Measurement, Designing in research and types of scale.

 Measurement, Designing and writing items:

Measurement is important in research. Measure aims to as certain the dimension, quantity or capacity of the behaviors or events that researcher want to explore. Researcher can measure certain events in certain range. the range is consisting of scale.

Quantitative measurement

Quantitative measurement is measurement of data that can be put into numbers. The goal of quantitative measurement is to run statistical analysis, so data has to be in numerical form. In Carrie's case, her data is already quantitative; so is data like blood pressure, height, or age.

Qualitative measurements 

Qualitative measurements are ways of gaining a deeper understanding of a topic. Researchers who are looking to find the meanings behind certain phenomenon or are investigating a new topic about which little is known, use qualitative measures.

What is Uni-dimensionality?

“Uni-dimensionality” is used to describe a specific type of measurement scale. A unidimensional measurement scale has only one (“uni”) dimension. In other words, it can be represented by a single number line. Some examples of simple, unidimensional scales:

• Height of people.
• Weight of cars.
• IQ.
• Volume of liquid.

Multidimensional Scaling.

Multidimensional Scaling. Select at least four numeric variables for analysis. In the Distances group, select either Data are distances or Create distances from data. If you select Create distances from data, you can also select a grouping variable for individual matrices.

scale: A scale is a devise or an object used to measure or quantity any event or another object.

Levels of Measurement

• Nominal scales 
• Ordinal scales
• Interval scales
• Ratio scales

Nominal scales , also called categorical scales, measure categorical data. These scales are used for variables or indicators that have mutually exclusive attributes. Examples include gender (two values: male or female), industry type (manufacturing, financial, agriculture, etc.), and religious affiliation (Christian, Muslim, Jew, etc.).

Ordinal scales are those that measure rank-ordered data, such as the ranking of students in a class as first, second, third, and so forth, based on their grade point average or test scores. However, the actual or relative values of attributes or difference in attribute values cannot be assessed. For instance, ranking of students in class says nothing about the actual GPA or test scores of the students, or how they well performed relative to one another.

Interval scales are those where the values measured are not only rank-ordered, but are also equidistant from adjacent attributes. For example, the temperature scale (in Fahrenheit or Celsius), where the difference between 30 and 40 degree Fahrenheit is the same as that between 80 and 90 degree Fahrenheit. 

Ratio scales are those that have all the qualities of nominal, ordinal, and interval scales, and in addition, also have a “true zero” point (where the value zero implies lack or non-availability of the underlying construct). Most measurement in the natural sciences and engineering, such as mass, incline of a plane, and electric charge, employ ratio scales, as are some social science variables such as age, tenure in an organization, and firm size (measured as employee count or gross revenues). For example, a firm of size zero means that it has no employees or revenues. 

Rating Scale Definition

Rating scale is defined as a closed-ended survey question on used to represent respondent feedback in a comparative form for specific particular features/products/services. It is one of the most established question types for online and offline surveys where survey respondents are expected to rate an attribute or feature. Rating scale is a variant of the popular multiple-choice question which is widely used to gather information that provides relative information about a specific topic.

There are four primary types of rating scales which can be suitably used in an online survey:

• Graphic Rating Scale
• Numerical Rating Scale
• Descriptive Rating Scale
• Comparative Rating Scale

Advantages of Rating Scale

• Rating scale questions are easy to understand and implement.
• Offers a comparative analysis of quantitative data within the target sample for researchers to make well-informed decisions.
• Using graphic rating scales, it is easy for researchers to create surveys as they consume the least time to configure.
• Abundant information can be collected and analyzed using a rating scale.
• The analysis of answer received for rating scale questions is quick and less time-consuming.
• Rating scale is often considered to a standard for collecting qualitative and quantitative information for research.

Ranking scale 
A ranking scale is a close-ended scale that allows respondents to evaluate multiple row items in relation to one column item or a question in a ranking survey and then rank the row items. It is the scale used by market researchers to ask ranking questions

The Sample Size Decision

The sample size often depends on the cost involved or the time required by the systems analyst, or even the time available by people in the organization. This subsection gives the systems analyst some guidelines for determining the required sample size under ideal conditions, for example, to determine what percentage of input forms contain errors, or alternatively what proportion of people to interview. The systems analyst needs to follow seven steps, some of which involve subjective judgments, to determine the required sample size

1. Determine the attribute (in this case, the type of errors to look for).
2. Locate the database or reports in which the attribute can be found.
3. Examine the attribute. Estimate p, the proportion of the population having the attribute.
4. Make the subjective decision regarding the acceptable interval estimate, i.
5. Choose the confidence level and look up the confidence coefficient (z value) in a table.
6. Calculate σp, the standard error of the proportion, as follows:
7. Determine the necessary sample size, n, using the following formula:

Secondary data sources :

Sources of secondary data includes books, personal sources, journal, newspaper, website, government record etc. Secondary data are known to be readily available compared to that of primary data. It requires very little research and need for manpower to use these sources.

Sources of secondary data

• information collected through censuses or government departments like housing, social security, electoral statistics, tax records.
• internet searches or libraries.
• GPS, remote sensing.
• km progress reports.

Sampling is the process whereby a researcher chooses her sample. This might seem pretty straightforward: just get some people together, right? But how does Brooke do that? Should she just stand on a corner and start asking people to take her survey? Should she send out an email to every college student in the world? Where does she even begin?

Because sampling isn't as straightforward as it initially seems, there is a set process to help researchers choose a good sample. Let's look closer at the process and importance of sampling.

