Coming back to the previous blog post where the bank wanted a university letter from me. I have been trying to hold back on providing the university letter to the bank until my full time job was confirmed for next year. In my emails with the bank; though, apparently they only wanted the university letter to show that I am working in the university full-time. They were not going to use the university letter against me to show that I did not disclose one of my fixed-term contracts ending soon. Instead, they asked me whether I thought my contract would not be extended into next year. I confirmed that my contract would likely be extended into next year as I am working on a project that will continue into next year. With the crisis averted for now, I sent the latest set of documents to the bank. Hopefully I pass the bank’s pre-approval process so that my home loan application can be passed onto the Victorian Government to assess my eligibility for the Victorian Homebuyer Fund.
Attending more inspections and observing auctions
While waiting to get into the Victorian Homebuyer Fund, I have continued to do some research on the suburbs and properties I want to live in. On most Saturdays, I have been attending inspections to see firsthand what the properties are like and auctions to see how they are run. On Saturday 19th October; though, I took a break from going to auctions and inspections as there were not many auctions going on. Instead, I spent the morning touring some suburbs in Melbourne’s south-east, taking notes on what the streets were like and where the shops were located.
On Saturday 26th October; though, I managed to watch an auction where the property was sold during the auction instead of being passed in. That property was unique in that, despite being near the train line, it is surrounded by green space and trees that shield the apartment from outside noise in the train line and main road. There was also plenty of outdoor space for one to walk around in and get some fresh air. Consequently, around seven bidders came to the auction. However, only two bidders made bids during the auction, initially raising the price at $10,000 increments before varying increments from $3,000 to $7,000. After one bidder pulled out, the other bidder won the property outright without it being passed in. That auction gave me a good taste of what it is like to bid in an auction. In particular, I see the balance between knowing in advance how much I would be prepared to buy the property and being tactical in bidding to ensure that I win the auction for the property.
My view on Melbourne’s activity centres
Something that has been on the news in Melbourne recently is the announcement of activity centres being planned across Melbourne. These activity centres are suburbs in Melbourne where more houses will be built to provide more people access to public transport and town centres. These are associated with high-rise buildings (up to 12 storeys) close to train stations and town centres and medium-rise buildings (up to 6 storeys) within walking distance of them. Ten suburbs were previously earmarked to become activity centres, but now 25 more suburbs will become activity centres, with more still to come. While some groups such as Property Council of Australia and YIMBY (Yes in my Backyard) Melbourne have welcomed the move as more homes would be built in areas where people want to live, others have lambasted the move, with local residents and the Victorian Opposition feeling concerned that high-rise buildings would change the dynamics of the suburbs.
The activity centres being proposed in Melbourne. Source
Personally, I am fearful of how the announcement of activity centres will affect my property search in the suburbs I want to live in. Plans have not yet been announced on the activity centres that have recently been announced, so I am not sure what plans will be in the suburbs I want to live in. I am avoiding high-rise apartments in my property search as they are not only uncomfortable for me to live in, they are also bad investments as the numerous similar apartments would cause prices to stagnate or fall. Instead, I would like to live in a low-rise apartment or unit with not too many people in the building, where I have plenty of indoor and outdoor space to walk around. If houses and apartment blocks have to be repossessed and demolished to pave the way for more high-rise apartments, it would rob me of my home that I plan to live in for over 10 years.
The announcement of activity centres will not deter me from searching for the ideal property that I can live in for 10 years or more. Hopefully plans for activity centres will be released before I start seriously looking for my first home. That way I can mark out areas where I do not want to live in, refining my property search to areas and properties that would not be affected too much by the activity centres.
