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Region of Waterloo Residents Priorities 2019

Student

By Laura Krizan

The results of this poll were based on an interactive voice response survey conducted Friday March 15thand Monday March 17th, 2019. A total of 1003 individuals completed the first question of the survey and 715 completed the entire survey. The survey was designed to aid in the development of the Region of Waterloo’s Strategic Plan for 2019-2023. The Strategic Plan helps Council and staff set priorities and achieve goals, all while keeping the community’s concerns and suggestions in mind. A significant proportion of the Strategic Plan includes hearing input from the public and listening to comments, concerns, etc. so that the Region can set appropriate priorities. The questions in the survey are also aimed to help the Region of Waterloo during the drafting of its Strategic Plan in the future.

The Strategic Plan has 5 focus areas: Thriving Economy, Sustainable Transportation, Environment and Sustainable Growth, Healthy, Safe, and Inclusive Communities, and Responsive and Engaging Government Services. The questions that were administered as a part of this survey were developed by ensuring that these focus areas were kept in mind.

The first question on the survey asked participants about the level of confidence they have in their local government. The results are shown in the chart below, indicating that a majority of citizens (56%) are confident in the regional government to some degree (including somewhat confident, confident, and very confident levels).

The survey was a way to analyze the top priorities that need to be set by the Region of Waterloo for the development of their Strategic Plan. Respondents were asked what they think the biggest priority in Waterloo Region is that the regional government should address.  The results, as shown in the chart below, have been ranked based on the number of respondents choosing a given category as their top priority. The top 3 priorities are: 1) Supporting the development of affordable housing 2) Managing growth, 3) Protecting the environment. 

A significant component of the Strategic Plan focuses on the services that are delivered by the Region of Waterloo, such as public transportation, waste collection, and so forth. In order to better understand the preferences among citizens living in the Region of Waterloo in relation to the delivery of services, the survey asked: “Regional Government must balance the cost of delivering services with taxation. Which of the following would you most prefer for property taxes in Waterloo Region?” 

Results indicated that 19% preferred increasing taxes to improve services while 14% preferred having property taxes decreased. 23% preferred keeping taxes that same and possibly reducing services. The largest proportion (44%) preferred having taxes increased with the rate of inflation and maintaining current services.

This survey was also used to analyze the best ways and platforms to receive public input in the future. Respondents were asked, “If the Region of Waterloo wanted to gather public input or engage you on major issues or decisions, what are the best ways?” It was found that the best ways to gather public input or engage on major issues/decisions in the region are: 1) Online Survey, 2) Social Media, and 3) Telephone Survey. All other options that were included in the survey are listed below.

Ultimately, the survey helped to provide the Region of Waterloo with important information that can be used during the development of the 2019-2023 Strategic Plan. A total of 9 questions were administered, yet the responses that have been analyzed above highlight the most critical results that will be taken into consideration by the Region.

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Survey Details

The Interactive Voice Response (IVR) survey was conducted by Laura Krizan; Abby Schlueter; Andrea Volford, and Professor Anthony Piscitelli on March 15th and March 17th, 2019. Throughout the development of the survey, the students worked alongside Lorie Fioze, Manager of Strategic Planning and Strategic Initiatives. The questions that were formulated for the survey focused on supporting the development of the Region of Waterloo’s strategic plan. The survey was funded by the Region of Waterloo to support this initiative. 

Sampling Approach

The sample size was created by randomly selecting Waterloo Region landlines listed in a digital phone book. A sample of likely cellphone numbers was added by randomly selecting phone numbers that were originally assigned to Waterloo Region, according to the Canadian Numbering Administrator. Sampling errors exists as a result of this approach due to the mismatch created by the random dialling of phone numbers as opposed to randomly sampling actual Waterloo Region residents.

Response rate

The survey called 46,912 live lines. The response rate was 1.5%, which is based on 715 respondents who completed the entire survey. All 788 respondents who answered the first three questions were included in the results. It is worth noting that 215 (21%) respondents were not eligible to participate due to being under 18 or not living in Waterloo Region.

