When talking about the world of statistics, one cannot talk without mentioning types of quantitative scales. Such a title is quite deserved. It is very important for all kinds of professions – teachers, students, researchers, corporate managers, media outlets, etc. – to have some knowledge about such scales in order to make their work easier and to come up with better output and value-added data sets.

Different scales can be used. One common type is the ordinal scale, which is widely used in almost all fields of study. Ordinal scales are graphical works that transform a domain of numerical values into a range of more definite graphical values (i.e., numbers, colors, measurements). For example, the distance from the sun to the earth or the weight of an object can be thought of as an ordinal dimension. Another common type of quantitative scale is the ratio scale, used to represent time (and thus, height and temperature) and distance.

One more important type of quantitative scale is the probability scale, which compares the value of a property over time. This can be thought of as a range value or probability density over time. Another common type is the spatial probability scale, which is used to represent spatial data domains (such as locations). The third popularly used type is the binomial curve, which calculates the probability distribution of a particular data range. This is based on logistic regression.

Some scales are binomial (x-intercept) scales (log-normal deviation), log-normal (x-intercept), or quadratic (log-normal plus tails) based. Other scales are simply point-form distributions. One common example is the binomial logistic or Gaussian random variable. A log-normal exponential curve has the same range as the natural log. The probability of sampling even a single point from a Gaussian curve is very low.

There are also other types of scales that can be considered useful for scientific analysis. One is the arithmetic mean, which is the asymmetrical mean of the underlying distribution. Another is the binomial curve, which involves a mean and a mode. Another type is the mathematical cephalic mean, which is formed by dividing the range of values by the square root of their frequencies. And, a few more include the logistic scale, the exponential distribution, and the logistic function scale.

The most visual analysis uses some kind of graphical scale. It may use a range, a data set, or just one or more of the many graphical scale types. The most common visual scale is the plotted line or spike chart, but more complex charts that make use of color for presentation also appear to be quite popular in the quantitative field. Other types of charts that are often used in science, engineering, and statistics studies include the histogram, the scatter plot, and the panel chart.

One thing that must be understood about the use of quantitative data in research is that different types of quantitative scales cannot be used at the same time, if at all. This is because data interpretation and the reporting of that information require a wide variety of methods, which depend on the type of scale being used. Some quantitative scales cannot be analyzed using more than one type.

For example, the Student’s t-test, also known as the Wertheimer Test, can be performed using either a one-element array (a two-element array consisting of two numbers) or a two-element ensemble (a two-element array of numbers and their corresponding interpretive symbol). One-element arrays can be analyzed using a normal bell curve or by a logistic function.

Two-element arrays, however, cannot be analyzed by a normal bell curve or by a logistic function. If the data set involves a significant number of numerical elements, then it is usually preferable to analyze the data using a logistic function or a two-element ensemble rather than the Student’s t-test. In cases where data analysis requires the employment of a two-dimensional array, then the common type of analytical method employed is the parabola.

This was a small post to share few scales that are not very common. To check the scales that are commonly used, you may watch the following video.

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The importance of consumer insights in decision-making is well recognized in India. It was the British colonialists who changed the direction of the economy, unleashing the power and potential of markets to cater to consumers more efficiently. This made the masses consumers instead of just shopkeepers and entrepreneurs. Today, Indian markets are no longer dominated by the elite and top profiteers, but by the middle classes, as reflected in the extensive use of the term “Lake in India” to indicate products that have been made in India and imported into the country.

A market is only as good as its distribution which is a complex phenomenon. It is influenced by various forces such as demand, price, competition, and many others. All these forces have an impact on the distribution of goods and services in the market. One cannot ignore them and hope that things will remain as they are.

Consumers are the most important force in determining the quality and quantity of any product or service. With their insights into the market, they can make informed decisions as to where to spend their money. They will also have an idea as to the level of satisfaction they can expect from the service they receive. As far as distribution is concerned, the consumers can play a major role. They decide where and how they want to spend their money. That is the power of consumer insights in decision-making.

