic statistics that can be used with Quantitative data. In fact, all of the basic statistics shown on this slide can be used in a meaningful way with quantitative data.

(Text on screen: Basic statistics include counts, ranks, means, totals and varainces. Other basic statistics include: Proportions, frequencies and cross-tabulations; mode, median, ranks and percentiles; means totals and variances.)

Data types

Remember that data can be categorical or quantitative. Categorical data can be nominal, labels only, or ordinal, having a particular order. Quantitative data can be discreet things we count or continuous, which are things we measure. The next slide provides examples of different types of data and you will have to determine the data type: nominal, ordinal, discrete or continuous.

Guided practice: What is the data type ?

Pause the video here and take the time you need to determine whether each example is nominal, ordinal, discrete or continuous. Continue to play the video to see the answers.

(Image on screen where 4 different examples need to be answered: 1) Names of instruments in an orchestra; 2) Temperature outside right now; 3) Number of pounds gained over the holidays; 4) Rank in a household based on age.)

Do you agree with our suggestions ?

The names of instruments in an orchestra are categorical, nominal data because they can be in any order. Although violin players would probably say they should come first.

Temperature is quantitative continuous data because it can be measured in small increments. We use degrees Celsius for convenience.

Number 3 is a trick question. Weight is measured in pounds in kilograms, which are continuous, but the question asked for the number of pounds gained, which is a count, meaning that these are quantitative discrete data.

Lastly, a person's rank in a household by age is categorical ordinal data because rank by age means putting households in order from youngest to oldest. How did you do?

Summary of key points

Data can be in the form of numbers, texts, observations or recordings. Statistical methods are applied to data to produce statistical information. Data can be nominal, which are categories, or ordinal which are categories in a particular order. Numerical or quantitative data can be continuous, in which case we need to take measurements or discrete, in which case we need to count. We also learned to be careful of the value of zero, which could mean different things depending on the nature of the data.

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What is Data? An Introduction to Data Terminology and Concepts

Catalogue number: 892000062020006

Release date: September 23, 2020 Updated: October 25, 2021

The data terminology and concepts covered in this video are datasets, databases, data protection, data variables, micro and macro data, and statistical information.

No previous knowledge is necessary.

Data journey step
Foundation
Data competency
Data awareness
Audience
Basic
Suggested prerequisites
N/A
Length
06:38
Cost
Free

Watch the video

What is Data? An Introduction to Data Terminology and Concepts - Transcript

(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "What is Data? An Introduction to Data Terminology and Concepts")

What's data? An introduction to data terminology and concepts

This video will introduce some basic terminology and concepts related to data.

Learning goals

The data terminology and concepts covered in this video are datasets, databases, data protection, data variables, micro and macro data, and statistical information. No previous knowledge is necessary.

Steps of a data journey

(Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)

The data journey represents the steps data goes through on its way to telling a story. We will try to answer the question what is data by considering data at these different steps. First, let's consider what is data in the context of finding, gathering and protecting it.

What is data?

Data are facts or figures about an object or phenomenon. Objects simply exist and phenomena happen. People create data. We measure, count, observe, and describe the world around us. We record what we find using symbols and images. This is what data is.

Where does data come from?

Where do data come from? Data come from everywhere. For example, doctors gather data about our health and wellbeing, stores gather data about our purchases, surveys gather data about our habits, and scientists gather data about climate conditions such as temperature and wind speed. This is sometimes referred to as Earth observation data. These are just a few examples in the digital age, data is literally all around us.

How is data organized?

Data can be organized in structured formats such as tables, graphs, or Maps, or a data can also be unstructured, for example text and documents.

Data protection

Custodians of data have a responsibility to be good stewards and protect the privacy, confidentiality and security of personal identifiable information.

Personal identifiable information includes any information that could directly or indirectly identify an individual person, business, or organization.

Step 2: Explore, clean and describre

(Diagram of the Steps of the data journey with an emphasis on Step 2 - explore, clean, describe.)

Now let's consider what is data at the next step in the data journey. Once we have data were curious to explore it. If we find errors in the data, we try to correct them. What does data look like for this to happen?

Datasets and databases

Data is often organized in a table with rows and columns. In electronic format, this is known as a dataset. A dataset that is organized for a particular purpose, for example, hospital registrations is called a database. There are software packages for managing databases, such as Oracle, SQL and Microsoft Access.

relational databases

A relational database is an organized collection of datasets that relate to each other through key values.

