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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
Before going into details, ALWAYS follow these principles:
Readers should understand the table or graph without explanation.
Avoid unnecessary lines, colors, or excessive numbers.
Ensure numbers sum correctly and visuals reflect the data.
Every graph/table MUST have:
Title
Variables
Units (if applicable)
Source (if necessary)
Tables and graphs must be followed by a brief narrative explanation.
Tables are best for showing exact values, comparisons, and when multiple pieces of information need to be displayed in one place.
Every table must have:
Table Number
Example: Table 4.1
Table Title
Clear and descriptive.
Example: Table 4.1: Distribution of Respondents by Gender
Column Headings
Example: Gender, Frequency, Percentage
Body of the Table
Data neatly arranged in rows and columns.
Total Row (if necessary)
Source (optional)
Example: Source: Field Survey, 2025
| Gender | Frequency | Percentage (%) |
|---|---|---|
| Male | 52 | 43.3 |
| Female | 68 | 56.7 |
| Total | 120 | 100.0 |
Source: Field Survey (2025)
Table 4.1 shows that the majority of respondents were female (56.7%), while males constituted 43.3% of the sample.
Graphs help readers visualize patterns, especially when showing trends, comparisons, or proportions.
Use for comparisons between groups.
Example: gender distribution, departmental responses
Use to show proportions of a whole.
Example: percentage of respondents by age group
Use to show distribution of continuous data.
Example: test scores, income levels
Use to show trends over time.
Example: unemployment rate from 1980–2024
Use to show relationships between two variables.
Example: hours studied vs. exam score
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)
[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.
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
Only include necessary data.
They distort perception and reduce clarity.
Stick to one style across all figures.
Round percentages to one decimal place.
Never let the reader interpret independently.
❌ 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
Below is how tables and graphs should appear in Chapter Four:
| 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.
(Bar chart representation of the above)
Interpretation:
Figure 4.2 visually confirms that most respondents were satisfied with the quality of service delivery.
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?
Descriptive statistics are grouped into three main categories:
Describe how often something occurs.
Examples include:
Counts (n)
Percentages (%)
Frequency distribution
Mode (most frequent value)
Used in:
Demographic analysis
Questionnaire summaries
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
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
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
Frequency tables show how many respondents selected each option.
| Response | Frequency | Percentage |
|---|---|---|
| Yes | 80 | 66.7% |
| No | 40 | 33.3% |
| Total | 120 | 100% |
Useful for:
Demographics
Likert-scale data
Categorical variables
These help you state the average view of respondents.
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.
These show how responses differ.
Standard deviation = 0.45
Interpretation:
Responses are consistent and tightly grouped around the mean.
High standard deviation = high variability
Low standard deviation = uniform responses
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
This is the most important part. Interpretation is written in sentences.
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).
Go to:
Analyze → Descriptive Statistics → Frequencies
Go to:
Analyze → Descriptive Statistics → Descriptives
Go to:
Graphs → Chart Builder
SPSS outputs:
Mean
Median
Mode
SD
Variance
Frequency tables
Your Chapter Four should include:
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 |
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.
Use bar charts or pie charts to show distributions.
❌ 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!)
Descriptive statistics allow you to:
✔ Summarize demographic characteristics
✔ Describe trends in responses
✔ Support inferential statistics
✔ Provide an overview of main variables
The Chi-Square (χ²) test is a non-parametric statistical test used to determine whether there is a significant association between two categorical variables (e.g., gender, marital status, satisfaction level, income category).
It is one of the most commonly used tools in social sciences, management, education, public health, and business research.
Use Chi-Square when:
Your data is categorical (e.g., Yes/No, Male/Female)
You want to test for relationship/association between variables
Example: Is there a significant relationship between gender and voting behaviour?
Sample size is moderate or large (usually ≥ 20)
Observations must be independent
There are two main types:
Used when you want to know if two variables are related.
Example: Is there a relationship between educational level and job satisfaction?
Used when you want to know if observed frequencies fit expected frequencies.
Example: Do students equally prefer the 4 faculties in the institution?
To use Chi-Square:
Data must be presented in a frequency table (contingency table).
Categories should be mutually exclusive (no overlap).
Expected frequency in each cell should be ≥ 5 (for reliability).
For Chi-Square test of independence:
χ2=∑E(O−E)2Where:
O = Observed frequency (data you collected)
E = Expected frequency (calculated value)
Expected frequency is calculated as:
E=Grand Total(Row Total×Column Total)You always test for independence (no relationship).
There is no significant relationship between Variable A and Variable B.
There is a significant relationship between Variable A and Variable B.
Example: Relationship between Gender and Product Preference
| Gender | Prefer Product A | Prefer Product B | Total |
|---|---|---|---|
| Male | 30 | 20 | 50 |
| Female | 25 | 45 | 70 |
| Total | 55 | 65 | 120 |
This is your observed (O) data.