I think I may have made a mistake in applying for my home loan too soon. Late last month, I submitted my credit and Victorian Homebuyer Fund applications to the bank with the required documents. I was motivated to submit these applications before the RBA announced that it would not be changing interest rates as I did not want to miss out on getting a place in the Victorian Homebuyer Fund. Also, given that it takes around 6 weeks to get a spot, I wanted to submit these applications soon to get the process started. This motivation was further fuelled by the fact that I would not be eligible for the Commonwealth Government’s “Help to Buy” scheme as I exceed the $90,000 per year income threshold as a single person. Hence, if I failed to get in the Victorian Homebuyer Fund, there would be no other shared equity schemes I would be eligible for. This would restrict my budget on buying my first home.
Since submitting my applications last month, the bank has sent numerous enquiries and requests for more documents to provide evidence behind my income and employment history. Having previously provided the minimum number of documents I needed to submit my applications, for the bank to come back to me asking for more documents was a bit dispiriting. In particular, the bank wanted a letter from my employer to show that I am now working full-time in the university. Because I have been moving around jobs over the past few months, they wanted that document to show that I am now earning a full-time income from the university.
Even though I have the letter to submit to the bank, I am hesitant to do so because one of my roles is contracted to end soon. My work in the university is split across two fixed-term roles. While one role will last until mid-2027, the other role is contracted to conclude by the end of the year. I did not disclose that detail in my credit application as I am in the process of getting that role converted into a continuing role and assumed that I would be working full-time in the university next year. The fact that the bank wants an employer letter from me saying that one of my fixed-term roles will end soon will lay bare the fact that I did not fully disclose my current employment situation in my credit application.
I have emailed the bank to see what they will do with my credit application. Best case scenario, they put my credit application on hold while I negotiate to secure a continuing role in the university, meaning I can work full-time in the university until at least mid-2027. Worst case scenario, I need to submit the required documents now with the current situation that one of my roles would be ending soon. This would either decrease my borrowing capacity massively, limiting the properties I can buy, or they reject my credit application outright. If that happens, that would put a black mark on my credit history which would make it harder for me to get a home loan. I am hoping that I can delay my credit application without the bank rejecting my application outright so that I have time to negotiate for a continuing role with the university.
Upon reflection, I was driven by FOMO in trying to get a place in the Victorian Homebuyer Fund before those places run out. The presence of the government getting an equity on my first home would allow me to buy a more expensive property with the money I have, increasing the chances that I would stay in it for a longer period of time. I overlooked the fact that full-time employment in the university was not guaranteed from the end of this year. If I had waited until I secured full-time work beyond 2024, I would have the evidence I need to show the bank that I have the income to keep up with my minimum repayments for my home loan. Here’s hoping that this is not a fatal mistake in my home buying process and that the bank would be kind enough for me to ameliorate this mistake.
In the last blog post, I briefly explained what I CAN Network’s peer mentoring programs were aiming to achieve and described how I have presented internal evaluation results in I CAN Network’s 2023 Social Impact Report. I represented changes in outcomes during the peer mentoring programs by comparing the distribution of responses before and after the program and using 100% stacked bar charts. From these results, we can calculate the difference in the proportion of positive responses before and after the program and test whether the result is significant and relevant. This is important for assessing whether the changes we are seeing in the peer mentoring programs are real or have arisen through chance.
In this blog post, I would like to provide some information on how to interpret the statistical results in the 2023 Social Impact Report so that you have a better understanding of how we have drawn the conclusions of the report.
Why we conduct statistical analyses
I CAN Network runs surveys and polls with Autistic young people (aged 5-20 years) before and after participating in the peer mentoring programs. In these surveys and polls, mentees rate how much they agree with statements relating to the program outcomes. We then compile these survey responses to look at how they are distributed before and after the program and calculate percentage changes in positive responses. We use these results to explain changes in outcomes among Autistic young people to different stakeholders.
A limitation of these surveys and polls is that they only sample a proportion of Autistic young people who have come to I CAN Network’s peer mentoring programs and were willing to provide feedback. It does not encompass all Autistic young people attending these peer mentoring programs, let alone across Australia. Statistics allows us to extend these survey results to describe what the general effect of the peer mentoring programs would be to all Autistic young people across Australia. It does this by testing how significant and relevant improvements in outcomes due to the peer mentoring programs are among Autistic young people.