However, the power of consumers is not confined to distribution alone. It extends to pricing also. The consumers are the ones who decide which products and services are cheap or expensive. This is the power of market dynamics. When the consumers make their decisions based on economic factors, the prices of the products and services will follow.

The decision-makers, i.e., retailers and other big businesses, pay very close attention to the price of the product or service they offer. The consumers will be the ones to determine the level of affordability of the product or service.

The information available to them gives them the tools to bargain for lower prices with suppliers and make good deals. If they find out that the consumers are getting cheated by sellers of products and services, the power of consumer awareness will play a decisive role in their decision-making. The sellers will start to see that the competition is heating up and will find it increasingly difficult to maintain prices.

Consumer insights also influence the manner in which businesses operate. In industries where consumers have specific demands, they are the ones who ensure that the goods and services meet these demands. If consumers feel that the quality of the product is poor, they will not buy it. On the other hand, if they feel that the service they get is good, then they would not hesitate to patronize that business.

Consumer insights also give businesses important signals about the type of customer they should target. The analysis shows that consumers do not purchase the same type of product or service from different companies. Instead, they prefer brands that resonate with their values, lifestyle, and other characteristics. Thus, a business may choose to focus on the more lucrative segments of consumers. It may choose to specialize in providing goods and services that are in line with what consumers need. Alternatively, it may focus on services that consumers cannot live without.

By providing an analysis of the way in which consumers use – different marketing tools and businesses will be able to adapt quickly to consumer demands. Once the necessary changes are made, consumers will not only continue to patronize the business but also refer others to the business. This, in turn, will boost its market share and expand the market share of the business. This is how consumer insights play a crucial role in business. Without them, businesses would not be able to survive and grow.

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Quantitative Research Types

Quantitative Research – Experimental

• True experimental
  • • True experimental research aims to determine causal relationships among variables, and it relies on statistical analysis to approve or disprove a hypothesis. It is also worth noting that true experimental research is considered the most accurate type of experimental design.
    • It may be conducted with or without a pre-test, and it always involves a control group and a test group.
    • Other characteristics include variable manipulation, Random selection of participants. It is always conducted in a controlled setting, and intervention is always present.
    • For example, a researcher wants to do quantitative research and determine the effects of classical music on students’ academic performance. Since this is true experimental research, it must have at least two groups. One would be the control group, and the other one would be the test or experimental.
      • In the control group, everything is held constant or regular, which means that there would be no intervention, which means there is no music or no music played in their classes.
      • While in the test or experimental group, there is the presence of intervention which is classical music. This intervention can further be manipulated with variables such as frequency and music volume.
      • At the end of the study, the researchers assess the control without music. And test or experiment with music by looking at their academic performance.
  • Based on this example, we conclude that played classical music was studying as a positive effect on the student’s grades.
• Quasi-experimental
  • • Similar to true experimental research, this type of research aims to determine causal relationships among variables.
    • It bears a resemblance to true experimental research but different because participants are not randomly assigned in this type of research. It involves the conduct of the pre-test and post-test.
    • Furthermore, other characteristics of the Quasi-experimental research involved variable manipulation. It’s also conducted in a controlled setting, and that the intervention is also present.
    • For example, a teacher wants to do quantitative research and test a reading intervention program to aid students with a low level of comprehension. Since this is Quasi-experimental research, this study would usually start with the pre-test.
      • The pre-test would be a reading comprehension test that would help determine who among the students has passed and who has failed.
      • Recognizing the students to a field is essential as these students will be considered participants for the reading intervention program.
      • Once the participants have been identified, they shall undergo their reading intervention program, which can further manipulate them through the duration of the program, the schedule given to the students, the reading activities given, and the teaching strategy that is used.
      • After undergoing intervention, we now proceed with the post-test, which is another reading comprehension test. The students who spilled their reading comprehension tests given before would have experienced the reading intervention program and will then be given another reading comprehension test to determine whether they will pass or still fail.
      • If they have passed, we can conclude that the reading intervention program effectively improves the reading comprehension of students with low-level understanding.

