Executive Summary

The report demonstrates the application of data visualisation on the given poster. The poster was extremely descriptive as it depicted the information for the number of respondents using emails. However, some aspects from the poster are entirely missing. This report will analyse the missing elements of the poster that makes it incomplete and vague. Hence, each component of the poster will be analysed individually in this report, respectively. Visualisation saves time by taking valuable information as an input and offering an outstanding and colourful image. The graphics, content analysis, colour combination and other issues will be discussed for improvisation as well as additional content. However, data visuals and content does not match each other or complement. Hence, the report will define competitive analysis for better changes successfully.

Contents

Executive Summary 1

Content Analysis 3

Negatives of the Poster 3

1. a) Color Combination 3
2. b) Graphics 4

Recommendation for Changes 6

1. a) Theory of visualisation 6
2. b) Theory of storytelling 6
3. c) Introduction of technique 6
4. d) Linking Variables 7
5. e) Storytelling in layers 7
6. f) The flow of ideas 7

Executive Summary

The report demonstrates the application of data visualisation on the given poster. The poster was extremely descriptive as it depicted the information for the number of respondents using emails. However, certain elements from the poster are entirely missing. This report will analyse the missing aspects of the poster that makes it incomplete and vague. Hence, each component of the poster will be analysed individually in this report, respectively. Visualisation saves time by taking valuable information as an input and offering an outstanding and colourful image. The graphics, content analysis, colour combination and other issues will be discussed for improvisation as well as additional content. However, data visuals and content does not match each other or complement. Hence, the report will define competitive analysis for better changes successfully.

Content Analysis 

The content analysis is described as, a research method which analyses written content based on words, themes and concepts (Zhang, Ding and Milojević, 2013). Additionally, it also highlights the meanings and relationships as well as their relevance to the topic.

1. Introduction: In this piece of content, the maker shows an email is a common form of communication which is used in every domain. However, survey introduction just explores the topic in general sense rather than narrowing scope.
2. Dataset Description: The dataset is a collection of data which is used to show and perform the statistical analysis. However, the lack of dataset usage properly could distract the audience from understanding the poster. Therefore, this heading also describes what given dataset was; still, it lacks to show the information through graphical representation of diagrams. The case wise method is not shown or elaborated further so that the audience could understand how it was implemented.
3. Analysis: In this section, the common answers are shown for review; however, did not match the graphical circular graph in any way. It indicates only theoretically explanations are given, which still lacks the complete usage of email. The data has more information; however, it is neither explored nor represented. Further, the content for the analysis section is much bigger and irrelevant. The ninety per cent of people from the higher education sector did not attend any email usage training sessions.  It could be noticed that Figure 1 just highlight the pie chart, but percentage mention is not present.
4. Conclusions and Recommendations:The emerging pictures description in this section is wrong because images do not match dataset completely. The other technical issues of using email should also be part of the training session. However, survey data has not taken this part seriously because of no objective research.

Negatives of the Poster

a) Color Combination

The colour combination for the professional poster should not be RAG colours as per the suggestions offered. The poster colour combination must be light and show the information clearly and concisely (Chang et al., 2018). However, the poster maker has used a green background as a colour combination.

Furthermore, rather than using different colours, one single colour brings darkness. Therefore, it is essential to identify which colours are professionally used and not. The data visuals are just placed in between which any line or section division. Hence, colour is vital to factor and should be kept in mind before making statistical analysis content posters.

b) Graphics 

In the present poster, we find that there are two graphs. Although chart titles are present on both the graphs, the font size of the titles vary. Thus, while the caption of “figure 1” is distinctly visible, the caption for “figure 2” is not so noticeable.

The title for the 1^st Figure is given as “Main drawbacks.” The title of a chart should be self-explanatory. From the title, the audience should be able to understand the proposition of the chart. In the present pie chart, the title is not able to depict the presentation. Pie charts are either 2D or 3D. In the current poster, a 2D plot is presented.

Further, the first variable is represented in the vertical position. It is usually a good practice which has been followed in the chart. However, from the pie chart, we find that there are many categories which are represented. As such, the proportion of each category in the population cannot be deciphered. A pie chart can best be used when a maximum of three categories are represented.

Moreover, the categories are not well represented. Of the 12 categories which are presented in the chart, two are fully documented, single alphabets represent rest. Thus, the pie chart is neither the best form to represent the variable, nor has it been well documented.