The below figure summarises the purpose of statistics.
How statistics can be used to generalise survey responses from the sample (Autistic young people surveyed) to the population (Autistic young people across Australia)
There are a range of statistical techniques that can be used to test the significance and relevance of the findings. The 2023 Social Impact Report uses three statistical techniques to assess the significance and relevance of the findings:
Hypothesis testing: Hypothesis testing allows us to see whether we should accept a statement about a population based on whether the result would have arisen through chance. This is represented by the p-value. If the difference did not arise through chance, then we say that the peer mentoring programs have a statistically significant effect on the outcome.
95% confidence intervals: Hypothesis testing does not give us the range of possible values that could be experienced by the general population. 95% confidence intervals not only give us that range, but also describe how confident we can be of the result.
Effect size: The effect size gives us a measure of how relevant our results are. This is done by standardising the changes in outcomes.
These statistical techniques produce numerical outputs that accompany the percentage increases. As an example, in the I CAN Imagination Club® mentoring program (the peer mentoring program for primary schools), we reported an 11% increase in positive responses towards the self-confidence statement. These are accompanied by a p-value of 0.006, a 95% confidence interval of [3%, 18%] and an effect size of 0.22.
What do these numbers mean, and how are they calculated? The next few sections will explain how these statistical outputs are calculated and interpreted.
Hypothesis testing
Distributing surveys to Autistic young people and analysing their responses allows us to calculate percentage increases in positive responses that indicate how much they have changed during the program. However, we are unsure of whether this result would have arisen through chance (i.e., we get a different result if we run the survey again), or if it is an effect that can be generalised to other Autistic young people across Australia. Hypothesis testing allows us to answer this question.
In hypothesis testing, we want to see whether there is a higher proportion of positive responses after the program compared to before the program. In other words, does pafter > pbefore? To see whether that is the case, I used a two-proportion z-test. This statistical technique allows us to convert the difference in proportions into a standard z-value that can be used to determine the p-value. The p-value can be used to see whether the difference in proportions would have arisen through chance or not.
To calculate the z-value, we need two things: the percentage change in positive responses and the standard error (SE). The percentage change in positive responses is derived by calculating the proportion of positive responses before and after the program and taking the difference between the two. We can take the difference between the two proportions as we can rearrange the inequality from pafter > pbefore to pafter – pbefore > 0. If the difference between the two proportions is positive, it means there is a higher proportion of positive responses after the program compared to before.
At the same time, we calculate the standard error of the difference between two proportions. The standard error describes the spread of calculated differences from running multiple hypothetical tests to see whether we would get similar results. From there, we divide the difference between two proportions by its standard error to get the z-value.
We match the z-value to the z-distribution curve to get the area underneath the tail on one side of the curve. The area underneath one side of the curve is our one-tailed p-value, the probability that we would produce a difference between two proportions that is just as big, if not bigger, than what we would get by chance. If we multiply the p-value by 2, we get a two-tailed p-value which describes the probability that we would get a difference (positive or negative) that is just as big, if not bigger, than what we would get by chance. We use the two-tailed p-value in our hypothesis testing to account for both increases and decreases in the proportion of positive responses after the program.
One-tailed and two-tailed p-values in a standard normal distribution
If the two-tailed p-value is 0.05 or below, the result is considered to be statistically significant. In other words, it is unlikely that the increased proportion of positive responses arose randomly. This result indicates that the peer mentoring programs may have contributed to positive outcomes among Autistic young people, something that is evident in our analyses of comments and mentee creations for the 2023 Social Impact Report.