Weights

Results of this survey have been weighted by age, gender, and city/township according to the 2016 census. The full weights are posted along with the raw data on OpenIcpsr.org and can be found by visiting: http://doi.org/10.3886/E110225V1  

Margin of Error

Results are considered accurate +/-3.7%, 19 times out of 20. The margin of error on subsamples is higher.

Raw Data

Raw survey data is available on OpenIcpsr.org. The data can be found at: http://doi.org/10.3886/E110225V1  

Disclaimer

The survey results will exhibit sampling error as a result of the mismatch created by the random dialling of phone numbers as opposed to randomly sampling actual Waterloo Region residents. This survey was approved by the Conestoga College Research Ethics Board.

Tracking Public Opinion: Ontario Election Wcalc Results

Student

By Suhani Singh

The previous Wcalc blog discussed the results generated from compiled federal polling data from November 7, 2015 to May 4, 2019. We discovered that our results were not consistent with other polling trackers in terms of calculating popular support percentage for the People’s Party of Canada and “Other Party” support which were overestimated. However, it did a satisfactory job in estimating Liberal, Conservative, NDP, Bloc, and Green Party Support. Since the federal polling data is just an estimate at this time with no election results to compare to, we wanted to test Wcalc once more with 2018 Ontario election polling data before we completely disregarded the Wcalc program for our polling calculation purposes.

We ran the overall Ontario polling data collected from March 11 to June 6 through Wcalc. The goal was to compare the Wcalc results to the real election results to test the programs ability to model changes in public opinion toward provincial parties over a short period of time. The expectation was that Wcalc would create a clearer picture of election results, as it proved successful at predicting the 2015 federal election results.

For the purposes of running the data in Wcalc, we removed the Green Party and “other party” polling data from Ipsos and Leger because the Green Party was not asked as a separate answer for respondents, thus inflating the “other party” category and giving us no Green Party data.

 The table below compares the actual percentages of the popular vote from the 53rd Ontario general election with Wcalc the final predicted results based on compiled polling data. It is clear that the Wcalc results were significantly different from the election results.

Party Election results Wcalc results
Conservatives 40.6% 36%
Liberal 19.3% 14.9%
NDP 33.7% 41.7%
Green Party 4.6% 5.7%

The Conservatives and NDP had the most shocking results as their numbers were far from reality and seem like they should be switched with one another. The Conservatives were off by 4.6% scoring less than what they actually received in the election while NDP support was inflated by 8% in Wcalc from the real numbers. Had these results from Wcalc been released before the election to predict the outcome, it would have wrongly predicted an NDP victory.

Comparing our predictions to CBC Poll Tracker and Wikipedia, we can see how different and skewed the Wcalc results are when predicting election results based off of poll averages. The two other predictions were much closer to the actual results of the popular vote as indicated in the chart below.

The time series generated by Wcalc is shown in the graph below. It is evident from the graph that the NDP takes a bit of a jump during the campaign period, followed by a slight drop, and then emerges on the top with 41.7%. Conservative support declined a little and then gradually increase to 36%. The prediction for Liberal Party was 4.4% lower than the actual results and 1.09% higher for the Green Party. The Green Party difference was not significant and fell within a +/-1% margin of error.

The scatter plot graph for Ontario shows a neck to neck race between the Conservatives and the NDP. Conservatives can be seen polling close to 40% with NDP just slightly under. Wikipedia’s graphical analysis predicted results closer to actual election results with a margin of only +/-2%. Wikipedia used local regressions for smoothing the data, therefore, projecting results closer to the actual election results.

The data from WCalc did not match the election result for PC and NDP. The Dyad Ratio Algorithm that Wcalc is based on overestimated the trend of NDP support and while underestimating PC support.

These results were disappointing and have caused ThreeHundrededandThirtyEight.com to reconsider the use of Wcalc for predicting elections. While the research evidence suggests Wcalc is effective at modeling policy mood, we will no longer be using it to predict election results.