The 2^nd Figure presented in the poster is a bar chart. However, the title of the graph “Training uncommon” is unclear.  From the title, one cannot interpret what the Figure is about. A better title could have been “Frequency of training scores.” Also, the plot is used to represent only two factors – “yes” and “no” for the q_22_score. The better process to represent the two factors would have been the use of a pie chart rather than a bar chart. The proportion of “yes” to “no” could have been represented by the proportion of the slice. In addition, the percentage proportion could have been represented in the pie. In the chart, the x-labels and y-labels seem to be in the opposite places. While the x-axis represents the response of the survey participants towards q 22, 1 or 2, the bales have been placed on the y-axis. Similarly, while the score is represented in the y-axis, the label is placed on the x-axis. Further, the width of the bars is not adequate.

Thus, we find that both the charts used in the poster are incorrectly made. Further to our analysis, we find that the speaker reports that “most variables are categorical and answers are coded on a scale, e.g. some response is coded as, 2.” In a categorical variable there must be more than one category– only one numerical “2” has been giving.  Moreover, the answers are not coded on a scale; rather, the range is represented through categories. The speaker further reports that the present data is “time-series data.” However, no time-series charts are presented to justify his claim. Therefore, suggestions are made for incorporating the following types of data visuals effectively.

• Pie Chart: It is visual that show division of whole 100 per cent pie into circular slice division.

• Line Graph: It is a single line represents the curve of plots for variables on x and y-axis.

• Time Series Chart: The time series chart showcase variables on x and y-axis in context with time and date graph curve.

Recommendation for Changes

a) Theory of visualisation

Visualisation theory states that a visual should help to reveal knowledge as well as provide facility pathways to some insight (). In other words, visualisation showcase respective elements of size, colour and alignment in the representation. Comparatively, poster maker has forgotten to showcase and adopt visualisation theory. For example, he has forgotten to use figure 1 pie chart properly due to a large number of slices. Hence, element size is not proportionate to the original 100 per cent.

b) Theory of storytelling 

Storytelling narrative is another concept which depicts a process journey for a given scenario. However, the lack of story produces confusion and chaos to the poster. The maker has forgotten to depict what the topic is saying to the audience.  As per the poster, email is an important part of communication still reasons could be narrated effectively.

c) Introduction of technique

Poster making is an effective technique which displays a concept through graphics, visuals, and colour combination. Still, no incorporation of technique properly makes poster vague and unclear to understand or acquire knowledge. For instance, the below example shows no clear structure, excluding key information as well as design consistency for text (Hess, Tosney and Liegel, n.d.). Similarly, the poster maker has not covered every key information related to topic making structure unclear and lacking.

d) Linking Variables

The linking of variables used on the data visuals are another important part of graphs. However, if used variables are not present or mapped properly, it will create representation issues. As compared with a poster, scores are not showing appropriate to the x and y-axis.

e) Storytelling in layers 

There are different layers of storytelling which collectively show a journey for incidents. Hence, the layered approach should be followed for storytelling that depicts what, when and how the narrative occurred (Goldhammer, 2013). However, compared to the poster, this approach seems missing on content analysis. In the introduction, the email’s negative and positive points are offered by the maker. However, what is email and how it is a form of effective communication for education is missing.

f) Flow of ideas

The flow of ideas depicts that concept or topic should be structured and connected. However, the flow of idea is missing from the poster due to multiple thoughts represented. Therefore, the flow of ideas should be interconnected with each other and speak for one topic only.

References

Chang, C., Xu, K., Guo, C., Wang, J., Yan, Q., Zhang, J., He, F. and Zhu, Y. (2018). PANDA-view: an easy-to-use tool for statistical analysis and visualisation of quantitative proteomics data. Bioinformatics, 34(20), pp.3594-3596.

D’Ignazio, C. and Klein, L.F., (2016). October. Feminist data visualisation. In Workshop on Visualisation for the Digital Humanities (VIS4DH), Baltimore. IEEE.

Goldhammer, G. (2013). The End of “Content” — Storytelling Using the Layered Narrative System. [online] Gary Goldhammer – Below the Fold. Available at: https://garygoldhammer.com/2013/02/14/the-end-of-content-storytelling-using-the-layered-narrative-system/ [Accessed 12 Feb. 2020].

Hess, G., Tosney, K. and Liegel, L. (n.d.). HOW TO DISTINGUISH A GOOD POSTER DESIGN FROM A BAD ONE.

Tomita, K. (2017). Visual Design Tips to Develop an Inviting Poster for Poster Presentations. TechTrends, 61(4), pp.313-315.

Zhang, G., Ding, Y. and Milojević, S. (2013). Citation content analysis (CCA): A framework for syntactic and semantic analysis of citation content. Journal of the American Society for Information Science and Technology, 64(7), pp.1490-1503.