04-09-2012, 04:27 PM
An Overview of Data Analysis
Data Analysis.ppt (Size: 821.5 KB / Downloads: 39)
Key Considerations in Data Analysis
Identify the purpose of the analysis or project
Understand the sample(s) under study
Understand the instruments being used to collect data
Be cognizant of data layouts and formats
Establish a unique identifier if matching or merging is necessary
Plan your work and work your plan!
Components of a Data Analysis Plan
Statement of research questions
Methods used to answer research questions
Timeline
Budget
File restructuring procedures (syntax creation, adding new variables as needed)
Algorithms for scoring, equating, etc.
Data cleaning procedures (e.g. removing outliers)
Quality control procedures at every step in the project
Examples of Analyses
Frequency Distributions and Cross -Tabulations
Descriptive Statistics (Means, Std. Deviations, Correlations)
T-tests and Analysis of Variance (ANOVA)
Regression
Principal Components/Factor Analysis (Data Reduction)
Cluster and Discriminant Analyses (Segmentation)
Latent Class Analysis (Classification)
Hierarchical Linear Modeling (HLM)
Differential Item Functioning (DIF)
ASCII Text Files
Usually rectangular in structure
One record per observation
Each data variable in same position on each record
Each record may have multiple instances of data
Arrays
Repeating blocks (sets of variables)
File may have multiple records per observation
Number of records per observation can be variable
Most government data files come in this format at a minimum
Every software package can handle this file type
What is Excel?
Data are organized by worksheets, rows and columns
Worksheet limits are 256 columns and 65,536 total cells
Cells contain data or formulas with relative or absolute references to other cells
Direct manipulation of data and flexibility to move data “around” (e.g. sorting, replacing, merging)
Opens many file types
Quite useful in prepping files for use in SPSS, SAS or other programs
Conditional formatting
Also features macro capabilities, replicating user actions, allowing simple automation of regular tasks
What is SAS?
A general purpose statistical package with a basic programming capability utilizing scores of statistical and mathematical functions in numerous “modules”
Can readily access data from a wide variety of sources, perform data management, and present findings in a variety of report and graph formats
Provides powerful tools for both specialized and enterprise-wide analytical needs
SAS - Weaknesses
Steep learning curve; volume of functions, options and documentation can be overwhelming for the novice
Inconsistent syntax across different procedures or modules
Not a good choice for applications that interact with external systems such as hardware devices or software programs because of its inconvenient interface
Difficult interaction with other programming languages
Expensive