Use the formula:
E=Grand Total(Row Total×Column Total)Example: For Male + Product A:
E=12050×55=22.92You calculate E for each of the 4 cells.
Apply:
χ2=∑E(O−E)2Do this for each cell and sum the results.
Where:
r = number of rows
c = number of columns
Example: 2 rows, 2 columns:
df=(2−1)(2−1)=1If using statistical software (SPSS, R, Excel), you get a p-value automatically.
If p-value < 0.05 → Reject H₀ → Significant relationship exists
If p-value > 0.05 → Fail to reject H₀ → No significant relationship
Write your results in APA-style format:
The Chi-square test showed a significant relationship between gender and product preference
(χ² = 12.46, df = 1, p < 0.05).
This indicates that product preference varies significantly by gender.
Go to Analyze
Select Descriptive Statistics → Crosstabs
Move one variable to Rows, the other to Columns
Click Statistics, then tick Chi-square
Click OK
SPSS outputs:
Pearson Chi-Square value
Degrees of freedom
p-value
You interpret the p-value.
❌ Using continuous variables (e.g., age) without categorizing them
❌ Small sample sizes with expected frequency < 5
❌ Using Chi-Square for paired or dependent data
❌ Interpreting Chi-Square as measuring the strength of relationship
(Use Cramer's V for strength)
Mention data type is categorical
Mention you used Chi-Square to test relationships
Justify because assumptions are met
Present contingency table
Present χ², df, and p-value
Provide interpretation
Compare your findings with previous studies
A survey is one of the most widely used methods for collecting primary data in academic and professional research. It involves systematically gathering information from a sample of individuals to understand attitudes, opinions, behaviors, characteristics, or experiences. Conducting a high-quality survey requires careful planning, designing, sampling, data collection, and analysis.
This guide explains step-by-step how to conduct a survey that produces accurate and credible research findings.
Before collecting data, clearly state what you want to achieve.
What problem am I investigating?
What information do I need from respondents?
What decisions or conclusions will the survey help me reach?
“To determine factors influencing customer satisfaction in Nigerian banks.”
Your objectives will guide your questionnaire, sampling strategy, and analysis.
The population is the total group of people your study aims to understand.
Examples:
All undergraduate students in a university
All customers of a supermarket
All residents of a particular community
All employees in an organization
A clear population definition ensures that your sample represents the right group.
Since surveying an entire population is often impossible, you draw a sample.
Simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling
These techniques allow generalization of results to the entire population.
Convenience sampling
Purposive sampling
Snowball sampling
Quota sampling
Useful when the population is difficult to access or when time/resources are limited.
Use formulas such as Cochran’s or Krejcie & Morgan’s table, or use online tools like:
Raosoft Sample Size Calculator
Qualtrics Sample Size Tool
The questionnaire is the main instrument for collecting survey data. A good questionnaire must be clear, concise, and relevant to objectives.
Example:
Age
Gender
Education
Occupation
Use:
Close-ended questions (Yes/No, multiple choice)
Likert-scale questions (Strongly Agree → Strongly Disagree)
Ranking questions
Rating questions
Simple and easy to understand
Neutral (avoid bias or leading questions)
Focused on one idea at a time
Free from technical language
Example order:
Demographics
General questions
Specific questions
Sensitive questions near the end
Give your survey to 5–10 people similar to your target respondents to ensure:
Questions are clear
Length is manageable
Instructions are easy
Revise based on feedback.
Surveys can be conducted using:
Used in schools, workplaces, and field studies.
Using platforms such as:
Google Forms
SurveyMonkey
Microsoft Forms
Typeform
Online surveys are fast, cost-effective, and automatically save responses.
Useful for hard-to-reach populations.
Good for communities with low literacy or no internet access.
Choose the method that best fits your population and available resources.
During administration:
From:
Supervisors
Ethics committees
Organizational authorities
Briefly tell them:
Why the survey is being conducted
That participation is voluntary
Their answers will remain confidential
Avoid influencing respondents’ answers. Maintain neutrality.
By:
Sending reminders
Keeping the questionnaire short
Offering small incentives (if allowed)
Before analysis:
Such as:
Excel
SPSS
R
Python
STATA
Remove incomplete responses
Correct typing errors
Handle missing data
Code qualitative responses
E.g., a respondent cannot select:
“Age: 12” and
“Marital status: Married”
Your analysis depends on your research objectives.
Used to summarize data:
Mean
Frequency
Percentage
Standard deviation
Used to test hypotheses:
Chi-square test
Correlation analysis
T-tests
Regression analysis
ANOVA
Using charts:
Bar charts
Pie charts
Histograms
Line graphs
Software options include Excel or SPSS.
Explain what your findings mean in relation to:
Your research questions
Your hypotheses
Existing literature
Key trends
Relationships between variables
Significant findings
Unexpected patterns
Limitations of your survey
Implications of results
Use tables, charts, and quotes (if open-ended questions were included) to enhance clarity.
