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• VARIABLE - is any factor or property that a researcher measures, controls, and/or manipulates in a research study.
• Ordinal Variables - have values that can be ranked or ordered • Ex: Size, Educational Attainment, Satisfaction level, Agreement Level, Socio economic status, Military rank.
• Nominal Variables - have more two categories or values • Ex: Blood Type, Citizenship, Hair color, Marital Status, Gender, Race, Mode of Transportation, Religion
• Discrete Variables - can be counted • denoted by positive whole numbers not in range • Ex: Number of children in the family; Total number of Faculty members; Population of students
• Continuous Variables - measured in ranges • can be denoted by nonwhole number • positive and negative values • Ex: Temperature; Time; Height; Weight; Age; IQ test score
• Special Types of Continuous Variables 1. Ratio Variables - do not have a negative value • Ex: Age, Height, Weight, Distance, and Test scores 2. Interval Variables - difference between points is standardized and meaningful. • Ex: Temperature (in Celsius or Fahrenheit), IQ test score.
• Independent Variables - variable that is considered to affect the dependent variable • manipulated by the researcher.
• Dependent Variables - variables that bear or manifest the effects caused by the independent variables.
• Bar Graph (Histogram) - Shows comparisons of amounts and quantities.
• Pie Graph (Scatter Diagram) - Shows the relationship of parts to a whole, usually in percentage and proportions.
• Line Graph (Frequency Polygon) - Shows trends and changes in the data.
• Title, caption, labels, and legend of the graph.
• Bar graph- to show comparisons of variables and show frequencies of data in a vertical or horizontal orientation.
• Pie graph- is used to show distribution or how parts are allocated versus the totality.
• Line graph- utilized to present trends, increased and decreased on the quantity of variables.
• Consider the dates or time intervals generated between data and relate to the values being compared and analyzed.
• Take note of noticeably high or low values and any unusual pattern in the data.