HOW TO PRESENT DATA IN TABLES AND GRAPHS (CORRECTLY)

Presenting data correctly is essential in research because it makes your findings clear, readable, and easy to interpret. Well-designed tables and graphs help examiners, supervisors, and readers instantly understand your results.

This guide covers:

• How to design professional tables

• How to design and label graphs

• Common mistakes and how to avoid them

• Examples of correctly presented tables and charts

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1. GENERAL RULES FOR PRESENTING RESEARCH TABLES AND GRAPHS

Before going into details, ALWAYS follow these principles:

✔ Clarity

Readers should understand the table or graph without explanation.

✔ Simplicity

Avoid unnecessary lines, colors, or excessive numbers.

✔ Accuracy

Ensure numbers sum correctly and visuals reflect the data.

✔ Appropriate Labels

Every graph/table MUST have:

• Title

• Variables

• Units (if applicable)

• Source (if necessary)

✔ Interpretation

Tables and graphs must be followed by a brief narrative explanation.

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2. HOW TO PRESENT DATA USING TABLES

Tables are best for showing exact values, comparisons, and when multiple pieces of information need to be displayed in one place.

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A. ELEMENTS OF A GOOD TABLE

Every table must have:

1. Table Number
   Example: Table 4.1

2. Table Title
   Clear and descriptive.
   Example: Table 4.1: Distribution of Respondents by Gender

3. Column Headings
   Example: Gender, Frequency, Percentage

4. Body of the Table
   Data neatly arranged in rows and columns.

5. Total Row (if necessary)

6. Source (optional)
   Example: Source: Field Survey, 2025

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B. EXAMPLE OF A WELL-FORMATTED TABLE

Table 4.1: Distribution of Respondents by Gender

Gender	Frequency	Percentage (%)
Male	52	43.3
Female	68	56.7
Total	120	100.0

Source: Field Survey (2025)

How to interpret it:

Table 4.1 shows that the majority of respondents were female (56.7%), while males constituted 43.3% of the sample.

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3. HOW TO PRESENT DATA USING GRAPHS/CHARTS

Graphs help readers visualize patterns, especially when showing trends, comparisons, or proportions.

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A. COMMON TYPES OF GRAPHS AND WHEN TO USE THEM

1. Bar Chart

Use for comparisons between groups.

Example: gender distribution, departmental responses

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2. Pie Chart

Use to show proportions of a whole.

Example: percentage of respondents by age group

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3. Histogram

Use to show distribution of continuous data.

Example: test scores, income levels

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4. Line Graph

Use to show trends over time.

Example: unemployment rate from 1980–2024

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5. Scatter Plot

Use to show relationships between two variables.

Example: hours studied vs. exam score

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B. ESSENTIAL PARTS OF A GOOD GRAPH

Every graph MUST include:

✔ Graph number (e.g., Figure 4.1)
✔ Title
✔ Labelled X-axis
✔ Labelled Y-axis
✔ Units of measurement
✔ Legend (if multiple variables plotted)
✔ Clear scale intervals
✔ Source (optional)

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C. EXAMPLE OF A CORRECTLY PRESENTED BAR CHART

Figure 4.1: Gender Distribution of Respondents

[Imagine a bar chart with two bars: Male = 52, Female = 68]

Interpretation:

Figure 4.1 indicates that females (68) were more than males (52) in the study population.

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4. HOW TO DECIDE WHETHER TO USE A TABLE OR GRAPH

Use a TABLE when:

• Exact numbers are important

• You want to show multiple data categories at once

• The data has many variables

Use a GRAPH when:

• You want to highlight patterns or trends

• Visual comparison matters

• Showing proportional relationships

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5. BEST PRACTICES FOR TABLE AND GRAPH PRESENTATION

✔ Avoid clutter

Only include necessary data.

✔ Avoid 3D charts

They distort perception and reduce clarity.

✔ Maintain consistency

Stick to one style across all figures.

✔ Use whole numbers

Round percentages to one decimal place.

✔ Place interpretation BELOW the table/figure

Never let the reader interpret independently.

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6. COMMON MISTAKES TO AVOID

❌ Missing titles on tables or graphs
❌ No numbering (Table 1, Figure 1…)
❌ Using too many colors
❌ Inconsistent formatting
❌ Using mean for categorical variables
❌ Presenting graphs without interpretation
❌ Tables too large or cramped

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7. SAMPLE PROJECT-LIKE PRESENTATION

Below is how tables and graphs should appear in Chapter Four:

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Table 4.2: Respondents’ Level of Satisfaction with Service Delivery

Scale	Frequency	Percentage (%)
Very Satisfied	34	28.3
Satisfied	50	41.7
Neutral	18	15.0
Dissatisfied	12	10.0
Very Dissatisfied	6	5.0
Total	120	100.0

Interpretation:

Table 4.2 shows that a combined total of 70% (Very Satisfied + Satisfied) of respondents expressed satisfaction with service delivery.