Based on your survey findings:
Summarize major insights.
Answer your research questions directly.
Suggest practical recommendations for policymakers, organizations, or future researchers.
Conducting a survey for project research involves careful planning, designing an effective questionnaire, selecting an appropriate sample, gathering responses professionally, and analyzing data accurately. A well-designed survey enhances the quality, validity, and credibility of your research findings. When properly executed, surveys provide rich information that can guide decision-making, solve real-world problems, and contribute to academic knowledge.
Qualitative data analysis involves systematically examining non-numerical data—such as interview transcripts, focus group discussions, observation notes, or open-ended survey responses—to identify patterns, themes, meanings, and insights. Unlike quantitative analysis, which is statistical, qualitative analysis is interpretive, subjective, and iterative. The goal is to understand participants’ experiences, perceptions, beliefs, and motivations.
Qualitative analysis is especially useful in studies focused on attitudes, behaviors, lived experiences, social interactions, policy evaluation, and exploratory research.
Before analyzing qualitative data, you must ensure that your raw data is properly organized.
If you conducted:
Interviews, produce a word-for-word transcript.
Focus groups, include speaker labels (e.g., Participant 1, Participant 2).
Audio/video recordings, convert to text (manual or software-based).
Accuracy is crucial—transcription errors can distort findings.
Read through the transcript multiple times to gain an overall sense of:
Participants’ viewpoints
Repeated concepts
Strong emotions
Contradictions or unique ideas
At this stage, write memos or initial notes in the margins.
Use:
Microsoft Word
Excel
NVivo
ATLAS.ti
MAXQDA
Dedoose
Proper organization makes interpretation easier.
Coding is the backbone of qualitative analysis. It involves labeling chunks of text so that you can categorize and interpret them.
This is the first-level coding where you break the text into smaller parts and assign labels based on:
Key words
Concepts
Actions
Emotions
Observations
Example:
Transcript: “I waited for three hours before seeing the doctor.”
Code: long waiting time
Here, you group related codes to form categories.
Example:
long waiting time
slow service
staff shortage
→ Category: Operational Inefficiency
At this stage, you integrate categories into broader themes that represent the underlying patterns.
Example:
Theme: Patient Dissatisfaction with Healthcare Delivery
Descriptive codes – summarize the topic
Process codes – words ending in “–ing” describing actions
Emotion/Value codes – express feelings or beliefs
In vivo codes – participants’ own words
Themes represent the major ideas emerging from the data.
Look for repetition across participants
Identify contradictions
Compare responses across gender, age, job role, or other segments
Examine participant language (metaphors, strong statements)
Note unusual or surprising insights
Themes must:
Be meaningful
Be grounded in data
Answer the research question
Be supported by direct quotations
Theme: “Lack of Trust in Management”
Supporting categories:
ineffective communication
broken promises
poor conflict resolution
Focus groups provide group-level insights, which can be analyzed by:
Notice:
Consensus
Contradictions
Dominant participants
Minority voices
If you conduct multiple groups (e.g., Group A vs Group B), compare:
Similarities
Differences
Unique comments
This enhances the depth of findings.
Researchers can choose from several established approaches:
Steps (Braun & Clarke, 2006):
Familiarization
Coding
Generating themes
Reviewing themes
Defining and naming themes
Writing the report
Focuses on counting codes, words, or categories.
Useful for media studies, open-ended questionnaires, policy analysis.
Analyzes stories or experiences—how people construct meaning.
A systematic approach leading to the development of a new theory.
Includes open, axial, and selective coding.
Focuses on lived experiences.
Used in psychology, sociology, nursing research.
Analyzes language use, power relations, and communication context.
Quantitative studies use validity and reliability; qualitative studies use:
Member checking
Prolonged engagement
Triangulation (use multiple sources or methods)
Provide thick descriptions so others can judge relevance.
Document your decisions (audit trail).
Ensure neutrality; avoid personal bias.
Qualitative results must be presented in a clear academic format.
Introduce each theme with:
Explanation
Supporting quotes
Interpretation
Example:
“The workload is overwhelming; we barely rest.”
Use pseudonyms or codes to protect identity.
Discuss how themes support or contradict existing research.
Tables can show:
Themes
Sub-themes
Sample quotes
NVivo
ATLAS.ti
MAXQDA
Dedoose
QDA Miner
These help:
Store and organize data
Code text efficiently
Generate word clouds
Visualize themes
Microsoft Word (comments)
Excel (coding matrix)
Colored highlighters
Analyzing qualitative data (interviews, focus groups) is a structured, interpretive process that involves transcription, coding, theme development, and interpretation. The goal is not to count numbers but to understand experiences, motivations, perceptions, and meanings. By following a systematic approach—familiarization, coding, categorization, theme development, interpretation, and validation—you produce high-quality qualitative findings that are trustworthy, rigorous, and academically credible.
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