For example, a relational database about the school system could have one data set of a list of schools. Another data set of classes within schools, and another data set of students within classes.

There's a way to connect up all the datasets in a relational database. In this example, an identification variable for the school could be on all three datasets so that you could find all the classes and all the students related to a particular school.

What's inside a dataset?

The actual data inside a dataset or database are arranged in variables. Some of the variables represent the measurements, counts, observations, or descriptions that we talked about earlier. Other variables served to identify to whom or what those measurements counts, observations or descriptions pertain data where one record or row represents one unit of observation is called Micro data. It's highly recommended to explore and clean micro data before doing any sort of analysis on it or using it for any other purpose.

To do this, basic statistical methods are applied to Microdata variables. For more information, see the videos on central tendency and dispersion.

Step 3: Analyze and model

(Diagram of the Steps of the data journey with an emphasis on Step 3 - analyze, model.)

To discover relationships between variables or to look for trends through time, we do analysis on cleaned Microdata. Other terms for doing analysis are modeling, deriving inference and data analytics. To learn more about data analysis, see the analysis one or one video series.

Different states of data

Here's a handy way to summarize the different states that data can be in. Microdata refers to a data set where one record represents one unit of observation. Microdata are the basic building block whether you're using data to provide services, enforce regulations, answer research questions, or build policy. Macro data refers to a data set where records have been rolled up or aggregated together.

Statistical analysis or data analytics can be performed uncleaned, microdata, or macro data. Metadata is the documentation or information that provides context It makes it easier to use the data appropriately.

Step 4: tell the story

(Diagram of the Steps of the data journey with an emphasis on Step 4 - Tell the story.)

Applying statistical analysis or a data analytics to data is a way to produce statistical information. The last step in the data journey is to tell the story that emerges from statistical information.

Statistical information

Statistical information looks quite different from the original data on which it is based. It has been synthesized and transformed to reveal meaning or intelligence that is difficult to discern in the micro data. The statistical information that comes from analysis and modeling is easier for people to understand if it's presented in some sort of story. The story could be told through a research paper, an infographic a media article, a data visualization, or some combination of these and other data presentation methods.

Recap of key points

Data are facts or figures about an object or phenomenon. Data variables are stored in a dataset. Custodians have a responsibility to protect the privacy, confidentiality and security of identifiable data. Clean micro or macro data is analyzed using statistical analysis or data analytics to produce statistical information. The data story is told from the statistical information.

Recap of key points

To learn more about data, check out the videos called the data journey types of data and gather data.

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Why are we conducting this survey?

This survey collects data on capital and repair expenditures in Canada. The information is used by federal and provincial government departments and agencies, trade associations, universities and international organizations for policy development and as a measure of regional economic activity.

Your information may also be used by Statistics Canada for other statistical and research purposes.

Your participation in this survey is required under the authority of the Statistics Act.

Other important information

Authorization to collect this information

Data are collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.

Confidentiality

By law, Statistics Canada is prohibited from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent, or as permitted by the Statistics Act. Statistics Canada will use the information from this survey for statistical purposes only.

Record linkages

To enhance the data from this survey and to reduce the reporting burden, Statistics Canada may combine the acquired data with information from other surveys or from administrative sources.

Data-sharing agreements

To reduce respondent burden, Statistics Canada has entered into data-sharing agreements with provincial and territorial statistical agencies and other government organizations, which have agreed to keep the data confidential and use them only for statistical purposes. Statistics Canada will only share data from this survey with those organizations that have demonstrated a requirement to use the data.

Section 11 of the Statistics Act provides for the sharing of information with provincial and territorial statistical agencies that meet certain conditions. These agencies must have the legislative authority to collect the same information, on a mandatory basis, and the legislation must provide substantially the same provisions for confidentiality and penalties for disclosure of confidential information as the Statistics Act. Because these agencies have the legal authority to compel businesses to provide the same information, consent is not requested and businesses may not object to the sharing of the data.

For this survey, there are Section 11 agreements with the provincial and territorial statistical agencies of Newfoundland and Labrador, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, British Columbia, and the Yukon.

The shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.

