1. SOWK 6003 Social Work Research Week 10 Quantitative Data Analysis
By Dr. Paul Wong

2. Overview Introduction Levels of Measurement
Coding, Data Entry, and Cleaning
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Descriptive Statistics and Qualitative Research

3. Introduction Quantitative Analysis
Techniques used to convert data to a numerical form
Quantifying data is necessary when statistical analyses are desired

4. Levels of Measurement
An attribute (e.g., male, female) is a characteristic or quality of a variable, and a variable (e.g., sex) are logical sets of attributes
Different types of attributes (e.g., sex, age, social class) represent different levels of measurement: nominal, ordinal, interval, and ratio

5. Levels of Measurement
A given variable can sometimes be measured at different levels of measurement (e.g., age)

6. Nominal Measures
Include those variables with only discrete, nonmetric or categorical attributes
In other words, they include variables whose attributes are different from one another (e.g., sex, ethnicity)
Code numbers are assigned to the different attributes or categories of a variable (e.g., “0” = male, “1” = female), but the code numbers have no quantitative meaning

7. Ordinal Measures
Include those variables whose attributes may be rank-ordered
E.g., prejudice as composed of very prejudiced, somewhat prejudiced, and not at all prejudiced
Code numbers are assigned to the categories, but the precise differences or distance between the categories is unknown - we only know the order of the categories (e.g., high to low, more to less)

8. Interval and Ratio Measures
Interval measures include those variables whose attributes are not only rank-ordered but also separated by a uniform distance between them (e.g., IQ)
Ratio measures are the same as interval measures except ratio measures are based on a true zero point (e.g., age)

9. Coding, Code Categories, and Codebooks
The goal is the conversion of data items into numerical codes, necessary for statistical analyses
Often occurs after the data have been collected using computer programs, such as SPSS
Coding approaches vary and should be appropriate to the theoretical concepts under investigation
Coding approaches for the same concept may vary depending on the study. For example, the coding of occupational status may be either white and blue collar jobs or by occupation, such as self-employed versus not self-employed.

10. Coding, Code Categories, and Codebooks
Two basic approaches to coding:
Well-developed coding scheme or categories based on research purpose (e.g., items with pre-determined categories), or
Codes generated from data as discussed in Chapter 19 (e.g., open-ended items without response categories)

11. Coding, Code Categories, and Codebooks
Codebooks describe the locations of variables and list their attributes and assigned codes
Codebooks have two primary functions
Primary guide during the coding process
Guide for locating variables and interpreting codes during data analysis

12. Data Entry
Data entry may be approached in a variety of ways, depending on the original form of your data
Data can be entered
Directly into computer program, such as SPSS or Excel
Using optical scan sheets
Using computer-assisted telephone interviewing (CATI) or online surveys

13. Data Cleaning
After entering the data, the next step is to eliminate error – that is, “clean” the data
Possible-code cleaning involves the process of checking to see that only those codes assigned particular attributes appear in the data files
Some computer programs can check for data errors
Some computer technologies are available to test for illegitimate codes that were not checked during data entry.

14. Univariate Analysis Analysis of a single variable
The original data collected with regard to a single variable are usually difficult, if not impossible, to interpret
Data reduction involves summarizing the original data to make them more manageable
An example of univariate analysis would be if gender was measured, we would look at how many subjects were men and how many were women

15. Univariate Analysis
Several techniques are available to make original data more manageable:
Frequency distributions
Measures of central tendency:
Mean: arithmetic mean, or “center or gravity”
Median: middle attribute in the ranked distribution of attributes
Mode: most frequent attribute
Frequency distributions show the number of cases that have each attribute of a given variable.

16. Univariate Analysis
Several techniques are available to make original data more manageable:
Measures of dispersion provide a summary of the distribution of cases around some central value
Range, the distance between the highest and lowest value, is the simplest dispersion measure
Standard deviation is the most common and is used to get an idea of far away from the mean the values in our data are falling

17. Univariate Analysis
Measures of central tendency and dispersion should be used for interval or ratio level variables and may not be appropriate for all variables – e.g., discrete variables
However, technical violations are common and may be useful, and caution should be used to avoid misrepresenting something that is not truly precise

18. An Example of a Univariate Table

19. Bivariate Analysis
Examines relationships between two variables, typically for explanatory purposes
Divides cases into subgroups according to their attributes on some independent variable
Describes each subgroup in terms of some dependent variable
Compares the dependent variable descriptions of the subgroups
Interprets observed differences as statistical associations between the independent and dependent variables

20. Constructing and Reading Bivariate Tables
Often referred to as contingency tables
Provide clear, succinct table heading
Present original content of variables, if possible, or in the text with a paraphrase in the table
Clearly indicate the attributes of each variable
Indicate the base numbers from which any percentages were computed
Indicate number of cases omitted from table due to missing data

21. Constructing and Reading Bivariate Tables
Bottom Line for Constructing Tables
Readers should be able to tell what each variable in the table is, and
Be able to interpret the overall meaning of this table without having to read the narrative text of the report
Rule of Thumb for Reading Tables
If table is “percentaged down” then “read across” in making the subgroup comparisons, or
If table is “percentaged across” then “read down” in making subgroup comparisons

22. An Example of a Bivariate Table

23. Multivariate Analysis
A more complex method that involves analyzing the relationships among several variables
E.g., examining the relationship between an independent and dependent variable while controlling for extraneous variables (recall extraneous, moderating, and control variables in Chapter 7)

24. An Example of a Multivariate Table

25. Descriptive Statistics and Qualitative Research
The use of descriptive statistics, which are used to describe characteristics of the sample, often can enrich a qualitative study
It is not uncommon to find quantitative data included in reports of qualitative research – oftentimes counting phenomena is part of detecting patterns in qualitative research