Page 27 - Quantitative Data Analysis

Quantitative Data Analysis
Simply Explained Using SPSS

1. Descriptive Statistics

Descriptive statistics deals with the organization, tabulation,
summarization, and presentation of data.

Examples: Charts, graphs, summarizing with numbers, frequency
tables.]

Descriptive statistics allows us to describe particular characteristics
of a set of data. It can be further divided into two categories:

1. Central Tendency (how similar data are)
1. Mean = average score
2. Mode = category with highest frequency
3. Median = middle category or score

4. Dispersion (how dissimilar data are)
1. Range = difference between highest and
lowest scores
2. Standard deviation = Measure of variability;
involves deviations of scores from mean
3. Variance = square of standard deviation
4. Percentile and quartile

2. Inferential Statistics

Inferential Statistics allows us to generalize from our sample of data
to a larger group of subjects and to infer our findings to the general
population. Inferential statistics provides procedures to test the
research hypothesis. For example, all significance tests such as t-
test, ANOVA, regression analysis and multivariate analysis are
inferential statistics. Inferential Statistics is used to determine the
probability that a conclusion based on analysis is true. Later chapter
will discuss inferential statistics in details.

The Theory and Applications of Statistical Inferences 11