Going back to our sample self-confidence result, we reported a p-value of 0.006. This means we have a 0.6% chance that we would get an increase in self-confidence that is 11% or more randomly. Given the small chance of it happening, we would likely conclude that the I CAN Imagination Club® mentoring program had an effect on students’ self-confidence.
Confidence intervals
A hypothesis test might indicate that a specific result is statistically significant as indicated by a low p-value. However, it does not give us the range of possibilities that could be experienced by the general population as a result of the program. This is where the 95% confidence interval comes in. The 95% confidence interval indicates a range of possible values where we are 95% sure the true percentage increase lies. The size of the confidence interval can tell us how confident we can be of the result:
A small 95% confidence interval indicates a small margin of error, indicating a high level of confidence in the result.
In contrast, a large 95% confidence interval makes us less certain of the result as the true percentage increase could take a wide range of values.
To calculate the lower and upper bounds of the 95% confidence interval, we multiply the standard error by 1.96 and subtract or add it to the observed percentage increase respectively. The lower and upper bounds of the 95% confidence interval are encased in square brackets to indicate the range of possible values of the true percentage increase.
A visual of the 95% confidence interval for increased self-confidence
In our sample self-confidence results, we had a 95% confidence interval of [3%, 18%]. This means we are 95% confident that the true increase in self-confidence among students attending the I CAN Imagination Club® mentoring program could be as low as 3%, or as high as 18%. This confidence interval gives us a range of possibilities that the I CAN Imagination Club® mentoring program could take to boost self-confidence among Autistic young people in Australia.
Effect size
A result could be statistically significant as indicated by a low p-value. However, this result might not be relevant in real life as the change is too small to have an impact on the population of interest. This is particularly true when we survey hundreds or even thousands of people.
This is where effect size comes in. The effect size describes whether the increase in the proportion of positive responses has an impact on real life. To do this, we use Cohen’s h which describes how far off the proportion of positive responses after the program is from before the program. A larger h value indicates bigger changes in outcomes. We use cut-offs of h = 0.2, h = 0.5 and h = 0.8 to indicate small, medium and large effect sizes respectively.
In the 2023 Social Impact report, Cohen’s h in most outcome statements range from 0.15 to 0.35, representing insignificant to small effect sizes. Given that we survey around 300 to 500 students per time point; though, getting a small effect size is still good in a policy setting as it describes a sizeable change that is happening in a large population.
The effect sizes, along with thresholds of Cohen’s h
Going back to our sample self-confidence result one last time, the effect size (Cohen’s h) is 0.22. As it falls between the Cohen’s h thresholds of 0.2 and 0.5, the effect size is considered to be small. Given our 11% increase in self-confidence over 300 students; though, we still have a notable improvement in self-confidence that would be relevant in the real world.
Conclusion
This blog post provides an overview of the statistical techniques that were used in the 2023 Social Impact Report. The result is a series of numbers that accompany the percentages changes to underline the significance and relevance of the findings. These analyses are important in providing an evidence base behind the effectiveness of I CAN Network’s peer mentoring programs to Autistic young people across Australia.
Personal disclaimer
This blog post was written by James Ong in his personal capacity. The content, views and opinions represented in this blog post are solely my own and do not reflect those of I CAN Network Ltd.
I was listening to a podcast the other day where they compared the property markets in Melbourne, Sydney and Brisbane. They noted that compared to Sydney and Brisbane, Melbourne’s house prices have remained stagnant over the past few years. This was due to a range of factors such as the 2020-21 COVID-19 lockdowns in Melbourne, the apartment oversupply across the city and high land taxes for investors. The stagnant house prices have made the Melbourne property market conducive to first home buyers and investors looking to buy their first home or enter the property market.
I have been tracking the outcomes of auctions in the suburbs of interest over the past few weeks. And just yesterday, I was looking over properties in the suburbs of interest that have been sold over the past month. Although I can see bargains in some properties that are within my borrowing capacity, I will be going into a property market that may be increasingly difficult to get into, for two reasons.