Section 12 of the Statistics Act provides for the sharing of information with federal, provincial or territorial government organizations. Under Section 12, you may refuse to share your information with any of these organizations by writing a letter of objection to the Chief Statistician, specifying the organizations with which you do not want Statistics Canada to share your data and mailing it to the following address:

Chief Statistician of Canada
Statistics Canada
Attention of Director, Enterprise Statistics Division
150 Tunney's Pasture Driveway
Ottawa, Ontario
K1A 0T6

You may also contact us by email at statcan.esdhelpdesk-dsebureaudedepannage.statcan@statcan.gc.ca or by fax at 613-951-6583

For this survey, there are Section 12 agreements with the statistical agencies of Prince Edward Island, the Northwest Territories and Nunavut as well as Environment and Climate Change Canada, Infrastructure Canada, the Canada Energy Regulator and Natural Resources Canada.

For agreements with provincial and territorial government organizations, the shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province or territory.

Business or organization and contact information

1. Verify or provide the business or organization's legal and operating name and correct where needed.

Note: Legal name modifications should only be done to correct a spelling error or typo.

Legal Name

The legal name is one recognized by law, thus it is the name liable for pursuit or for debts incurred by the business or organization. In the case of a corporation, it is the legal name as fixed by its charter or the statute by which the corporation was created.

Modifications to the legal name should only be done to correct a spelling error or typo.

To indicate a legal name of another legal entity you should instead indicate it in question 3 by selecting 'Not currently operational' and then choosing the applicable reason and providing the legal name of this other entity along with any other requested information.

Operating Name

The operating name is a name the business or organization is commonly known as if different from its legal name. The operating name is synonymous with trade name.

Legal name

Operating name (if applicable)

2. Verify or provide the contact information of the designated business or organization contact person for this questionnaire and correct where needed.

Note: The designated contact person is the person who should receive this questionnaire. The designated contact person may not always be the one who actually completes the questionnaire.

First name

Last name

Title

Preferred language of communication

  • English
  • French

Mailing address (number and street)

City

Province, territory or state

Postal code or ZIP code

Country

  • Afghanistan
  • Åland Islands
  • Albania
  • Algeria
  • American Samoa
  • Andorra
  • Angola
  • Anguilla
  • Antarctica
  • Antigua and Barbuda
  • Argentina
  • Armenia
  • Aruba
  • Australia
  • Austria
  • Azerbaijan
  • Bahamas
  • Bahrain
  • Bangladesh
  • Barbados
  • Belarus
  • Belgium
  • Belize
  • Benin
  • Bermuda
  • Bhutan
  • Bolivia
  • Bonaire, Sint Eustatius and Saba
  • Bosnia and Herzegovina
  • Botswana
  • Bouvet Island
  • Brazil
  • British Indian Ocean Territory
  • Brunei Darussalam
  • Bulgaria
  • Burkina Faso
  • Burma (Myanmar)
  • Burundi
  • Cambodia
  • Cameroon
  • Canada
  • Cape Verde
  • Cayman Islands
  • Central African Republic
  • Chad
  • Chile
  • China
  • Christmas Island
  • Cocos (Keeling) Islands
  • Colombia
  • Comoros
  • Congo, Republic of the
  • Congo, The Democratic Republic of the
  • Cook Islands
  • Costa Rica
  • Côte d'Ivoire
  • Croatia
  • Cuba
  • Curaçao
  • Cyprus
  • Czech Republic
  • Denmark
  • Djibouti
  • Dominica
  • Dominican Republic
  • Ecuador
  • Egypt
  • El Salvador
  • Equatorial Guinea
  • Eritrea
  • Estonia
  • Ethiopia
  • Falkland Islands (Malvinas)
  • Faroe Islands
  • Fiji
  • Finland
  • France
  • French Guiana
  • French Polynesia
  • French Southern Territories
  • Gabon
  • Gambia
  • Georgia
  • Germany
  • Ghana
  • Gibraltar
  • Greece
  • Greenland
  • Grenada
  • Guadeloupe
  • Guam
  • Guatemala
  • Guernsey
  • Guinea
  • Guinea-Bissau
  • Guyana
  • Haiti
  • Heard Island and McDonald Islands
  • Holy See (Vatican City State)
  • Honduras
  • Hong Kong Special Administrative Region
  • Hungary
  • Iceland
  • India
  • Indonesia
  • Iran
  • Iraq
  • Ireland, Republic of
  • Isle of Man
  • Israel
  • Italy
  • Jamaica
  • Japan
  • Jersey
  • Jordan
  • Kazakhstan
  • Kenya
  • Kiribati
  • Korea, North
  • Korea, South
  • Kosovo
  • Kuwait
  • Kyrgyzstan
  • Laos
  • Latvia
  • Lebanon
  • Lesotho
  • Liberia
  • Libya
  • Liechtenstein
  • Lithuania
  • Luxembourg
  • Macao Special Administrative Region
  • Macedonia, Republic of
  • Madagascar
  • Malawi
  • Malaysia
  • Maldives
  • Mali
  • Malta
  • Marshall Islands
  • Martinique
  • Mauritania
  • Mauritius
  • Mayotte
  • Mexico
  • Micronesia, Federated States of
  • Moldova
  • Monaco
  • Mongolia
  • Montenegro
  • Montserrat
  • Morocco
  • Mozambique
  • Namibia
  • Nauru
  • Nepal
  • Netherlands
  • New Caledonia
  • New Zealand
  • Nicaragua
  • Niger
  • Nigeria
  • Niue
  • Norfolk Island
  • Northern Mariana Islands
  • Norway
  • Oman
  • Pakistan
  • Palau
  • Panama
  • Papua New Guinea
  • Paraguay
  • Peru
  • Philippines
  • Pitcairn
  • Poland
  • Portugal
  • Puerto Rico
  • Qatar
  • Réunion
  • Romania
  • Russian Federation
  • Rwanda
  • Saint Barthélemy
  • Saint Helena
  • Saint Kitts and Nevis
  • Saint Lucia
  • Saint Martin (French part)
  • Saint Pierre and Miquelon
  • Saint Vincent and the Grenadines
  • Samoa
  • San Marino
  • Sao Tome and Principe
  • Sark
  • Saudi Arabia
  • Senegal
  • Serbia
  • Seychelles
  • Sierra Leone
  • Singapore
  • Sint Maarten (Dutch part)
  • Slovakia
  • Slovenia
  • Solomon Islands
  • Somalia
  • South Africa, Republic of
  • South Georgia and the South Sandwich Islands
  • South Sudan
  • Spain
  • Sri Lanka
  • Sudan
  • Suriname
  • Svalbard and Jan Mayen
  • Swaziland
  • Sweden
  • Switzerland
  • Syria
  • Taiwan
  • Tajikistan
  • Tanzania
  • Thailand
  • Timor-Leste
  • Togo
  • Tokelau
  • Tonga
  • Trinidad and Tobago
  • Tunisia
  • Turkey
  • Turkmenistan
  • Turks and Caicos Islands
  • Tuvalu
  • Uganda
  • Ukraine
  • United Arab Emirates
  • United Kingdom
  • United States
  • United States Minor Outlying Islands
  • Uruguay
  • Uzbekistan
  • Vanuatu
  • Venezuela
  • Viet Nam
  • Virgin Islands, British
  • Virgin Islands, United States
  • Wallis and Futuna
  • West Bank and Gaza Strip (Palestine)
  • Western Sahara
  • Yemen
  • Zambia
  • Zimbabwe

