Variables and Scale of Measurements

Types of Variables ◦ Variables are measurements or observations that are typically numeric.

1. Independent VS Dependent variable 2. Quantitative VS Qualitative 3. Continuous VS Discrete

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Independent VS Dependent ◦ Independent variable (IV) ◦ Variable with two or more levels that are expected to have different effects on another variable. (affect/ change other outcome variable) ◦ Sometimes called as predictor or experimental variable.

◦ Dependent variable (DV) ◦ The outcome variable that is used to compare the effects of the different independent variable (IV) levels.

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Independent VS Dependent In an experiment to study the effects of a new treatment on reducing depressive symptoms, researchers gave the new treatment to a sample of people with depression and withhold the treatment from another sample of people with depression. (i.e. new treatment vs no treatment).

After both samples were given their respective treatment levels, the amount of depression in each sample was compared by counting the number of depression.

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Independent VS Dependent ◦ Independent variable: 1) The new treatment 2) No treatment ◦ Dependent variable: 1) The number of depressive symptoms observed in each person

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Quantitative VS Qualitative ◦ Quantitative data is information about quantities; that is, information that can be measured and written down with numbers. ◦ Varies by amount ◦ Examples : height, shoe size, and the length of your fingernails.

◦ Qualitative data is information about qualities; information that cannot be easily measured, but can be observed subjectively. ◦ Varies by class ◦ Examples : the smells, tastes, textures, attractiveness, color.

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Continuous VS Discrete ◦ A continuous variable is measured along a continuum. ◦ measured in whole units or fractional units. ◦ e.g.: If we measure a height of 35cm and 36cm, an infinite number of heights is possible in the range of 35 and 36.

◦ A discrete variable is not measured along a continuum. ◦ variable is measured in whole units or categories. ◦ e.g.: The number of your siblings and your family’s socioeconomic class (working class, middle class, upper class)

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Quantitative Types of Data ◦ Measured in numeric units, so both continuous and discrete unit can be quantitative. ◦ E.g.: we can measure food intake in calories (a continuous variable) or we can count the number of pieces of food consumed (a discrete variable)

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Qualitative Types of Data ◦ Only discrete variables can fall into this category. ◦ E.g.: socioeconomic class (working class, middle class, upper class) is discrete and qualitative.

Mental disorders/depression (unipolar/bipolar) or drug use (none, experimental, abusive)

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Categories of Variables Variables

Quantitative

Discrete

Continuous

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Qualitative

Discrete

Scales of Measurement ◦ Important to determine the kind of statistical procedures that can be used on that variable. ◦ Four different scales of measurement: 1) 2) 3) 4)

Nominal Ordinal Interval Ratio

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QUALITATIVE & DISCRETE

Nominal

◦ Cannot be measured, because of their qualitative nature and also known as categorical data. ◦ Do not denote quantity. ◦ Categorize individuals into groups that are qualitatively different from other groups. ◦ Not in order. ◦ Usually this categorical variables have been coded (converted into numeric) ◦ E.g. person’s race (malay [1], chinese [2] & etc.), gender (female [1] & male[2]), marital status (single[1], married[2]) and etc.

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Nominal cont. ◦ Sometimes, data that might have been expressed on different scale of measurements (ordinal, interval, ratio) may be recorded in nominal categories. ◦ E.g. ◦ Marks of between 50.0 and 100.0 = PASS ◦ Marks of between 0.0 and 49.0 = FAIL

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Ordinal

QUALITATIVE & DISCRETE

◦ An ordinal scale of measurement is one that conveys order alone (no indication of how much more). ◦ Only indicates that some value is greater or less than another value, so differences between ranks do not have meaning. ◦ E.g.: ◦ level of education (PhD, MSc, Bachelor’s) ◦ level of satisfaction (Very Unsatisfied, Unsatisfied, Neutral, Satisfied, Very Satisfied) ◦ degree of illnesses (mild, moderate, severe).

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Interval

QUANTITATIVE & CONTINUOUS

◦ Interval scales are measurements where the values have no true zero and the distance between each value is equidistant. ◦ Equidistant – those values whose intervals are distributed in equal units. ◦ True zero – values where the value 0 truly indicates nothing. ◦ e.g. temperature in Celcius or Fahrenheit. Difference between 5°F and 3°F is similar to 8°F and 6°F (equidistant) but 0°F is not the absence of heat.

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Ratio

QUANTITATIVE & CONTINUOUS

◦ Similar to interval scales in that scores are distributed in equal units (equidistant). ◦ Unlike interval scales, a distribution scores on a ratio scale has a true zero (the absence of quantity being measured). ◦ Example: salary amount, duration of drug abuse (in years), score (from 0 to 100%) on an exam, weight (in pounds) of an infant.

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Examples (Scales of Measurement)

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Examples (Types of Data)

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