Measurement  Data Collection

Measurement Data Collection PowerPoint PPT Presentation


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Types of Variables. Dependent variables ? those variables we expect to change as the result of an experimental manipulationIndependent variables ? those variables we control ? we determine the values for these variables and them manipulate them during the experimentTreatment ? the actual experimen

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Measurement Data Collection

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1. Measurement & Data Collection

2. Types of Variables Dependent variables – those variables we expect to change as the result of an experimental manipulation Independent variables – those variables we control – we determine the values for these variables and them manipulate them during the experiment Treatment – the actual experimental manipulation

3. Measurement The assignment of symbols to events according to a set of rules. Four scales of measurement for measuring dependent variables. 1. Nominal scale - the simplest; categories with names (e.g., for drinking vessels: mugs, goblets, cups, juice glasses, etc.) 2. Ordinal scale - when the terms in question can be rank ordered (intervals between ranks may not be ordered) (e.g., 1st, 2nd, 3rd place at a track meet - no information about how far apart the winners were) 3. Interval scale - when the terms in question can be rank ordered and equal intervals separate them, but there is no zero point (e.g., temperatures in °C, when at 0 temperature doesn't cease to exist but interval of 1°C is constant) 4. Ratio scale - goes one step further; it assumes the presence of a zero point (e.g., measurement of sound in decibels, zero means no sound and intervals are strict) Nominal scales are most useful for rough assignments for example groups of individuals made according to species or females as "group 1" and males as "group 2." Another example of an ordinal scale is your year in college or class rank from high school. The pitfall of such a system of scale is that it may be so subjective that there is overlap or confusion. For example, tall and short should be avoided (how tall is tall?). Another example of interval scale is an ACT test score -- a zero does not mean that someone knows nothing. Other examples of ratio scales are age, weight, time, etc.Nominal scales are most useful for rough assignments for example groups of individuals made according to species or females as "group 1" and males as "group 2." Another example of an ordinal scale is your year in college or class rank from high school. The pitfall of such a system of scale is that it may be so subjective that there is overlap or confusion. For example, tall and short should be avoided (how tall is tall?). Another example of interval scale is an ACT test score -- a zero does not mean that someone knows nothing. Other examples of ratio scales are age, weight, time, etc.

4. Nominal and ordinal scales are for discrete data – data in which each item is a separate, whole unit – number of individuals or species, members of particular groups (species, type of car, round or square, heavy or light, etc.) Interval and ratio scales are for continuous data – data for points along a scale that, at least theoretically, could be subdivided – temperature, weight, lengths, units of time, etc.

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