Email address

Telephone number (including area code)

Extension number (if applicable)
The maximum number of characters is 10.

Fax number (including area code)

3. Verify or provide the current operational status of the business or organization identified by the legal and operating name above.

  • Operational
  • Not currently operational
    • Why is this business or organization not currently operational?
      • Seasonal operations
      • Ceased operations
      • Sold operations
      • Amalgamated with other businesses or organizations
      • Temporarily inactive but will re-open
      • No longer operating due to other reasons
    • When did this business or organization close for the season?
      • Date
    • When does this business or organization expect to resume operations?
      • Date
    • When did this business or organization cease operations?
      • Date
    • Why did this business or organization cease operations?
      • Bankruptcy
      • Liquidation
      • Dissolution
      • Other
    • Specify the other reasons why the operations ceased
    • When was this business or organization sold?
      • Date
    • What is the legal name of the buyer?
    • When did this business or organization amalgamate?
      • Date
    • What is the legal name of the resulting or continuing business or organization?
    • What are the legal names of the other amalgamated businesses or organizations?
    • When did this business or organization become temporarily inactive?
      • Date
    • When does this business or organization expect to resume operations?
      • Date
    • Why is this business or organization temporarily inactive?
    • When did this business or organization cease operations?
      • Date
    • Why did this business or organization cease operations?

4. Verify or provide the current main activity of the business or organization identified by the legal and operating name above.

Note: The described activity was assigned using the North American Industry Classification System (NAICS).

This question verifies the business or organization's current main activity as classified by the North American Industry Classification System (NAICS). The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS , are suitable for the analysis of production-related issues such as industrial performance.