Similarities between True and Quasi-experimental design of quantitative research

1. Both are used to determine causal relationships
2. Both have interventions
3. Involves manipulation of variables
4. both are conducted in a controlled setting

Differences between True and Quasi-experimental design of quantitative research

1. There is a random selection of participants in True experimental, but in Quasi, the selection is not random.
2. True experimental may be conducted with or without a pre-test, while Quasi-experimental research involves pre-test and post-test.
3. True experimental research involves a test group and a control group, while Quasi-experimental research does not have any control group.

Quantitative Research – Not experimental

• According to Purpose
  • Surveys
    • • • Survey research gathers evidence on people’s knowledge, opinions, attitudes, and values on various issues and concerns.
        • The survey research makes use of questionnaires, interviews, and surveys. Also, in survey research, the variables are not controlled or manipulated, and no intervention is applied.
        • For example – A researcher wants to survey the preference of individuals towards mass testing. What he does is identify the participants and give them the questionnaires that they will answer.
        • Again, there is no intervention in survey research because the researcher would only wait for the respondents to give out their answers. Also, in survey research, researchers focus on recording and tallying the responses, analyzing and interpreting that data.
        • From these detailed data, he comes up with conclusions. The data that the researcher was able to identify is that those who answered yes are 75 percent, while those who answered No are 25 percent. From this detailed data, the researcher then concludes that the majority, or 75 percent or therefore Group A, favors mass testing.
  • Correlational Research
    • • • The correlational research seeks to interpret the degree of relationship between 2 or more variables using statistical data. Similar to the survey research, the variables are not controlled or manipulated, and no intervention is applied here.
        • It is also worth noting that while correlational research aims to interpret the degree of relationship between variables, it does not seek to determine cause and effect relationships.
      • For example, the researcher wants to do quantitative research to determine the influence of online gaming on students’ critical thinking.
        • • In the study, he hypothesized that there is no significant relationship between online gaming and critical thinking.
          • To conduct this detailed research, the researcher needs to find online gamers who will agree to become participants in our study. Again in the correlational analysis, there is no intervention because the responsibilities of researchers focused primarily on gathering the needed data, applying the correct statistical treatment, analyzing and interpreting the treated data, and rejecting or accepting the hypothesis.
          • Now going back to our online universe, these participants are then given instruments that would assess their online gaming habits and their critical thinking level.
        • From there, we will give the appropriate statistical treatment to determine whether there’s a relationship between online gaming and critical thinking and whether an increase in online gaming would increase critical thinking or vice versa.
• According to time
  • Cross-sectional research
    • • • Cross-sectional research involves gathering data at a single point in time. It focuses on the same set of variables, after which comparisons are made across variables of interest.
        • For example – A researcher once identified the spending trends between men and women in their thirties. From this detailed research, the findings came that women tend to spend more money than men.
      • The researchers then take this particular data in cross-sectional research and compare it with other variables like age brackets. He will then compare whether there are similarities or differences in terms of the trends and results.
  • Longitudinal
    • • • In longitudinal research, the data is collected at multiple points in time. A researcher collects data from the present and again in the future to compare datasets.
        • For example – A study on the effects of antiretroviral medicines as a maintenance drug for HIV-positive patients needs to be done. The participants for this particular study are individuals who tested positive for HIV.
        • At the start of the longitudinal research, their HIV viral load is taken and reported before they are given the medicines. After which their viral load is monitored on a specific schedule to determine how the drugs are working.
    • Based on this particular example, you could say that it takes quite a long time before this specific study is finished, hence longitudinal.

As researchers, it is essential to understand the Quantitative research types. This knowledge will help us to determine the best research design for the research objectives.

In this post, I’ve covered the basics of Quantitative research types. If you found my post helpful, then do share it with your friends and colleagues. If you have any feedback/questions, you may leave a comment below.

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Qualitative research is a type of research that focuses on explaining the world’s reality through the participants’ perspectives. Also, it is characterized by the researcher’s active involvement when interacting with participants while gathering data in the natural setting. Lastly, in qualitative research, the research study results tend to be subjective because it is affected by the perceptions and opinions of the respondents based on their experiences.

Now that we have understood what qualitative research is, the next question is the different types of qualitative research design.

