analysis using spss

Manipulation and Transformation of Data Recode Compute Replace missing values Select cases Sort cases Merge files Aggregate data

Methods for transforming data

Computing a new variable Recode

into same variabledifferent variable

Select subset of cases Random sample Replace missing values

Compute a new variable

You can calculate different variables from the existing variables.

For this you need to know the way to compute your target variable from the existing variables.

You can perform operations like addition, subtraction, division and multiplication of variables to create a new variable.

Recode into same variable

Using SPSS you can recode a variable into the same variable?

Recode into different variable

You can Recode existing variable into a different variable.

Recode into Different Variables reassigns the values of existing variables or collapses ranges of existing values into new values for a new variable.

For example, you could collapse salaries into a new variable containing salary-range categories.

Select subset of cases

You can select subset of cases for your analysis using SPSS.

For example, you can use select procedure if you want to do analysis of the relation between education of females and their income from the data set that has information of both males and females.

Replace missing values

Missing observations can be problematic in analysis, and some time series measures cannot be computed if there are missing values in the series.

Replace Missing Values creates new time series variables from existing ones, replacing missing values with estimates computed with one of several methods.

Aggregate data

Aggregate Data combines groups of cases into single summary cases and creates a new aggregated data file.

Cases are aggregated based on the value of one or more grouping variables.

The new data file contains one case for each group.

Create time series

Create Time Series creates new variables based on functions of existing numeric time series variables.

These transformed values are useful in many time series analysis procedures.

Available functions for creating time series variables include differences, moving averages.

Sort cases

You can sort cases of the data file based on the values of one or more sorting variables.

You can sort cases in ascending or descending order.

If you select multiple sort variables, cases are sorted by each variable within categories of the prior variable on the Sort list.

Merge files

There are two types of merging:Adding new cases for the same

variables.Adding new variables for the same

cases. Depending on what you want to add you

select this option.

Add cases Add Cases merges the working data file with a

second data file that contains the same variables but different cases.

For example, you might record the same information for customers in two different sales regions and maintain the data for each region in separate files.

Variables from the working data file are identified with an asterisk (*). Variables from the external data file are identified with a plus sign (+).

Add variables

Add Variables merges the working data file with an external data file that contains the same cases but different variables.

For example, you might want to merge a data file that contains pre-test results with one that contains post-test results.

You can save this new file with a new name after merging.

Before merging… Cases must be sorted in the same order in both

data files. If one or more key variables are used to match

cases, the two data files must be sorted by ascending order of the key variable(s).

Variable names in the second data file that duplicate variable names in the working data file are excluded by default because Add Variables assumes that these variables contain duplicate information.

Data Analysis

Types of Variables Nominal

example: nationality, race, gender… based on a concept (two categories variable called

“dichotomous nominal”)

Ordinalexample: knowledge, skill... (more than, equal, less than)rank-ordered in terms of a criterion from highest to lowest

Interval/Ratio example: age, income, speed... based on arithmetic qualities and have a fixed zero point

Types of AnalysisUnivariate Analysis

Descriptive Statistics (Summarising Data)

Frequency Distributions Frequency tables Histograms



Central Tendency The mean The median The mode



Central Tendency The mean the arithmetic average

= ( X) / N

identifies the balance point in a distribution of scores.



Variance spread of data around the mean

○ The range○ Standard deviation


The Range

The range is the difference between the highest and lowest scores.

= Range = Highest Score - Lowest Score


Standard Deviation The standard deviation is the average amount of deviation from the mean within a group of scores. The greater the spread of scores, the greater the standard deviation.

Types of Analysis

Skewness

Skewness refers to the degree and direction of asymmetry in a distribution.

No Skew

Positively Skewed Negatively Skewed

Types of AnalysisBivariate Analysis

Exploring differences relationships

between two variables


Exploring differences between two variables Criteria for selecting bivariate tests of differences

○ Type of data (nominal/ordinal/interval)○ Purpose of investigation (means/varience)○ Relationship between groups (independent/dependent)○ Number of groups (two/more)


Exploring differences between two variables Parametric vs non-parametric tests

○ The scale of measurment is of equal interval.○ The distribution is normal.○ The variences of both variables are homogenous.


Exploring differences between two variables

1. Non-parametric tests

Categorical variables

Non-categorical variables

2. Parametric tests

Non-categorical variables



Non-parametric tests - Categorical variables

- Binomial test: to compare frequencies, two categories, one sample

Example: Ratio of male to female in specific industry compared to industry in general.

- Chi-square test: to compare frequencies, more than two categories, one sample

Example: Number of workers from four different ethnic groups



Non-parametric tests - Categorical variables

- Crosstabulation: two or more categories, unrelated samples

Example: The proportion of male to female workers in both white and black workers.

- Q test: three or more categories, related samples

Example: The number of people who didn’t attend the three meetings.



Non-parametric tests - Non-categorical variables

- Kolmogorov-Smirnov test: one sample & two unrelated samples

- Median test: two or more unrelated samples

- Mann-Whitney U test: two unrelated samples

- Kruskal-Wallis H test: three or more unrelated samples

- Wilcoxon test: two related samples

- Friedman test: three or more related samples



Non-parametric tests - Non-categorical variables

- Mann-Whitney U test: two unrelated samples

Example: Rated quality of work for men and women.

- Wilcoxon test: two related samples

Example: Rated quality of work is the same in the first and second month.

Types of Analysis

Bivariate Analysis Exploring differences between two variables

Parametric tests - Non-categorical variables

- t test: one sample

Example: The mean of a sample to that of the population

- t test: two unrelated samples

Example: Job satisfaction between men and women

- One-way ANOVA (analysis of variance): three or more unrelated means

Example: Job satisfaction of four ethnic groups



Parametric tests - Non-categorical variables

- Levene’s test: three or more unrelated variances

Example: The variances of job satisfaction across four ethnic groups

- t test: two related means

Example: Means of the same subject s in two conditions


Exploring relationships between two variables: CrosstabulationTo demonstrate the presence or absence of a relationship (nominal and ordinal variables)

Count

5 9 14

4 2 6

9 11 20

no

yes

Absenteesim

Total

no yes

Satisfaction

Total


Exploring relationships between two variables: Correlation To show the strength and the direction of a relationship (ordinal and interval variables)

1. Rank correlation (ordinal variables)2. Linear correlation (interval variables)


Exploring relationships between two variables Rank correlation

○ for ordinal variables and non-parametric samplesSpearman’s rhoKendall’s tau


Exploring relationships between two variables Linear correlation

○ for interval variables and parametric samples Pearson’s r Regression (for making predications of likely values of the dependent variable)

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