The distinction between measuring objects, properties, and indicants of properties.

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The distinction between measuring objects, properties, and indicants of properties.

Measuring objects entail the ordinary experience concepts for instance tangible goods like furniture, laundry, people, detergent and even automobiles. Measuring objects also entail things like concrete for instance genes, attitudes, and peer-group pressures. Measuring Properties and the object’s characteristics. For instance when describing a person’s physical characteristics, one may state his or her weight, height and posture. Social property of a person include leadership, status while psychological properties deal with attitudes and intelligence. Measuring indicates are things that indicate something for example rain gauge.

The similarities and differences between the four scale types used in measurement and when each is used

Measuring scale

Measurement Scales

In taking measurements, one defines s mapping rule and then transforms the property of the observed characteristic using this rule.

For every measurement, various measurement types can be carried out and the choice depends on what a researcher assumes about the mapping rule. For every mapping rule, there exists an underlying assumption of how the rule relates to the actual life.

Characteristics of mapping rules (four):

Classification. This is a case where numbers are used to group or sort responses and there is no order.

Order. This is where numbers are ordered. In this case, one number is greater than, less than, or equal to another number.

Distance. This is the difference between two points in space.

Origin. The number series has a unique origin indicated by the number zero. This is an absolute and meaningful zero point.

Taking the four characteristics of measurements into consideration, classification can be grouped into normal, ordinal, interval and ratio.

Nominal Scales

This is a collection of information on a variable that can be categorized into two or more categories which are mutually exclusive and collectively exhaustive.

Ordinal Scales

They include the characteristic of nominal scale but further entails an indication of order. The classification needs a conformity t some logical order for instance suppose a is greater than b and b greater than c, then a is greater than c.

Interval Scales

This combines the strengths of nominal and ordinal plus an extra feature. It entail idea of equality of interval or the scale distance between two distances. An example of interval scale is dates, time and temperature.

Ratio Scales

This entails all the three classification plus an extra feature of origin or zero. It indicates the exact amount of variable.

Example

Nominal Ordinal Interval Ratio

Similarities 1.numbers used to group or sort responses 1. numbers used to group or sort responses

2. Numbers are ordered 1. Numbers are ordered

2. Distance

3. numbers used to group or sort responses 1.Distance

2. numbers used to group or sort responses

3. Numbers are ordered

Differences 1. Mutually exclusive.

2. Collectively exhaustive categories

3. least powerful of the four data types Indication of order. Equal distance between numbers

Natural Origin

Similarities Nominal and ordinal takes unquantifiable measurements while scale and ration takes quantifiable measurements.

Differences Nominal takes measurements of variables without evaluative distinctions while ordinal takes without evaluative connotation. Interval and ratio measures how better one object is another. Interval and ratio is therefore a comparison while normal and ordinal are not.

The four major sources of measurement error.

There are different reason why an error can happen in a measurement. It is not possible to have a perfect analysis and error free research. Among the reasons are:

1. Situation: This depends on the researcher’s opinions and belief. It also depends on the intended purpose and the researcher’s desired outcome. When two options are to be weighed and a researcher supports one side, this leads to biasness and the researcher might record wrong value to have one side win.

Respondent’s error: this occurs when the respondents either give biased or untrue information to the researcher. In some cases respondents falsify information to have a given situation favored or altered and this creates error in analysis or data recording. for example (When it comes to a face to face interview the researcher can encounter  participants that don’t want to give a strong negative or positive opinion)

Measurer’s error or researcher’s error: This happens when someone accidentally take a wrong measurement or accidentally records a wrong value when taking measurements. This is in cases where the researcher or the person recording data or measurements make a mistake and records a wrong value or inaccurately takes the measurement.

Instrument: when an instrument is not valid or inaccurate error occurs during measurements measuring error might occur. for example  The interviewer can create a bad survey by not including more broad questions about the entire subject or company that they are trying to investigate. Even when the general issue or subject are studied, the questions may not cover enough aspects of each area of concern.

The criteria for evaluating good measurement.

Reliability. A good measurement tool should be as reliable as possible. This is the ability of an instrument or tool to produce similar result when a similar research procedures are followed and another test conducted. This answers whether consistent results are produced, assuming no changes are made either in the data to be measured or the procedure used. When the same results are produced, then the tool is reliable. To assess test-retest reliability, the same procedure should be carried out.

Practicality. A good measuring tool should be producing consistent results. There should not fluctuations due to weather or other atmospheric conditions or due to any other condition. Suppose a similar measurement is taken using similar instruction, the same results should be obtained when using the same tool. A tool’s reliability is determined using a correlation coefficient of one type or another.

Validity. A good measuring tool should be valid. A valid tool measures the concept that it is designed to measure. it is essential to take into account that instrument’s validity is only applicable to specific purpose with specified group of people. An instrument cannot be grouped as valid or invalid rather validity or invalidity fully depends on the type of the measurement to be taken. For example, one tool might be useful to a group of people and not useful to another group.

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