- 618 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about 'Assumption University' - bernad

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

Assumption University

1. Close-ended Question

The question that gives the choices for respondent to select.

1.1 The question that the respondents can choose only one answer, setting only one variable and the number of code will be as much as the number of choice.

Ex: question concerning with sex sex ( ) 1. male ( ) 2. female

variable name SEX

setting:code 1 for ‘male’ and code 2 for‘female’

* The width of sex variable will be 1 column

1.2 The question that can be choose more than one

Ex: Who is/are your favorite football players? (can choose more than one)

( ) 1. David Beckham (code 0 = dislike/ code 1 = like)

( ) 2. Michael Owen (code 0 = dislike/ code 1 = like)

( ) 3. Luis Figo (code 0 = dislike/ code 1 = like)

( ) 4. Ruud Van Nistelrooy (code 0 = dislike/ code 1 = like)

( ) 5. Raul Gonzalez (code 0 = dislike/ code 1 = like)

( ) 6. Zinedine Zidane (code 0 = dislike/ code 1 = like)

( ) 7. Michael Ballack (code 0 = dislike/ code 1 = like)

( ) 8. Ronaldo (code 0 = dislike/ code 1 = like)

( ) 9. Oliver Kahn (code 0 = dislike/ code 1 = like)

( ) 10. Ronaldinho (code 0 = dislike/ code 1 = like)

In this case, the variable setting will depend on the required analyzing result. If researchers want to know the number and percentage of each choice, the researchers have to set number of variables to be equal to number of choices. So, each variable will has only 2 codes;

1 selected

0 non-selected

The width of each variable will be 1 column as follows:

PLAYER1 : David Beckham(code 0 =non-selected / 1 = selected)

PLAYER2 : Michael Owen(code 0 =non-selected / 1 = selected)

PLAYER3 : Luis Figo (code 0 =non-selected / 1 = selected)

Multiple Dichotomy method

1.3 The question that asks respondents to rank the importance

A. Define each variable as a rank

B. Define each variable as a choice

Ex:Please rank the first three football players do you like?

A. >Define variable as a rank ,total number of variables will be 3

rank1 = 1st favorite

rank2 = 2nd favorite

rank3 = 3rd favorite

* each variable will has 10 values (number of choices)*

B. Define each variable as a choice.

Total number of variables will be 10 which equal to number of choices ( each variable will has 3 values).

Ex: PLAYER1 = David Beckham (possible value is 3 )

1 = 1st favorite

2 = 2nd favorite

3 = 3rd favorite

Multiple Category method

2. open-ended question

A>Digit data:you can setcode as value of data such as weight, height,

income, and age.

Ex1. How old are you?Please specify……AGE…..years old.

B> Text data:you can set code for variable or keying code after you

get the data. Then you have to divide data into groups

and set code for each group.

Ex2: what is the important problem on developing sport in Thailand?

Please specify ………………..PROBLEM 1…………………

How to set missing data

The collected questionnaires can be easily found that the respondents will leave out some questions which may intend, cannot answer, or forget to answer.

In this case, the researchers have to set codeof variable that respondents leave out to be

“9” or “99” or “999”

depended on the width of each variable and we called it “user-missing value”

in questionnaires.(Distribute the questionnaire)

Set variables of your questionnaire and then complete the questionnaire

How to set up data file fromSPSS for Windows

The Data Editor provides 2 views of data:

Data View. Display the actual data values.

VariableView.

Displays variable definition information, including defined variable and value labels, data type, measurement scale and user-defined missing values.

**In both views, you can add, change, and delete information contained in the data file.**

Rows are cases. Each row represents a cases or an observation.

Columns are Variables.

Each column represents a variables, each item on a questionnaire is a variable.

Cell Contain values.

Each cell contains a single value of a variable for a case. The cell is the intersection of the case and variable.Cells contain only data values,cannot contain formulas.

From Variable view

you can add or delete variables and modify attribute of variables, including

Name :Variable name

Type:Data type

Width:Number of digits or characters

Decimal:Number of decimal places

Label:Descriptive variable

Values :Value labels

Missing:User-defined missing values

Column:Column width

Measure:Measurement scale

in SPSS. (Open SPSS )

Set variables by using SPSS.

Entering Data

you can enter data directly in the Data Editor in the Data View.You can enter data in any oder.You can enter data by case or by variable,for selected area or for individual cells.

- The active cell is highlighted.

- Data values are not record until you press ‘Enter’ or select another cell.

- To enter anything other than simple numeric data,you must define the variable type first.

If you enter a value in an empty column,the Data Editor automatically creates a new variables and assigns a variable name as ‘var001’.

Editing Data in Data View

with the Data Editor, you can modify data values in the Data view in many ways.

You can…

- Change data values.

- Cut, Copy, and paste data values.

- Add and delete cases.

- Add and delete variables.

- Change the order of variables.

Entering your data.