The target entity for which NAICS is designed are businesses and other organizations engaged in the production of goods and services. They include farms, incorporated and unincorporated businesses and government business enterprises. They also include government institutions and agencies engaged in the production of marketed and non-marketed services, as well as organizations such as professional associations and unions and charitable or non-profit organizations and the employees of households.

The associated NAICS should reflect those activities conducted by the business or organizational units targeted by this questionnaire only, as identified in the 'Answering this questionnaire' section and which can be identified by the specified legal and operating name. The main activity is the activity which most defines the targeted business or organization's main purpose or reason for existence. For a business or organization that is for-profit, it is normally the activity that generates the majority of the revenue for the entity.

The NAICS classification contains a limited number of activity classifications; the associated classification might be applicable for this business or organization even if it is not exactly how you would describe this business or organization's main activity.

Please note that any modifications to the main activity through your response to this question might not necessarily be reflected prior to the transmitting of subsequent questionnaires and as a result they may not contain this updated information.

The following is the detailed description including any applicable examples or exclusions for the classification currently associated with this business or organization.

Description and examples

  • This is the current main activity
  • This is not the current main activity

Provide a brief but precise description of this business or organization's main activity

e.g., breakfast cereal manufacturing, shoe store, software development

Main activity

5. You indicated that is not the current main activity.

Was this business or organization's main activity ever classified as: ?

  • Yes
  • No

When did the main activity change?

  • Date

Select this business or organization's activity sector (optional)

  • Farming or logging operation
  • Construction company or general contractor
  • Manufacturer
  • Wholesaler
  • Retailer
  • Provider of passenger or freight transportation
  • Provider of investment, savings or insurance products
  • Real estate agency, real estate brokerage or leasing company
  • Provider of professional, scientific or technical services
  • Provider of health care or social services
  • Restaurant, bar, hotel, motel or other lodging establishment
  • Other sector

Reporting period information

1. What are the start and end dates of this organization's 2020 fiscal year?

Note: For this survey, the end date should fall between April 1, 2020 and March 31, 2021 .

Here are twelve common fiscal periods that fall within the targeted dates:

  • May 1, 2019 to April 30, 2020
  • June 1, 2019 to May 31, 2020
  • July 1, 2019 to June 30, 2020
  • August 1, 2019 to July 31, 2020
  • September 1, 2019 to August 31, 2020
  • October 1, 2019 to September 30, 2020
  • November 1, 2019 to October 31, 2020
  • December 1, 2019 to November 30, 2020
  • January 1, 2020 to December 31, 2020
  • February 1, 2020 to January 31, 2021
  • March 1, 2020 to February 28, 2021
  • April 1, 2020 to March 31, 2021 .

Here are other examples of fiscal periods that fall within the required dates:

  • September 18, 2019 to September 15, 2020 ( e.g. , floating year-end)
  • June 1, 2020 to December 31, 2020 ( e.g. , a newly opened business).

Fiscal Year Start date

Fiscal Year-End date

Reporting period information

2. What is the reason the reporting period does not cover a full year?

Select all that apply.

Seasonal operations

New business

Change of ownership

Temporarily inactive

Change of fiscal year

Ceased operations

Other reason - specify:

Other reason - specify:

Additional reporting instructions

3. Throughout this questionnaire, please report financial information in thousands of Canadian dollars.

For example, an amount of $763,880.25 should be reported as:

CAN$ '000

I will report in the format above

What are Capital Expenditures?

Capital Expenditures are the gross expenditures on fixed assets for use in the operations of your organization or for lease or rent to others. Gross expenditures are expenditures before deducting proceeds from disposals, and credits (capital grants, donations, government assistance and investment tax credits).

Fixed assets are also known as capital assets or property, plant and equipment. They are items with a useful life of more than one year and are not purchased for resale but rather for use in the entity's production of goods and services. Examples are buildings, vehicles, leasehold improvements, furniture and fixtures, machinery, and computer software.

Include:

  • cost of all new buildings, engineering, machinery and equipment which normally have a life of more than one year and are charged to fixed asset accounts
  • modifications, additions and major renovations
  • capital costs such as feasibility studies, architectural, legal, installation and engineering fees
  • subsidies
  • capitalized interest charges on loans with which capital projects are financed
  • work done by own labour force
  • additions to capital work in progress.

Exclude:

  • transfers from capital work in progress (construction-in-progress) to fixed assets accounts
  • assets associated with the acquisition of companies
  • property developed for sale and machinery or equipment acquired for sale (inventory).

