Scales come in all shapes, sizes and price ranges. From the slightly skewed spring scale hanging above your produce aisle to the pit-and-girder monster that weighs train cars and tractor trailers, they all measure force.
A perfect scale would work like a perfectly stiff spring, bending proportionally with the weight being applied to it. A variety of research designs are used to improve the validity of scales during development.
Likert Scale
Likert scales are commonly used in market research, customer satisfaction surveys and to understand a respondent’s overall satisfaction. They also help in determining the impact of a particular factor on a variable, such as customer satisfaction or a product’s appearance or price. The questions are not binary and allow the respondent to choose from a range of options. For example, they can answer ‘extremely satisfied’ to ‘dissatisfied’, which helps companies get more actionable data and better insights.
It is important to decide how many response options you want the question to have. Choosing too few can remove the neutral option and cause respondents to not be able to answer the question honestly. Also, it is important to ensure that the scales are consistent and not polarized. This will reduce the risk of social desirability bias.
Scale of Attributes
A scale of attributes is a tool researchers use to measure the abstract intrinsic qualities of an individual. It is used to quantify responses that are not directly measurable and it is a crucial component of research design. There are many different types of psychological attribute scales with wildely varying measurement properties.
The interval scale is a type of attribute scale that has both an order and an equal interval. For example, a temperature scale in Celsius and Fahrenheit is an interval scale because the difference between 30 and 40 degrees Fahrenheit is the same as that between 80 and 90 degrees Fahrenheit. Interval scales also have a zero point, unlike nominal and ordinal scales.
An interval scale can be analysed using the arithmetic mean, median, mode and range. It can also be used to calculate a statistical variance and standard deviation. However, it does not have a built-in correlation between variables and cannot be used to identify cause and effect relationships.
Scale of Bipolar Labels
A bipolar matrix scale can be a powerful tool for measuring attitudes and perceptions in surveys. Its multidimensional design allows respondents to evaluate their attitudes and perceptions on multiple dimensions at once, which reduces the likelihood of response bias. However, there are several challenges with interpreting results from this question type.
One challenge is that bipolar scales often require more cognitive effort to evaluate than unipolar scales, because they contrast two opposite attributes. This can be especially problematic when using semantic differentials, where the endpoints of a scale are not pure opposites (e.g., boring vs interesting).
Another challenge is that bipolar scales can be difficult to interpret because they do not offer a midpoint or neutral option. As a result, it is important to test question wording with potential respondents before using it in your survey. It is also important to ensure that the polar adjectives you use in a bipolar scale are true opposites, and not just words with similar meanings.
Scale of Scales
Scales of measurement are the different ways in which a researcher can classify a group of variables. They can be ordered, ordinal, interval, or nominal. Each of these scales is characterized by its properties as listed below.
In music, a scale is a collection of tones or intervals dividing an octave. The most common scales are diatonic and chromatic. Each has a distinct pattern of gaps that are augmented or diminished by various pitches. The scales also differ by whether or not they contain a root note.
The nominal scale only contains labels for categorization or division and not for comparison or calculations. For example, a survey question asking which car brand the respondent prefers is of this type. The interval scale contains properties of the nominal and ordered data, and allows comparisons or calculations to be made between different variables. For instance, 40 degrees plus 20 degrees equals 60 degrees. This scale is commonly used in research.