Lesson 6: Excel Advanced — Charting and Analyzing SD Method Data

Goals for This Lesson

• Use class-wide data to create charts
• Compare "preference" across 6 font samples using a bar chart
• Visualize impression profiles using a radar chart
• Interpret and discuss findings from the charts

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Instructor preparation

At the start of class: Distribute the class summary Excel file (exported from Google Forms, with average ratings per sample × item). Share via OneDrive or classroom system.

Suggested file structure:

• Sheet 1: All individual responses (rows = students, columns = sample × item)
• Sheet 2: Class averages (rows = samples, columns = rating items) ← students mainly use this

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1. Review the Class Data

1-1. Open the Distributed File

Open the Excel file shared by the instructor (e.g., Class_Summary.xlsx).

Go to Sheet 2 "Class Averages" and verify the table looks something like this:

	Bright↔Dark	Light↔Heavy	Friendly	Unique	Refined	Warm	Like
Sample A	2.1	2.4	1.9	3.2	3.8	2.3	1.8
Sample B	3.5	4.1	3.9	2.8	2.1	4.0	3.7
Sample C	1.4	1.7	1.5	3.1	4.2	1.6	1.9
Sample D	3.2	3.4	2.8	3.7	2.5	2.9	2.6
Sample E	2.8	2.9	3.2	2.4	1.9	3.1	2.8
Sample F	3.8	3.5	3.6	2.2	2.3	3.7	3.9

(These are example values — use the actual class data.)

1-2. Compare Your Ratings to the Class Average

Open your own file from last lesson alongside the class data (you can use two windows).

• Which samples did you rate similarly to the class?
• Where did your ratings differ most?

These individual differences are themselves a fascinating subject of research in the SD method.

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2. Bar Chart — Comparing "Preference" Across Samples

2-1. Prepare a Chart Data Table

First, create a small table using only the "Like↔Dislike" column.

In an empty area of the class data sheet (e.g., starting at column K), enter:

Cell	Content
K1	Font
L1	Like↔Dislike (class average)
K2	Sample A
K3	Sample B
K4	Sample C
K5	Sample D
K6	Sample E
K7	Sample F

In L2–L7, reference the "Like↔Dislike" column from the class averages table (e.g., enter =H2 in L2, adjusting cell references to match your actual data layout).

2-2. Insert a Bar Chart

1. Select the range K1 to L7
2. Go to "Insert" tab → "Charts" group → "Insert Column or Bar Chart"
3. Select "Clustered Column"

The chart appears on the sheet.

2-3. Format the Chart

Change the chart title:

1. Double-click "Chart Title" at the top of the chart
2. Replace the text with "Font Preference (Class Average)"
3. Click outside the chart to confirm

Add axis labels:

1. Click the chart to select it
2. Click the "+" icon (Chart Elements) that appears in the top-right corner
3. Check "Axis Titles"
4. Edit the vertical axis label to read "Rating (1=Like ↔ 5=Dislike)"

Reading the chart

A lower score means the class liked that sample more (1 = Like). A shorter bar = more preferred font.

Change bar colors (optional):

1. Click any bar to select the series
2. Right-click → "Format Data Series"
3. Under "Fill", choose your preferred color

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3. Radar Chart — Impression Profile

A bar chart is great for comparing one item across samples, but to see all 7 items at once, a radar chart (spider chart) is much more powerful.

3-1. Understanding Radar Charts

A radar chart places multiple axes radiating outward from a center, and connects the values on each axis to form a polygon.

• Larger polygon → overall higher ratings (leans toward right adjectives)
• Smaller polygon → overall lower ratings (leans toward left adjectives)
• Irregular shape → one or more items stand out strongly

3-2. Create a Radar Chart for One Sample

Let's visualize all 7 item ratings for Sample A.

Select the data:

1. In the class averages table, select the header row (B1–H1: "Bright↔Dark" … "Like↔Dislike")
2. Hold Ctrl and also select Sample A's row (B2–H2)

Insert the chart:

1. Go to "Insert" tab → click the small dropdown arrow near the chart options
2. Choose "All Charts" → "Radar" → "Radar" (without fill)
3. Click OK

3-3. Overlay Multiple Samples for Comparison

Select two or three samples with contrasting impressions and overlay them to see differences clearly.

Add a data series:

1. Right-click the chart → "Select Data"
2. Click "Add"
3. In "Series name," enter Sample B
4. In "Series values," select Sample B's row of ratings
5. Click OK → OK

Comparison tip

Try overlaying the most-liked sample with the least-liked one. Which specific items differ most? Does the shape of the impression profile explain why one is preferred?

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4. Compare Yourself to the Class

4-1. Create a Radar Chart from Your Own Data

Open your file from Lesson 5 and create a radar chart using your own ratings. Compare it side by side with the class average radar chart.

Questions to consider:

• Do you and the class agree on which sample feels "warm" or "cold"?
• Are there items where individual differences seem especially large?

4-2. Write Your Analysis in the Spreadsheet

Below your chart, write a short analysis in a cell. For example:

[Analysis]
- The most preferred sample for our class was Sample __, with an average of __.
- The biggest gap between my rating and the class average was for Sample __
  on the __ item (I rated it __, class average was __).
- From the radar chart, Sample __ has a strong "refined" impression,
  while Sample __ scores high on "friendly/warm."

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5. Possibilities and Limitations of the SD Method

5-1. Where the SD Method Is Applied

Field	Application
Product design	Measuring impression before/after packaging redesign
Graphic design	Providing data-backed rationale for logo and font choices
UI/UX design	Evaluating usability and feel of websites and apps
Music & film	Analyzing emotions conveyed by sound or visuals
Architecture & space	Measuring comfort and impression of environments
Marketing	Brand image research and competitive analysis

5-2. Things to Watch Out For

Limitations of the SD method

• Results vary by culture, age, gender, and experience of respondents
• The choice of adjective pairs strongly influences results (choosing appropriate pairs is difficult)
• Watch for the central tendency bias — respondents who always choose the middle (3)
• With too few respondents, statistical reliability is low

Being aware of these limitations when interpreting data is a critical skill for any researcher or designer.

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Submission

#	Item	Content	How to submit
1	Analysis report (Excel file)	Bar chart + radar chart + written analysis	Submit as instructed in class

Pre-submission checklist

• [ ] Does the bar chart have a title and axis labels?
• [ ] Does the radar chart show at least 2 samples overlaid?
• [ ] Is there a written analysis summarizing what you read from the charts?
• [ ] Does the file name include your student ID?

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Summary

Across these two SD method lessons, you learned:

1. SD method theory — measuring impressions numerically using bipolar adjective pairs
2. Data entry and aggregation — AVERAGE, MAX, MIN functions
3. Bar charts — comparing numeric values across categories
4. Radar charts — visualizing multi-variable impression profiles at a glance
5. Writing analysis — extracting objective insights from charts and expressing them in your own words

Sensory experience and aesthetic feeling, when expressed as data, become shareable, comparable, and improvable. This is the key perspective that connects art and design to the wider world.