How to Treat Leases:

Include:

  • assets acquired as a lessee through either a capital or financial lease
  • assets acquired for lease to others as an operating lease.

Exclude:

  • transfers from capital work in progress (construction-in-progress) to fixed assets accounts
  • assets associated with the acquisition of companies
  • property developed for sale and machinery or equipment acquired for sale (inventory)

Capital Expenditures - Preliminary Estimate 2020

4.  From January 1, 2020 to December 31, 2020 , what are this organization's preliminary estimates for capital expenditures?

Report your best estimate of capital expenditures expected for the full year.

Include:

  • the gross expenditures (including subsidies received) on fixed assets for use in the operations of your organization
  • all capital costs such as feasibility studies, architectural, legal, installation and engineering fees as well as work done by your own labour force
  • additions to work in progress
  • leasehold improvements with the assets being leased ( e.g. , office leasehold with non-residential construction).

Exclude asset transfers and business acquisitions.

Imported used fixed assets should be reported under New assets including financial leases.

Purchase of Used Canadian Assets

Definition: Used fixed assets may be defined as existing buildings, structures or machinery and equipment which have been previously used by another organization in Canada that you have acquired during the time period being reported on this questionnaire.

Explanation: The objective of our survey is to measure gross annual new acquisitions to fixed assets separately from the acquisition of gross annual used fixed assets in the Canadian economy as a whole.

Hence, the acquisition of a used fixed Canadian asset should be reported separately since such acquisitions would not change the aggregates of our domestic inventory of fixed assets, it would simply mean a transfer of assets within Canada from one organization to another.

Imports of used assets, on the other hand, should be included with the new assets because they are newly acquired for the Canadian economy.

Work in Progress:

Work in progress represents accumulated costs since the start of capital projects which are intended to be capitalized upon completion.

Typically capital investment includes any expenditure on an asset in which its' life is greater than one year. Capital items charged to operating expenses are defined as expenditures which could have been capitalized as part of the fixed assets, but for various reasons, have been charged to current expenses.

Land

Capital expenditures for land should include all costs associated with the purchase of the land that are not amortized or depreciated.

Residential Construction

Report the value of residential structures including the housing portion of multi-purpose projects and of townsites.

Exclude:

  • buildings that have accommodation units without self-contained or exclusive use of bathroom and kitchen facilities ( e.g. , some student and senior citizen residences)
  • the non-residential portion of multi-purpose projects and of townsites
  • associated expenditures on services.

The exclusions should be included in non-residential construction.

Non-Residential Building Construction (excluding land purchase and residential construction)

Report the total cost incurred during the year of building construction (contract and by own employees) whether for your own use or rent to others.

Include also:

  • the cost of demolition of buildings, land servicing and of site-preparation
  • leasehold and land improvements
  • all preconstruction planning and design costs such as engineer and consulting fees and any materials supplied to construction contractors for installation, etc.
  • townsite facilities, such as streets, sewers, stores, schools.

Non-Residential Engineering Construction

Report the total cost incurred during the year of engineering construction (contract and by own employees) whether for your own use or rent to others.

Include also:

  • the cost of demolition of buildings, land servicing and on site-preparation
  • leasehold and land improvements
  • all preconstruction planning and design costs such as engineer and consulting fees and any materials supplied to construction contractors for installation, etc.
  • oil or gas pipelines, including pipe and installation costs
  • communication engineering, including transmission support structures, cables and lines, etc.
  • electric power engineering, including wind and solar plants, nuclear production plants, power distribution networks, etc.

Machinery and Equipment

Report total cost incurred during the year of all new machinery, whether for your own use or for lease or rent to others. Any capitalized tooling should also be included. Include progress payments paid out before delivery in the year in which such payments are made. Receipts from the sale of your own fixed assets or allowance for scrap or trade-in should not be deducted from your total capital expenditures. Any balance owing or holdbacks should be reported in the year the cost is incurred.

Include:

  • automobiles, trucks, professional and scientific equipment, office and store furniture and appliances
  • computers (hardware and software), broadcasting, telecommunication and other information and communication technology equipment
  • motors, generators, transformers any capitalized tooling expenses
  • progress payments paid out before delivery in the year in which such payments are made
  • any balance owing or holdbacks should be reported in the year the cost is incurred
  • leasehold improvements.
  New Assets including financial leases Purchase of Used Canadian Assets Renovation Retrofit Refurbishing Overhauling Restoration Total Capital Expenditures
Land        
Residential Construction        
Non-Residential Building Construction        
Non-Residential Engineering Construction        
Machinery and Equipment        
Software        

Research and Development

5.  From January 1, 2020 to December 31, 2020 , did this organization perform scientific research and development in Canada of at least $10,000 or outsource (contract-out) to another organization scientific research and development activities of at least $10,000?

Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge. For an activity to be an R&D activity, it must satisfy five core criteria:

  1. To be aimed at new findings (novel);
  2. To be based on original, not obvious, concepts and hypothesis (creative);
  3. To be uncertain about the final outcome (uncertainty);
  4. To be planned and budgeted (systematic);
  5. To lead to results that could be possibly reproduced (transferable/ or reproducible).

The term R&D covers three types of activity: basic research, applied research and experimental development. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.

  • Yes
  • No

Capital Expenditures - Intentions 2021

6. For the 2021 fiscal year, what are this organization's intentions for capital expenditures?

Report the value of the projects expected to be put in place during the 2021 fiscal year.

Include:

  • the gross expenditures (including subsidies) on fixed assets for use in the operations of your organization or for lease or rent to others
  • all capital costs such as feasibility studies, architectural, legal, installation and engineering fees as well as work done by your own labour force.

Imported used fixed assets should be reported under New assets including financial leases.

Purchase of Used Canadian Assets

Definition: Used fixed assets may be defined as existing buildings, structures or machinery and equipment which have been previously used by another organization in Canada that you have acquired during the time period being reported on this questionnaire.

Explanation: The objective of our survey is to measure gross annual new acquisitions to fixed assets separately from the acquisition of gross annual used fixed assets in the Canadian economy as a whole.

Hence, the acquisition of a used fixed Canadian asset should be reported separately since such acquisitions would not change the aggregates of our domestic inventory of fixed assets, it would simply mean a transfer of assets within Canada from one organization to another.

Imports of used assets, on the other hand, should be included with the new assets because they are newly acquired for the Canadian economy.

Work in Progress:

Work in progress represents accumulated costs since the start of capital projects which are intended to be capitalized upon completion.

Typically capital investment includes any expenditure on an asset in which its' life is greater than one year. Capital items charged to operating expenses are defined as expenditures which could have been capitalized as part of the fixed assets, but for various reasons, have been charged to current expenses.

Land

Capital expenditures for land should include all costs associated with the purchase of the land that are not amortized or depreciated.

Residential Construction

Report the value of residential structures including the housing portion of multi-purpose projects and of townsites.

Exclude:

  • buildings that have accommodation units without self-contained or exclusive use of bathroom and kitchen facilities ( e.g. , some student and senior citizen residences)
  • the non-residential portion of multi-purpose projects and of townsites
  • associated expenditures on services.

The exclusions should be included in non-residential construction.

Non-Residential Building Construction (excluding land purchase and residential construction)

Report the total cost incurred during the year of building construction (contract and by own employees) whether for your own use or rent to others.

Include also:

  • the cost of demolition of buildings, land servicing and of site-preparation
  • leasehold and land improvements
  • all preconstruction planning and design costs such as engineer and consulting fees and any materials supplied to construction contractors for installation, etc.
  • townsite facilities, such as streets, sewers, stores, schools.

Non-Residential Engineering Construction

Report the total cost incurred during the year of engineering construction (contract and by own employees) whether for your own use or rent to others.

Include also:

  • the cost of demolition of buildings, land servicing and on site-preparation
  • leasehold and land improvements
  • all preconstruction planning and design costs such as engineer and consulting fees and any materials supplied to construction contractors for installation, etc.
  • oil or gas pipelines, including pipe and installation costs
  • communication engineering, including transmission support structures, cables and lines, etc.
  • electric power engineering, including wind and solar plants, nuclear production plants, power distribution networks, etc.

Machinery and Equipment

Report total cost incurred during the year of all new machinery, whether for your own use or for lease or rent to others. Any capitalized tooling should also be included. Include progress payments paid out before delivery in the year in which such payments are made. Receipts from the sale of your own fixed assets or allowance for scrap or trade-in should not be deducted from your total capital expenditures. Any balance owing or holdbacks should be reported in the year the cost is incurred.

