types of data - university of phoenix
TRANSCRIPT
Types of Quantitative DataDR. MIKE MARRAPODI
Topics• Data Types
• Usefulness of Data Types
• Quantitative Tests
Data Types
Categorical
Objects in the study are grouped into categories. The categories are based on a qualitative trait. The data that is produced are labels or categories.
Measurement
The objects in the study are being measured on some quantitative trait. The data that is produced are numerical.
Example
Gender (male, female)
Marital status (married, single, divorced, widowed, never married)
Example
SAT scores
Age, height, weight
Categorical
Nominal
A type of categorical data where the objects fall into unordered categories.
Ordinal
A type of categorical data where the order of the objects is important.
Example
Eye color (blue, brown, hazel)
Voter registration (registered, not registered)
Example
Class (freshman, sophomore, junior, senior)
Opinion (agree, neutral, disagree)
Categorical
Binary
A type of categorical data where the objects fall into only two categories. Binary data can be ordinal or nominal.
Non-binary
A type of categorical data where the objects fall into more than 2 categories. Non-binary data can be ordinal or nominal.
Example
Attendance (present, absent)
Voter registration (registered, not registered)
Example
Political affiliation (Democrat, Republican, Independent)
Opinion (agree, neutral, disagree)
Measurement
Discrete
A type of measurement data where only certain values are possible and there are gaps between the values.
Continuous
A type of measurement data where the values are unlimited; i.e., there is no gap between the values.
Example
ACT scores
Number of students in advanced algebra
Example
Height, age
Cholesterol level
Levels of Measurement
Nominal
Ordinal
Interval
Ratio
Nominal
• From the Latin, nomen, or name• No order• Names or labels only for various categories
Description
Gender (male, female)Political affiliation (Democrat, Republican, Independent)Eye color (blue, green, brown)
Example
Nominal
• Demographic information
Collection
Representation
Ordinal
• Data has an observable order• Interval between measurements is not meaningful
Description
Class (freshman, sophomore, junior, senior)Anxiety (none, mild, moderate, severe)
Example
Ordinal
• Demographic information
Collection
Representation
Interval
• Data has an observable order• Interval between measurements is meaningful• No true zero• Difficult to identify since very few variables have no true zero
Description
IQTemperature (Fahrenheit, Celsius)
Example
Interval
• Testing
Collection
Representation
Weekly Temperature
Ratio
• Data has an observable order• Interval between measurements is meaningful• Data has a true zero
Description
IncomeDistanceTemperature (Kelvin)
Example
Ratio
• Demographic information
Collection
Representation
Usefulness of Data Types
Nominal
Ordinal
Can be used for simple counts
Can be used for rank order data
Usefulness of Data Types
Interval
Ratio
Can add or subtract, but cannot multiply or divide
Can add or subtract, multiply, or divide
Statistical Tests
Nominal
1. Mode identifies the category that occurs most frequently2. Index of Qualitative Variation is a measure of variability3. Crosstab is used to compare data when no statistical test can be performed4. Chi square is used to determine if a relationship between 2 categorical
variables in a sample is likely to reflect a real association between these 2 variables in the population
Statistical Tests
1. Mann-Whitney Test evaluates the difference between two treatments using data from two separate samples.
2. Wilcoxon test evaluates the difference between two treatment conditions using data from a repeated-measures design; that is, the same sample is tested/measured in both treatment conditions.
3. Kruskal-Wallis test evaluates the differences between three or more treatments (or populations) using a separate sample for each treatment condition.
4. The Friedman test evaluates the differences between three or more treatments for studies using the same group of participants in all treatments (a repeated-measures study).
5. Spearman’s Rho or Kendall’s tau can be used for interval data
Ordinal
Statistical Tests
Interval-Ratio
1. t test (one sample, independent, paired sample) can be used with 2 variables2. ANOVA can be used with 3 or more variables3. Pearson’s r can be used to test for correlations4. Regression can be used when seeking predictions about variables
Questions?
References• Antonius, R. (2003). Interpreting quantitative data with SPSS. Thousand Oaks, CA: SAGE.
• Balnaves, M., & Caputi, P. (2001). Introduction to quantitative research methods. Thousand Oaks, CA: SAGE.
• Carcelo, S. (2016). Evaluating a student leadership program’s impact on elementary students’ behavior and academic
achievement (Doctoral dissertation). Retrieved from ProQuest Dissertations Publishing. (10156615)
• Cohen, L. (1992). Power primer. Psychological Bulletin, 112(1), 155-159.
• Cohen, B. H., & Lea, R. B. (2003) Essentials of statistics for the social and behavioral sciences. Hoboken, NJ: John Wiley &
Sons, Inc.
• Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: SAGE.
• Kaplan, D. (2004). The SAGE handbook of quantitative methodology for the social sciences. Thousand Oaks, CA: SAGE.
• Muijs, D. (2011). Doing quantitative research in education with SPSS. Thousand Oaks, CA: SAGE.
• Osborne, J. W. (2008). Best practices in quantitative methods. Thousand Oaks, CA: SAGE.
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• Vogt, W. P. (2011). SAGE quantitative research methods. Thousand Oaks, CA: SAGE.