Process

1. Identify the population of interest. A population is the group of people that you want to make assumptions about. For example, Brooke wants to know how much stress college students experience during finals. Her population is every college student in the world because that's who she's interested in. Of course, there's no way that Brooke can feasibly study every college student in the world, so she moves on to the next step.

2. Specify a sampling frame. A sampling frame is the group of people from which you will draw your sample. For example, Brooke might decide that her sampling frame is every student at the university where she works. Notice that a sampling frame is not as large as the population, but it's still a pretty big group of people. Brooke still won't be able to study every single student at her university, but that's a good place from which to draw her sample.

3. Specify a sampling method. There are basically two ways to choose a sample from a sampling frame: randomly or non-randomly. There are benefits to both. Basically, if your sampling frame is approximately the same demographic makeup as your population, you probably want to randomly select your sample, perhaps by flipping a coin or drawing names out of a hat.

4. Determine the sample size. In general, larger samples are better, but they also require more time and effort to manage. If Brooke ends up having to go through 1,000 surveys, it will take her more time than if she only has to go through 10 surveys. But the results of her study will be stronger with 1,000 surveys, so she (like all researchers) has to make choices and find a balance between what will give her good data and what is practical.

5. Implement the plan. Once you know your population, sampling frame, sampling method, and sample size, you can use all that information to choose your sample.

Types of sampling: sampling methods 

Sampling in market research is of two types – probability sampling and non-probability sampling. Let’s take a closer look at these two methods of sampling.

1. Probability sampling: Probability sampling is a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly. All the members have an equal opportunity to be a part of the sample with this selection parameter.
2. Non-probability sampling: In non-probability sampling, the researcher chooses members for research at random. This sampling method is not a fixed or predefined selection process. This makes it difficult for all elements of a population to have equal opportunities to be included in a sample.

There are four types of probability sampling techniques:

• Simple random sampling: One of the best probability sampling techniques that helps in saving time and resources, is the Simple Random Sampling method. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance. Each individual has the same probability of being chosen to be a part of a sample.
  For example, in an organization of 500 employees, if the HR team decides on conducting team building activities, it is highly likely that they would prefer picking chits out of a bowl. In this case, each of the 500 employees has an equal opportunity of being selected.
• Cluster sampling: Cluster sampling is a method where the researchers divide the entire population into sections or clusters that represent a population. Clusters are identified and included in a sample based on demographic parameters like age, sex, location, etc. This makes it very simple for a survey creator to derive effective inference from the feedback.
  For example, if the United States government wishes to evaluate the number of immigrants living in the Mainland US, they can divide it into clusters based on states such as California, Texas, Florida, Massachusetts, Colorado, Hawaii, etc. This way of conducting a survey will be more effective as the results will be organized into states and provide insightful immigration data.
• Systematic sampling: Researchers use the systematic sampling method to choose the sample members of a population at regular intervals. It requires the selection of a starting point for the sample and sample size that can be repeated at regular intervals. This type of sampling method has a predefined range, and hence this sampling technique is the least time-consuming.
  For example, a researcher intends to collect a systematic sample of 500 people in a population of 5000. He/she numbers each element of the population from 1-5000 and will choose every 10th individual to be a part of the sample (Total population/ Sample Size = 5000/500 = 10).
• Stratified random sampling: Stratified random sampling is a method in which the researcher divides the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized and then draw a sample from each group separately.
  For example, a researcher looking to analyze the characteristics of people belonging to different annual income divisions will create strata (groups) according to the annual family income. Eg – less than $20,000, $21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000, etc. By doing this, the researcher concludes the characteristics of people belonging to different income groups. Marketers can analyze which income groups to target and which ones to eliminate to create a roadmap that would bear fruitful results.

Four types of non-probability sampling explain the purpose of this sampling method in a better manner:

• Convenience sampling: This method is dependent on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street. It is usually termed as convenience sampling, because of the researcher’s ease of carrying it out and getting in touch with the subjects. Researchers have nearly no authority to select the sample elements, and it’s purely done based on proximity and not representativeness. This non-probability sampling method is used when there are time and cost limitations in collecting feedback. In situations where there are resource limitations such as the initial stages of research, convenience sampling is used.
  For example, startups and NGOs usually conduct convenience sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do that by standing at the mall entrance and giving out pamphlets randomly.
• Judgmental or purposive sampling: Judgemental or purposive samples are formed by the discretion of the researcher. Researchers purely consider the purpose of the study, along with the understanding of the target audience. For instance, when researchers want to understand the thought process of people interested in studying for their master’s degree. The selection criteria will be: “Are you interested in doing your masters in …?” and those who respond with a “No” are excluded from the sample.
• Snowball sampling: Snowball sampling is a sampling method that researchers apply when the subjects are difficult to trace. For example, it will be extremely challenging to survey shelterless people or illegal immigrants. In such cases, using the snowball theory, researchers can track a few categories to interview and derive results. Researchers also implement this sampling method in situations where the topic is highly sensitive and not openly discussed—for example, surveys to gather information about HIV Aids. Not many victims will readily respond to the questions. Still, researchers can contact people they might know or volunteers associated with the cause to get in touch with the victims and collect information.
• Quota sampling:  In Quota sampling, the selection of members in this sampling technique happens based on a pre-set standard. In this case, as a sample is formed based on specific attributes, the created sample will have the same qualities found in the total population. It is a rapid method of collecting samples.