Tracking Public Opinion: Federal Wcalc Results

Student

By Natalie Pikulski

The original plan for ThreeHundredThirtyEight.com was to use Wcalc, a program developed by James Stimson to model changes in public support for provincial and federal parties in Canada. Wcalc uses the Dyad Ratio algorithm which is explained in greater detail in a previous ThreeHundredThirtyEight.com post. The Wcalc program worked very well for a trial run on the 2015 Federal Election but unfortunately more recent attempts to use the program have shown flaws in this approach. It appears the local regression approach (as used by Wikipedia), is a more effective method to track opinion changes related to elections, whereas Wcalc may be better suited to tracking policy mood. This blog post examines what went wrong when using Wcalc to track Canadian public opinion since the 2015 federal election and the following post examine the problems associated with using Wcalc to predict the 2018 Ontario provincial election.

We began this analysis by collecting all usable individual Canadian federal polling data from November 7, 2015 to May 4, 2019 that were reported by CBC Poll Tracker and Wikipedia. Usable data was based on working links to reports that are available online as of May 2019 and polling questions that are consistent or similar to questions asked by other polling organizations. Voter intention percentages and sample sizes were based on all leaning and decided responses in each poll. 

In an Excel spreadsheet, we tracked support for six party categories (Liberal, Conservative, NDP, Bloc Quebecois, Green, and Other) with the PPC added in 2018. However, there were notable issues that created some problems in our tracking: 

  • Mainstreet did not have an “other party” category until 2018 which resulted in 2 years’ worth of polls that were not able to be entered in the “other category” when using the Wcalc program as blank values are not accepted. 2018 onward, the organization included an “other party” category and eventually included a “People’s Party of Canada” option since the party emerged, allowing us to use all the data from this time forward.
  • Abacus Data conducted rolling polls in 2019 however the reports from March 4 to March 10 did not include detailed breakdown tables that we could pull “other numbers from”. The reports only provided graphs that indicated overall numbers for each main party’s support but did not feature a graph line for the “other category”. We needed to remove these polls before running the “other category” through Wcalc. 
  • Ipsos did not consider the Green party a separate category for any of its polls despite the Party polling at similar levels to the BQ, which was included by Ipsos. All the Green Party support was likely placed in the other category which inflated the category significantly when compared to other polling organizations that had Green as a separate category. These high “other” polls and blank Green polls needed to be removed before running the data through the Wcalc program. Ipsos only had one poll which included the Green Party and the PPC as a separate category, but this poll was not usable for our purposes as the Wcalc program requires at least 2 similar data sets to compare results. 
  • All Nanos Research polls were entirely removed for our purposes since the question asked had respondents rank their top two choices for voting intention rather than one choice like all other organizations. The difference in questions was not consistent with other polls and therefore not usable for the Wcalc program.

After running the individual polling data through the Wcalc program, the results showed some close similarities to other polling trackers for the Liberal, Conservative, and Green parties and a slightly larger gap between the NDP and Bloc but still within decent range. The main issue encountered with the program was the significantly higher score of the PPC and “Other Party” categories that scored much higher in Wcalc when compared to other polling averages. The average below are as of early May 2019.

The Wikipedia poll tracker uses a Loess regression method to calculate the public opinion poll averages over time. While most parties had similar results when compared to our Wcalc calculations, the results for the PPC were much higher in Wcalc while the NDP was lower. It should be noted that both Wikipedia and CBC poll trackers included the Nanos polls that we had removed due to their inconsistency with other polls. This may be a contributor to the differences, along with the limitations of Wcalc. 

Overall, Wcalc remains better suited for long-term tracking of public opinion polls that are not as susceptible to sudden changes like political/election polling are. The smoothing effect in this program assumes sudden changes are likely due to sampling errors which is less effective for polls that may change drastically in a short amount of time. The gaps in our results compared to others indicate this program may not be as reliable as others for our purposes. These gaps are even more pronounced when examining data during the 2018 Ontario election, which will be analyzed in our next blog post.