First, I am looking for a property that I can live in for a long time and that will be in high demand in the future when I lease or sell it. That means the property has to be rare in the market, yet valuable to the future renter or home buyer. Apartments in high-rise apartments are not rare and are clearly targeted to investors looking to rent them out, so I am ruling them out. I am also avoiding apartments on or near main roads or train lines as they would make too much noise and are overpriced for their location. I am anticipating that these apartments would be hard to lease or sell to other people in the future, making them bad property investments.
Instead, I am looking for apartments in a quiet street that I can stay in for many years and that would be valuable to a future buyer. The apartment would have enough indoor space for me to live by myself and work from home. Additionally, there is a private balcony or backyard for me to get some fresh air or to have a barbecue. Ideally, the rooms would be nicely laid out, with the living rooms separate from the bathroom and bedrooms, so that I can separate areas to relax from areas to work and to sleep. These requirements eliminate a lot of apartments for me, leaving me with few options to choose from. Additionally, there is a lot of demand for these apartments with some of them going for auction. These will be competitive to buy, particularly as more people enter the property market to buy their first home or to invest. This will be a main challenge in buying my first home.
Second, having kept track of auction outcomes in the suburbs of interest, the property market is increasingly heating up. I am seeing that most properties were sold either before or on auction day. Where the sold prices have been made publicly available, most properties were sold above the upper limit of the price guide. That has made me feel a bit apprehensive on how competitive the auctions will be, particularly if it is the property I really want to live in.
Still, I have a few weeks before I get my pre-approval to buy my first home. And there are more properties that will go on the market over the next few weeks, particularly as spring progresses. That gives me some leeway to do some desk research, go to some property inspections and watch some auctions to get acquainted with the property market. Hopefully by the time I receive my pre-approval from the bank, I will be ready to enter the property market and buy my first home as soon as possible.
I CAN Network is an Autistic-led social enterprise that aims “to prove what Autistics CAN do”. It is working towards this aim by running a variety of programs that engage Autistic people and their supporters (for example, parents, carers and teachers). In particular, I CAN Network runs peer mentoring programs in school and online environments for Autistic young people aged 5-20 years to come together and build their social connections, improve their self-esteem and develop life skills. It also runs professional development programs for schools to create safe environments for Autistic young people to be themselves and engage in school.
Over the past 10 years, I have been working on internal evaluations for I CAN Network. I planned and conducted evaluations measuring outcomes of I CAN Network’s programs and wrote numerous reports describing the results of these evaluations. These reports describe the outcomes I CAN Network has achieved in its programs, particularly peer mentoring programs for Autistic young people. While most reports are delivered to state governments and funders, I also wrote Social Impact Reports that are publicly available on I CAN Network’s website, with the 2023 edition being recently released.
With the 2023 Social Impact Report being the last evaluation report I will write for I CAN Network, in this blog post I will describe why I have designed the graphs in a particular way to make them easy for the reader to interpret and understand.
What are the aims of I CAN Network’s programs?
I CAN Network runs school and online peer mentoring programs for Autistic young people aged 5-20 years. At school, I CAN Network runs I CAN Imagination Club® and I CAN School® mentoring programs for Autistic young people in primary and secondary schools respectively. These programs are run during school hours. In comparison, I CAN Network runs I CAN Online, delivering online mentoring programs after school for Autistic young people across Australia.
All peer mentoring programs aim to achieve outcomes in the following three areas:
Self-esteem: The peer mentoring programs aim to improve Autistic young people’s views of themselves and their Autism.
Social connection: Autistic young people in the peer mentoring programs interact with each other to establish social connections and friendships, as well as their parents, teachers and mentors to develop support networks.
Skill development: These relate to Autistic young people acquiring and improving life skills such as communication and stress management so that they are more likely to advocate for themselves and manage their own lives.
These outcome areas are supported by a positive program environment, where Autistic young people feel safe to be themselves without feeling judged. This allows them to open up and interact with other people.