Include:

  • automobiles, trucks, professional and scientific equipment, office and store furniture and appliances
  • computers (hardware and software), broadcasting, telecommunication and other information and communication technology equipment
  • motors, generators, transformers any capitalized tooling expenses
  • progress payments paid out before delivery in the year in which such payments are made
  • any balance owing or holdbacks should be reported in the year the cost is incurred
  • leasehold improvements.
  New Assets including financial leases Purchase of Used Canadian Assets Renovation Retrofit Refurbishing Overhauling Restoration Total Capital Expenditures
Land        
Residential Construction        
Non-Residential Building Construction        
Non-Residential Engineering Construction        
Machinery and Equipment        
Software        

Capital Expenditures - Intentions 2021

7. You have not reported any capital expenditure intentions for 2021.

Please indicate the reason.

  • Zero capital expenditure intentions for 2021
  • Figures not available but plans are for no change in capital expenditures for 2021
  • Figures not available but plans are for an increase in capital expenditures for 2021
  • Figures not available but plans are for a decrease in capital expenditures for 2021

Research and Development

8. For the 2021 fiscal year, does this organization plan on performing scientific research and development in Canada of at least $10,000 or outsourcing (contracting-out) to another organization scientific research and development activities of at least $10,000?

Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge. For an activity to be an R&D activity, it must satisfy five core criteria:

  1. To be aimed at new findings (novel);
  2. To be based on original, not obvious, concepts and hypothesis (creative);
  3. To be uncertain about the final outcome (uncertainty);
  4. To be planned and budgeted (systematic);
  5. To lead to results that could be possibly reproduced (transferable/ or reproducible).

The term R&D covers three types of activity: basic research, applied research and experimental development. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.

  • Yes
  • No

Notification of intent to extract web data

9. Does this business have a website?

  • Yes
  • No

Specify the business website address

e.g., www.example.ca

Statistics Canada is piloting a web data extraction initiative, also known as web scraping, which uses software to search and compile publicly available data from organizational websites. As a result, we may visit the website for this organization to search for, and compile, additional information. This initiative should allow us to reduce the reporting burden on organizations, as well as produce additional statistical indicators to ensure that our data remain accurate and relevant.

We will do our utmost to ensure the data are collected in a manner that will not affect the functionality of the website. Any data collected will be used by Statistics Canada for statistical and research purposes only, in accordance with the agency's mandate.

Please visit Statistics Canada's web scraping initiative page for more information.

Please visit Statistics Canada's transparency and accountability page to learn more.

If you have any questions or concerns, please contact Statistics Canada Client Services, toll-free at 1-877-949-9492 (TTY: 1-800-363-7629)) or by email at infostats@canada.ca. Additional information about this survey can be found by selecting the following link:

Annual Capital Expenditures Survey: Preliminary Estimate for 2020 and Intentions for 2021

Changes or events

10. Indicate any changes or events that affected the reported values for this business or organization, compared with the last reporting period.

Select all that apply.

Labour shortages or employee absences

Disruptions in supply chains

Deferred plans to future projects on hold

Projects cancelled or abandoned

Strike or lock-out

Exchange rate impact

Price changes in goods or services sold

Contracting out

Organizational change

Price changes in labour or raw materials

Natural disaster

Recession

Change in product line

Sold business or business units

Expansion

New or lost contract

Plant closures

Acquisition of business or business units

Other
Specify the other changes or events:

No changes or events

Contact person

11. Statistics Canada may need to contact the person who completed this questionnaire for further information.

Is the provided given names and the provided family name the best person to contact?

  • Yes
  • No

Who is the best person to contact about this questionnaire?

First name:

Last name:

Title:

Email address:

Telephone number (including area code):

Extension number (if applicable):
The maximum number of characters is 5.

Fax number (including area code):

Feedback

12. How long did it take to complete this questionnaire?

Include the time spent gathering the necessary information.

Hours:

Minutes:

13. Do you have any comments about this questionnaire?

Quarterly Survey of Financial Statements: Weighted Asset Response Rate - second quarter 2020

Weighted Asset Response Rate
Table summary
This table displays the results of Weighted Asset Response Rate. The information is grouped by Release date (appearing as row headers), 2019 Q2, Q3, Q4 and 2020 Q1, Q2, calculated using percentage units of measure (appearing as column headers).
Release date 2019 2020
Q2 Q3 Q4 Q1 Q2
quarterly (percentage)
August 25, 2020 86.0 80.0 67.4 80.0 57.8
June 9, 2020 86.0 80.0 67.4 67.8 ..
February 25, 2020 81.9 75.4 62.4 .. ..
November 26, 2019 80.1 64.9 .. .. ..
August 23, 2019 65.2 .. .. .. ..
.. not available for a specific reference period
Source: Quarterly Survey of Financial Statements (2501)
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