.STRENGTHS i.Quantitative: objective, reliable, models ignore flukes and outliers. Can be used to predict and give a specific description, with appropriate units, of a phenomena. ii.Qualitative: descriptive, gives significance, more in depth, opens doors to further areas of study. Can be used to gain insight and understanding of a phenomena or process.
LIMITATIONS
Quantitative: statistical analysis problems. Ie. Sample size. Hasty generalization from a limited sample size. Danger of not taking outliers into consideration when they may be important.
Qualitative: Perspective is subjective depending on the person, so it’s hard to make certain observations. Cannot do any statistical analysis on them (which people rely on so much)
a. Human Sciences: majority of people in experiments chosen to represent a phenomena (Bandura) but qualitative data is essential (Autobiography stories, Bandura, Phineas Gage case study) because behavior varies wildly
b. Natural Sciences: mathematical models, equations, etc investigated to conclude, but qualitative data needed in some branches more than quantitative (Biology: describing processes, characteristics of species etc.).
c. Example: Freud : absence of quantitative data = inconclusive, no predictions. BUT qualitative data gave valuable insight to the unconscious mind
d. = cannot ignore the value of qualitative data: numbers would be nothing without the descriptions that surround them
i. Quantitative: objective, reliable, models ignore flukes and outliers. Can be used to predict and give a specific description, with appropriate units, of a phenomena.
ii. Qualitative: descriptive, gives significance, more in depth, opens doors to further areas of study. Can be used to gain insight and understanding of a phenomena or process.
LIMITATIONS
Quantitative: statistical analysis problems. Ie. Sample size. Hasty generalization from a limited sample size. Danger of not taking outliers into consideration when they may be important.
Qualitative: Perspective is subjective depending on the person, so it’s hard to make certain observations. Cannot do any statistical analysis on them (which people rely on so much)
a. Human Sciences: majority of people in experiments chosen to represent a phenomena (Bandura) but qualitative data is essential (Autobiography stories, Bandura, Phineas Gage case study) because behavior varies wildly
b. Natural Sciences: mathematical models, equations, etc investigated to conclude, but qualitative data needed in some branches more than quantitative (Biology: describing processes, characteristics of species etc.).
c. Example: Freud : absence of quantitative data = inconclusive, no predictions. BUT qualitative data gave valuable insight to the unconscious mind
d. = cannot ignore the value of qualitative data: numbers would be nothing without the descriptions that surround them