The below figure summarises the outcomes that I CAN Network’s peer mentoring programs aim to achieve.
A visual summarising the aims of I CAN Network’s peer mentoring programs
These aims provide a structure in which the peer mentoring programs can be assessed. One way this can be done is by running internal evaluations within the organisation.
Why I have not used mean scores in the report
I CAN Network runs internal evaluations by distributing surveys and polls to Autistic young people before and after the program. Each survey or poll contains statements relating to the outcomes of the program, where mentees rate on a 3- or 5-point Likert scale how much they agree with the statements. We then compare responses before and after the program to see whether mentees have changed during the program. These can be shown both numerically and graphically.
One way to compare responses is to convert them into a score, calculate the mean score before and after the program and compare them to see whether that has changed. For I CAN Imagination Club® mentoring programs, I can convert the yes, maybe and no responses into scores of 1, 0.5 and 0 respectively and calculate a mean score out of 1. Similarly, for I CAN School® mentoring programs, I can convert the responses into scores ranging from 1 (for strongly disagree) to 5 (for strongly agree) and calculate a mean score out of 5.
We can compare the mean scores before and after the program in a table along with the standard deviation (SD which measures the spread of scores) and the total number of responses received.
Statement
Before
After
Mean score
SD
Total # responses
Mean score
SD
Total # responses
I can try new things
0.74
0.29
331
0.80
0.27
303
I know what makes me special
0.70
0.37
323
0.79
0.34
302
I think my brain is awesome
0.69
0.36
327
0.76
0.34
298
We can also visualise the scores in a bar chart, placing the mean scores before and after the program alongside each other. We can rule black solid lines to separate the statements, making it easier to compare mean scores before and after the program within a statement.
Mean scores before and after the program when the scale starts from 0
There are two main problems with using mean scores to compare outcomes. Numerically, calculating mean scores simplifies the responses too much. This makes it difficult to explain a specific mean score (“What does a mean score of 0.74 mean?”) or changes in a mean score (“What does a mean increase of X units mean?”) and relate them to the outcome. Furthermore, because we design our own statements instead of using a standardised tool, it is not possible to compare the changes to a specific standard to explain how relevant they are. The only way to explain the changes is to use statistics to assess whether the changes are statistically significant and relevant. This makes it difficult to explain to a lay audience how large outcomes have changed.
Mean scores when the scale starts from 0Mean scores when the scale starts from 0.62
Graphically, it is hard to accurately visualise changes in mean scores. Scaling the scores from 0 makes it very hard to differentiate mean scores before and after the program. In contrast, scaling the scores from a higher base value distorts the differences before and after the program, deceiving the reader. For example, in the ‘I can try new things’ statement, the mean score only increased by 0.06 after the program. That difference is quite small when scaling the scores from 0, but it is magnified when scaling the scores from a higher base value (specifically 0.62), distorting the difference.
For the above reasons, I decided not to describe changes in outcomes using mean scores. Instead, I represented the changes in a different way.
Why I have used 100% stacked bar charts in the report
To represent changes in outcomes among mentees attending the peer mentoring programs, I decided to compare the distribution of responses before and after the program. For each timepoint, I calculate the proportion of responses belonging to a particular category out of the total number of responses received (excluding not sure and missing responses). I then bring the categories together to form 100% stacked bar charts. I use black solid lines to separate the statements and place the distribution of responses before and after the program next to each other to make it easier to compare them.
Changes in I CAN Imagination Club® outcomes, expressed as proportions of the total number of responses
Using the distribution of responses is a better way to represent changes in outcomes compared to using mean scores. Numerically, it is easier to explain changes in outcomes by saying how much responses in a particular category have increased or decreased after the program. For instance, in the I CAN Imagination Club® graph above, we can compare the proportion of ‘yes’ responses before and after the program and calculate how much it has increased. We can see that compared to before the program, mentees coming into the program show an:
11% increase in trying new things (‘self-confidence’);
14% increase in knowing what makes them feel special (‘self-acceptance’); and
9% increase in thinking their brain is awesome (‘neurodiversity acceptance’).
