Managing and Analyzing Quantitative Data
I. Data Management.
A. Creating a new data set.
1. Sources of data.
a. Questionnaires and tally sheets from experiments.
b. Questionnaires and interview schedules from surveys.
c. Data from statistical reports and documents in studies using existing data.
d. Tally sheets from content analysis.
2. Entering data into a data set using a computer keyboard.
a. Data entry software.
i. Text editors and word processors.
ii. Spreadsheet and data base programs.
iii. Statistical software.
iv. Character sets and data formats.
b. Coding and data entry.
ii. Direct entry.
iii. Edge coding.
iv. Code sheets.
v. Documenting codes.
3. Entering data into a data set using scan sheets.
a. Scan sheets and scan sheet readers.
b. The pros and cons of scan sheets.
i. Ease of data entry.
ii. Problems with separate answer sheets.
iii. Availability of scan sheet readers.
4. Checking and cleaning the data.
a. Looking for out-of-bound and improbable values.
b. Consistency checks.
c. Correcting problems.
5. Saving the data set.
a. Storage media.
b. Files and file names.
c. Making backup copies.
6. Documenting the data set.
a. Where and how are the data stored?
b. What character set and data format is used?
c. What variables are in the data set and where is the data for each located?
d. What codes are used for the categorical variables in the data set and what is their meaning?
e. The codebook can be, and often is developed before data are entered.
B. Using existing data sets.
1. Reading the data
a. Storage media, character sets, and data formats.
b. Using database, spreadsheet, and statistical software.
c. Using provided documentation to define the data set.
2. Checking and cleaning the data.
3. Backing up the data set.
C. Manipulating data.
1. Recoding existing variables.
2. Computing new variables.
II. Analyzing Data.
A. Statistical software.
3. Other statistical software.
4. Spreadsheet and database software.
B. Descriptive and inferential statistics.
1. Descriptive statistics.
2. Inferential statistics.
i. Point estimates.
ii. Confidence intervals.
b. Hypothesis testing.
i. Null hypotheses.
ii. Test statistics and probabilities.
iii. Making a decision about the null hypothesis.
iv. Drawing conclusions.
C. Univariate statistics.
2. Measures of central tendency.
3. Measures of dispersion.
4. Graphical representations.
D. Bivariate statistics.
1. Comparing group means.
b. Analysis of variance (ANOVA).
2. Measures of association.
b. Nominal measures of association.
c. Ordinal measures of association.
d. Correlation and linear regression.
E. Multivariate statistics.
1. Multiple analysis of variance and analysis of covariance.
2. Multiple regression.
3. Logistic regression.
4. Discriminate and cluster analysis.
F. Evaluating the validity and reliability of indices and scales.
1. Factor analysis.
2. Reliability measures.
3. Assessing the structure of scales.