The assignment will be posted on the website by tomorrow afternoon (i.e., 3pm.)

by using ‘SPSS’

Frequencies

“The Frequencies” procedure provides statistics and graphical displays that are useful for describing many types of variables.

Data: Use numeric code or short strings to code categorical variables (nominal or ordinal level measurement)

To obtain frequencies and Satistics:

From menus choose: Data

Descriptive Statistics

Frequencies…….

- Select one or more categorical or quantitative variables.
- Optionally,you can:
- Click statistics for descriptive statistics for quantitative variables.
- Click Charts for bar chart, pie charts, and histograms.
- Click Format for the order in which results are displayed.

Descriptives

“The Descriptives” procedure displays univaiate summary statistics for several variables in a single table and calculates standized values (Z score).Variables can be ordered by the size of their means, alphabatically,or by order in which you select the variables (default)

Data: Use numeric variables after you have screened them

graphically for recoding errors, outliers. The descriptives

procedure is very efficient for large file (thousands of cases).

To obtain Descriptives Satistics:

From menus choose: Analyze

Descriptive Statistics

Descriptives…….

- Select one or more variables.
- Optionally,you can:
- Click Save standized values as variables to save z score as new variables.
- Click Options for optional statistics and display order.

Crosstabulation

“The Crosstab” procedure forms two-way amd multiway tables

and provides a variety of tests and measurement of the tables.

Data: To define the categoriess of each table variable, use value of

numeric or short string (eight or fewer charactors) var.

Assumption:Some statistics and measures assume ordered

categories (ordinal data) or quantitative values

(interval or ratio), as discuss in the section on

the statistics.

To obtain Crosstabulations:

From menus choose: Analyze

Descriptive Statistics

Crosstab…….

- Select one or more row variables and one or more column variables.
- Optionally, you can:
- Select one or more control variables.
- Click Statistics for test and measures of association for two way tables or subtables.
- Click Cells for observed and expected values, percentage, and residuals
- Click Format for controlling the order of categories.

“Inference statistic” will be used to summarize data from sample group by using theory of possibility in analyzing for summarizing result to sample group. Normally, selecting of statistic depends on objective, type of variables, research form, and basic agreement of each statistic which used in such analysis. For example: inference statistic often used in general research such as population mean test, variance analysis, correlative analysis, and linear regression analysis.

2.1 The mean test of 1 population by using

One – Sample T Test

‘The one sample T-Test’procedure tests whether the mean of a single variable differs from from a specified constant.

Data:To test the values of a quantitative variable

against a hypothesized test value, choose

a quantitatives variable and enter a hypothesized

test value.

To obtain One sample T-Test:

From menus choose: Analyze

Compare Means

One –Sample T Test…

Select one or more variables to be tested against the same hypothesis value.

Enter a numeric test value against which each sample mean is compared.

2.2 The mean test of 2 independent populations

by using Independent – Sample T Test

Comparesthe means of one variable for two group of

cases. Descriptive Statistics for each group and Levene’s test

for equality of of variance are provided, as well as both equal

and unequal variance t values and a 95% confidence interval

for the different in means.

Data:The values of the quantitative variable of interest are in a single column in the data file. The procedure uses a grouping variable with two values to separate the cases into two groups. The grouping variable can be numeric or short string.

To obtain Independent –Sample T Test:

From menus choose: Analyze

Compare Means

Independent-Sample T Test…

Selectone or more quantitative test variables. A separate t test is computed for each variable.

Select a single grouping variable, and click Define Groups to specify two codes for the groups you want to compare.

2.3 The test of 2 related populations

by using Paired-Sample T Test

The Paired-Sample T Test procedure compares

the means of two variables for a single group. It

computes the differences between values of two variables

for each case and test whether the average differs from 0.

Data: For each paired test, specify two quantitative variables (interval or ratio scale). For a match pairs or case-control study, the response for each test subject and its matched control subject must be in the same case in data file.

To obtain Independent –Sample T Test:

From menus choose: Analyze

Compare Means

Paired-Sample T Test…

2.4One – way analysis of variance analysis :ANOVA

The One-way ANOVA procedure produces a one –way analysis of variance for a quantitative dependent variable by a single factor (independent) var. Analysis of variance is used to test the hypothesis that several means are equal. This technique is an extension of two sample t test.

Data: Factor variables values should be integers, and the dependent variable should be quantitative.

To obtain a One-Way Analysis of Variance:

From menus choose: Analyze

Compare Means

One-Way ANOVA…

Select one or moredependent variables.

Select a single independent factor variable.

2.5 Simple Regression Linear Regression

Linear regressionestimates the coefficients of the linear equation, involving one or more independent variables, the best predict the value of the dependent variable. For example, you can try to predict a sales person’s total yearly sales (the dependent variable) from in dependent variables such as age,education, and year of experience.

Data: The dependent and independent variables

should be quantitative.

To obtain a Linear Regression Analysis :

From menus choose: Analyze Regression Lenear…

Download Presentation

Connecting to Server..