Representing outcomes as percentage changes makes it easy to explain to the reader how Autistic young people have changed in the peer mentoring program.
Changes in I CAN School® outcomes, expressed as proportions of the total number of responses
Graphically, showing the distribution of responses makes it easier to visualise how different responses shift over time. For instance, in the I CAN School® graph above, we can see that the proportions of ‘strongly disagree’ and ‘disagree’ responses have fallen, while the proportions of ‘agree’ and ‘strongly agree’ responses have risen. Visualising the distribution of responses in this way makes it easy for the reader to interpret the result to see whether Autistic young people are improving as a result of the program. It also does not deceive the reader as the graph is scaled from 0% to 100%, ensuring that the changes are properly represented.
Hence, using 100% stacked bar charts to show the distribution of responses allows us to retain all the information we have collected from our surveys and makes it easier to explain changes in outcomes to a lay audience.
Visualising mentee demographics using different graphs
In the 2023 Social Impact Report, I have also visualised some demographic data to describe who participates in I CAN Network’s peer mentoring programs. I used two different graphs to visualise demographic data: pie charts and bar charts.
There has been sometalk as to why pie charts are bad visualisation tools and that bar charts should always be used when looking at proportions. In my opinion, pie charts are still useful for visualising proportions. However, there are some conditions that need to be met:
It should show as few categories as possible (five at most).
It should be as visually simple as possible, with the parts clearly signposted.
The pie chart should be in 2-D instead of 3-D to prevent distortion of the data.
Gender among I CAN Online mentees as a pie chart
For gender, I used a pie chart as it only has three categories (male, female and trans and gender diverse), making it easy to show the sizes of each gender as a whole. I have also kept the pie chart as visually simple as possible.
I have earmarked each section with a solid black outline and different colours representing each gender.
I have also included some data labels indicating what each section represents, along with the number of mentees and the proportion out of the total number of mentees.
Using a pie chart provides a clear picture of how mentees are split up over the three gender categories.
States and territories among I CAN Online mentees as a pie chart
In contrast, I used a bar chart instead of a pie chart to represent Australian states and territories where I CAN Online mentees live. A pie chart is not appropriate as there are eight states and territories in Australia, resulting in numerous sections being crammed in one space. Using different colours to represent each section and data labels to identify different sections further increase the visual complexity of the graph. Hence, it is hard for the reader to comprehend what proportion of mentees live in a specific state or territory.
States and territories among I CAN Online mentees as a bar chart
In comparison, using a bar chart provides more room to show different categories. It is also easier to look at the length of each bar and align it to the y-axis to see what proportion of mentees live in a specific state or territory. The limitation of a bar chart is that it is hard to see how different states and territories can be pieced together to form a whole like what can be done in a pie chart.
Hence, pie charts have their uses, but their benefits and drawbacks compared to bar charts need to be considered when deciding how to visualise the results.
Conclusion
In this blog post, I explained how I have designed the graphs that appear in the 2023 Social Impact Report. People may have different opinions of how data should be analysed and visualised. From my perspective, I place great importance on presenting the results as simply as possible so that everyone can easily grasp what they are seeing. This facilitates their understanding of how I CAN Network’s programs are making an impact to Autistic young people attending them.
Personal disclaimer
This blog post was written by James Ong in his personal capacity. The content, views and opinions represented in this blog post are solely my own and do not reflect those of I CAN Network Ltd.
![Showing the 95% confidence interval of [3%, 18%], with end-points shaded, on a number line.](https://i0.wp.com/activeevaluator.com/wp-content/uploads/2024/07/95-ci.jpg?resize=1024%2C206&ssl=1)
