Analyze Quantitative Data Easily

See important notes below!

Do not rush to conclusions until all results are in!

Data analysis consists of looking for patterns, themes, associations, and interrelationships among data collected.

  • One must organize and find meaning in the mass of collected data.
  • Data must be looked at holistically
  • Quantitative data are summarized statistically or mathematically

Why do we analyze data?

  1. To describe or summarize data clearly.
  2. To search for patterns or themes among data.
  3. To enable us to answer our research questions and/or hypotheses.

For statistical analyses, the two common types are descriptive and inferential.

See below for types of inferential statistics:

T-Test

  • Used in groups larger than 15 people
  • Determines the difference between 2 means of statistical significance
  • Input raw scores into STATPAK
*Must consult T-Test tale the find out if data is significant or not
  • A high level of significance shows the results can be attributed to the treatment and not chance error.

Sign Test

  • Determines if post test scores are different from pretest scores.
  • Example: Comparing effects of 2 different reading programs in one class.
  • Used on one group designs.
  • Must use a "sign table" to get probability value
  • *Weakness: No control group.

Mann-Whitney U-Test

  • Can show if there is a significant difference between post test scores of 2 groups
  • Involves control and experimental group
  • Similar to T-Test
  • Uses pretest and post test scores
  • Must consult a "U Table" to receive results

Chi-Square

  • Used for nominal data (gender, ethnicity, socioeconomic status)
  • Example: Relationship between gender and teacher morale?
  • Can be calculated in STATPAK