Qualitative Research Designs

1. Case Studies

• • It requires a long time and in-depth study of Organization or situation or phenomena.
  • This type of study is often used to narrow down a very broad field of research into one or a few easy research examples.
  • It is also useful for testing whether a specific theory and model applies to the nominal in the real world.
• Example – How their environment influences students’ vocabulary development?
  • • Hypothesis – The environment influences a person’s vocabulary acquisition and development.
    • With this in mind, the researcher would interview two different groups of students with the different environment (stream, for example)
    • This case study would have multiple observations starting with the beginning of the senior high school years, middle of the senior high school, and the student’s high school years.
    • From there, the researchers can compare whether there’s a difference or similarity in terms of conversation expressions used and known vocabulary words between the different groups.
  • Based on the results, the researcher should be able to come up with conclusions.

1. Ethnography

• • It involves studying a particular cultural group to get a clear understanding of their belief systems and practices.
  • It also aims to have a more in-depth understanding of all of the group members’ organizational setup and lifestyle.
  • It is also worth noting that ethnography falls under the field of anthropology.
• For example, we will have to study that observes the culture and practice of people living in far-flung areas away from modernization.
  • • With this in mind, the researcher would have observed a group of people living in visiting far-flung areas.
• The researchers then typically interact with people through
  • • • Interviews
      • observations
    • Immersions
    • This kind of practice helps the researchers have firsthand experience of the practice of the group members and obtain firsthand testimonies from the research participants.
    • And present accurate reports regarding the research subject since the researcher can interact with them directly and personally.
  • Since the research involves a particular group of people, the results are context-specific. When you say politics specifically, the findings apply only to those specific people and not the general public.

1. Phenomenology – It aims to examine the human experience regarding a certain phenomenon. Also, it recognizes that each experience is unique and meaningful.

• • The objective of phenomenology is to present a live experience of the participants involved in the study.
• Example – We need to recognize the different strategies used by international students to adapt to the new language learning environment. 
  • • The researcher would interact with different international students who are studying in a particular country.
    • The researcher interacts with the participants and does few interviews, such as focus group discussions or in-depth interviews.
    • During these discussions or interviews, the researcher would then be able to identify the different experiences, struggles and adapt strategies used by the participants.
  • From there, the researchers would be able to come up with a detailed discussion of the results.

1. Historical Analysis – The historical analysis involves examining primary documents to make you understand the connection of past events to the present time.

• • The historical analysis researchers rely on primary sources of historical data.
• For example – The development of the educational system in the Philippines.
  • • With this in mind, the researcher needs to have primary sources of historical data.
    • Some examples would be direct accounts of events, Archival data, official documents, personal records and, Eyewitness records.
    • Once the researchers can analyze the sources of information, the researcher would then be able to identify how the educational system in the past.
  • It’s connected with the educational system in terms of policy changes, Friendships, Teaching strategy, development, and updates in the material used.

1. Content and Discourse analysis – This type of qualitative research design requires analysis or examination of text or content of the modes of communication. Furthermore, according to public health at Columbia ensues puts it in discourse analysis is conducted for the following reasons.

• • Identify the intentions, focus, or communication trends of an individual, group, or institution.
  • Describe the attitudinal and behavioral responses to communications.
  • Reveal the international differences in communication content.
  • Reveal the patterns in communication content.
  • Determine the psychological or emotional status of the person or group.
• For example – If a researcher wants to determine or understand the language of journalism. He would analyze the different modes of communication, such as journals, articles, and audiovisual materials.
  • Based on this analysis, the researcher would then be able to identify how journalistic jargon or the language of journalism has similarities and differences in terms of regular communication jargon.

1. Grounded Theory – This occurs when a researcher discovers a new theory to underlie his study at the time of data collection and analysis.

• • It also explains the main concern of the participants involved in the research. 
  • Through various means of data collection such as interviews, observations, fieldwork, focus group discussions, and study of artifacts and texts, the researcher would be able to discover an idea that would explain the how and why behind a particular phenomenon.

Summary

There are few other advanced methods like Semiotics which I’ve not covered here intentionally to keep this post simple.