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Figure 4.2: Respondents’ Level of Satisfaction with Service Delivery

(Bar chart representation of the above)

Interpretation:

Figure 4.2 visually confirms that most respondents were satisfied with the quality of service delivery.

 

HOW TO ANALYZE DATA USING DESCRIPTIVE STATISTICS

Descriptive statistics are used to summarize, describe, and present data in a meaningful way. They help you understand the basic patterns, trends, and characteristics of your dataset before moving to inferential statistics.

Descriptive statistics answer questions like:

• What is the average response?

• How spread out are the data?

• How many respondents selected each option?

• What are the dominant trends in the dataset?

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1. TYPES OF DESCRIPTIVE STATISTICS

Descriptive statistics are grouped into three main categories:

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A. Measures of Frequency

Describe how often something occurs.

Examples include:

• Counts (n)

• Percentages (%)

• Frequency distribution

• Mode (most frequent value)

Used in:

• Demographic analysis

• Questionnaire summaries

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B. Measures of Central Tendency

Describe the center of your data.

Main measures:

• Mean (average)

• Median (middle value)

• Mode (most common value)

Used when determining:

• Average satisfaction

• Average income

• Average test score

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C. Measures of Dispersion (Variability)

Describe how spread out the data are.

Key measures:

• Range

• Variance

• Standard deviation (SD)

• Minimum & Maximum values

Used to show:

• Consistency or inconsistency in responses

• How far data points deviate from the mean

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2. STEPS TO ANALYZE DATA USING DESCRIPTIVE STATISTICS

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STEP 1: Organize Your Data

Before analysis, ensure the data is clean and properly arranged.

Tasks include:

• Entering data into SPSS, Excel, or R

• Coding questionnaire responses (e.g., 1 = Yes, 2 = No)

• Removing missing or incorrect entries

• Ensuring all variables have proper labels

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STEP 2: Use Frequency Tables

Frequency tables show how many respondents selected each option.

Example:

Response	Frequency	Percentage
Yes	80	66.7%
No	40	33.3%
Total	120	100%

Useful for:

• Demographics

• Likert-scale data

• Categorical variables

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STEP 3: Compute Central Tendency (Mean, Median, Mode)

These help you state the average view of respondents.

Example:

Average score on a satisfaction scale:
Mean = 3.82 (on a 5-point scale)

Interpretation:

Respondents generally agreed that they are satisfied with the service.

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STEP 4: Compute Measures of Dispersion

These show how responses differ.

Example:

Standard deviation = 0.45

Interpretation:

Responses are consistent and tightly grouped around the mean.

High standard deviation = high variability
Low standard deviation = uniform responses

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STEP 5: Use Charts and Graphs

Graphs make the data easier to understand.

Common charts:

• Bar charts

• Pie charts

• Histograms

• Line Charts

Graphs are used for:

• Demographics

• Likert-scale summaries

• Trend descriptions

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STEP 6: Interpret the Results

This is the most important part. Interpretation is written in sentences.

Example:

The results show that 62% of respondents were female, while 38% were male.
The mean score of 4.12 indicates a high level of agreement that the institution has effective knowledge-sharing practices.
Standard deviation (SD = 0.53) suggests low variability in responses.

This is what you will write in Chapter Four (Data Presentation, Analysis, and Interpretation).

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3. HOW TO RUN DESCRIPTIVE STATISTICS IN SPSS

A. Frequency

Go to:
Analyze → Descriptive Statistics → Frequencies

B. Mean, SD, Variance

Go to:
Analyze → Descriptive Statistics → Descriptives

C. Charts

Go to:
Graphs → Chart Builder

SPSS outputs:

• Mean

• Median

• Mode

• SD

• Variance

• Frequency tables

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4. HOW TO PRESENT DESCRIPTIVE STATISTICS IN YOUR PROJECT

Your Chapter Four should include:

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A. Tables

Example format:

Table 4.2: Descriptive Statistics for Service Quality

Item	N	Mean	SD	Interpretation
The services are reliable	120	4.15	0.49	Agree

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B. Narrative Interpretation

Example:

The mean score of 4.15 (SD = 0.49) indicates that respondents generally agreed that the services offered were reliable. This suggests that the institution maintains a consistent level of service delivery.

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C. Charts

Use bar charts or pie charts to show distributions.

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5. COMMON MISTAKES TO AVOID

❌ Using mean for nominal data (e.g., gender)
❌ Ignoring standard deviation
❌ Presenting tables without interpretation
❌ Not cleaning the dataset before analysis
❌ Using too many tables (less is more!)

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6. WHAT YOU CAN USE DESCRIPTIVE STATISTICS FOR

Descriptive statistics allow you to:

✔ Summarize demographic characteristics
✔ Describe trends in responses
✔ Support inferential statistics
✔ Provide an overview of main variables