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A Step-by-Step Guide to Data Analysis Using SPSS for Beginners
You have finished your field work, and you have 300 questionnaires sitting on your desk. Now what? For most students, the sight of SPSS (Statistical Package for the Social Sciences) is intimidating. But data analysis is simply a language—once you learn the alphabet, the rest is easy.
### Step 1: Variable View vs. Data View
Before you enter a single number, you must define your "Variables." In the Variable View, you tell the computer what your questions are. Is it a "Nominal" scale (like Gender) or an "Ordinal" scale (like a Likert scale: Strongly Agree to Strongly Disagree)? Setting this up correctly is 70% of the work. If you mess up the variable type, your final charts will be meaningless.
### Step 2: Data Cleaning
Never analyze raw data immediately. Humans make mistakes. You might find a "3" entered where only "1" or "2" were options. Data cleaning involves running "Frequencies" to find these outliers and correcting them. This ensures your final thesis isn't rejected due to "dirty data."
### Step 3: Descriptive vs. Inferential Statistics
Descriptive statistics (Mean, Median, Frequency) tell you what happened in your sample. Inferential statistics (T-tests, ANOVA, Regression) tell you if what happened is actually *significant* or just a fluke. This is where the famous "p < 0.05" comes in. If your p-value is less than 0.05, you have a story to tell! If this sounds like Greek to you, don’t worry—uniSupport offers full-service data analysis where we handle the coding, the running of the tests, and the interpretation of the results.
### Step 1: Variable View vs. Data View
Before you enter a single number, you must define your "Variables." In the Variable View, you tell the computer what your questions are. Is it a "Nominal" scale (like Gender) or an "Ordinal" scale (like a Likert scale: Strongly Agree to Strongly Disagree)? Setting this up correctly is 70% of the work. If you mess up the variable type, your final charts will be meaningless.
### Step 2: Data Cleaning
Never analyze raw data immediately. Humans make mistakes. You might find a "3" entered where only "1" or "2" were options. Data cleaning involves running "Frequencies" to find these outliers and correcting them. This ensures your final thesis isn't rejected due to "dirty data."
### Step 3: Descriptive vs. Inferential Statistics
Descriptive statistics (Mean, Median, Frequency) tell you what happened in your sample. Inferential statistics (T-tests, ANOVA, Regression) tell you if what happened is actually *significant* or just a fluke. This is where the famous "p < 0.05" comes in. If your p-value is less than 0.05, you have a story to tell! If this sounds like Greek to you, don’t worry—uniSupport offers full-service data analysis where we handle the coding, the running of the tests, and the interpretation of the results.