4. Lecture 4 - Data management and SPSS Practices

Quản lý dữ liệu bằng SPSS

Lecture 4:

Database Management &

SPSS Practices

• SPSS interface: Data and Variables

• Data entering in SPSS

• Data import from excel & other programs

• Data cleaning, Variable modification

• Data filtering

• Descriptive statistics

Contents

• Data entering window:

• Code book & data entering guidance

• Assign each observation

(interviewee/questionnaire…) and ID

• If it is an online survey, we need to

define each observation

• We can enter data using Excel

Data Entering into SPSS

Data coding: an example

A code book is about how you code your variables What are in code book?

1.Variable names

numerical, string

4 Variable description

Variables Define

Data window

Observation Entering

Open this

1 There are 2 variables in the dataset

2 Variables: “Code” & “Q01”.

3 Variable “Code” is an ID of a specific

observation

4 Variable “Q01” is a age variable: 1 = or < 12

years old; 2 = 13; 3 = 14 years old…

• click File Open Data

• choose Excel as the source of data

• Choose file path/location/addrees

• Click Open

Importing Data from Excel

Importing Data from Excel

• Checking variable names, and values: Checking variable

min, max, unit, etc

• Do “ frequency analysis’ for each frequency analysis

variables

• Examine results: e.g with gender

variable 1 = male, 0 = female frequency

analysis produces results for 0 & 1 If

there is value of 2, 3… appearing -> there

is some data entering mistakes

Data Cleaning

• Checking for “missing values” in

database, then checking with

questionnaires.

• Data cleaning -Ddescriptive statistics

analysis:

Data Cleaning

Data Cleaning

Data Cleaning

* Data cleaning: Frequency Table

=> Cleaning data:

In data_view window: Choose/highlight column

gioitinh => click “Edit” => Find: entering wrong

value (e.g 20)

Data Cleaning

Data Cleaning

“Compute” new Variables

“Compute” new Variables

• Sorting data: Data Sort Cases

• Sorting data can be used to identify “missing values”

Sorting & Selecting

observations

• Select observations

– E.g.: Select all female observation for

analysis, selecting people from Dong

Thap province

1 Data Select Cases

2.Select: second one

Sorting & Selecting

observations

• Descriptive statistics:

– Purposes:

1.Finding mistakes in data entering

2.Getting basic information of variables

3.Good summary of dataset

Analyze Descriptive statistics

Frequency

Descriptive Statistics

Analysis

Descriptive Statistics

Analysis

Descriptive Statistics

Analysis

Descriptive Statistics

Analysis

Descriptive Statistics

Analysis

Descriptive Statistics

Analysis

Basic Statistics Analysis

• Comparing “means”

Basic Statistics Analysis

• Comparing “means”:

Basic Statistics Analysis

• Comparing “means”:

Basic Statistics Analysis

• Comparing “means”:

Basic Statistics Analysis

• Comparing “means”:

Basic Statistics Analysis

• Comparing “means”:

Basic Statistics Analysis

• Comparing “means”:

Basic Statistics Analysis

• Correlation analysis: Analyze->Correlate ->

Bivariate

Basic Statistics Analysis

• Correlation analysis: Analyze->Correlate ->

Bivariate

Basic Statistics Analysis

• Correlation analysis: Analyze->Correlate ->

Bivariate

Regression Analysis

• Analyze->Regression -> Linear

Regression Analysis

• Select variables:

Regression Analysis

Regression Analysis

Regression Analysis