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