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This is a book that really has mixed reviews everywhere. Hence, in this book review I want to share my views on this book. So let’s get started.

Freakonomics is written by 2 authors(mentioned above). One is an actual economist and then the other is a book author and they met because the author was interviewing the economist for The New York Times magazine. That was the time when they decided to write a book together. The interesting thing about this book is that there’s no unifying theme. Each chapter is like a separate entity in itself. But, the names of each chapter are super interesting.

What’s Inside Freakonomics?

The first chapter talks about how school teachers and sumo wrestlers are common. It talks about the incentives and how it is used to control human behavior. The author shares a story of incentives in the school system and how teachers used those incentives for their benefit. Similarly, how Sumo wrestlers used it for theirs. Some of it may look far-fetched but the comparison is purely from the eyes of the author. Some readers feel this comparison to be completely rubbish. This is completely fine because the author himself acknowledges this fact.

In another chapter, he talks about how a simple law on abortion was responsible for bringing down the crime rate. He gives an example of when abortion was legalized and how eventually the crime rate dropped in that city.

Another chapter that talks about what makes a perfect parent talks about nature versus nurture and if parents actually have an impact on how children turn out. There’s another chapter on How old people name their children so there’s a story about a guy named the winner and that his brother was named loser and whether their name had any impact on the success and outcomes of their life?

The chapters of the book are on a longer side (200 pages) but you could read it quickly because there is no unifying theme and each chapter is separate. People who are into statistics, data, and analysis would like this book.

This book really makes you stop and think about things going on around you. Why things are the way they are? This book would give new perspectives of how a normal person would look at the economics of the world. That’s the book of curiosity and if you read it let me know in the comments.

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• Someone who is facing difficulties in interpreting the data.

• Someone who wants to improve the data interpretation/reading skills

In this post, I will give you 2 important tips which will change the way you approach any type of data. This will definitely help you in interpreting data like a pro.

“Our problems are not with the data itself, but arise from our interpretation of the data” – Bruce. H. Lipton

1. Perspective/Hypothesis

The importance of having a perspective before looking at the data is a bit under-rated in the real world. When I started my career in market research back in 2011, not having a perspective before looking at the data was one of my biggest mistakes. I used to get so lost in the data that I would forget what I was really looking for. I see this challenge with many new professionals in the industry which is why I am sharing my learnings over a period of time.

Now, as a first step before jumping into a data ask yourself what you are really looking for or what you are expecting the data to tell you?

For Example – You are about to look at the monthly car sales data and see if the trend is increasing/decreasing. Now, before jumping into the data you should have a perspective/view on the trend beforehand as well. Suppose, you have a view that the car sales would be declining because the economy is in bad shape.

Now with this perspective when you look at the data, you would immediately get your answer.

• If the data shows that the car sales are declining – you’ve successfully understood that data.
• But, supposing the data shows that car sales are increasing against your perspective. You will find a feeling on inquisitiveness inside you to find the underlying reason for this.

As a result, you will automatically start looking for answers and deep dive further.

This is just a simple example to explain the power of perspective before reading the data. This is applicable to any data you want to read.

2. Data Type

Many times we do the mistake of reading qualitative data quantitatively and vice versa. It is important to have clarity in the back of your mind whether you are looking at quantitative data or qualitative data. For the purpose of this post, I’m not going into details about these data types as these are wide topics to cover.

Knowing the data types reduces the chances of misinterpretation of the data.

For Example – Qualitative data can tell you exhaustively what are the various car segments available in the market. But, only Quantitative data will be able to tell you the size and growth of each of these segments.

When you are analyzing the Quantitative data, the concept of the Significance test plays a very important role. For example, the size of the hatchback segment in two independent populations comes out to be 25% to 26% respectively. Now, this may look like 1% more in 1 data set than other but it is not statistically significant to be able to confidently say that.

Similarly, in Qualitative data, if a particular segment size of the car seems bigger than others it only gives a hypothesis and not a conclusive picture.

These are small examples to explain how these data sets can be misinterpreted.

Following the above 2 points will definitely improve your data analyzing skills. these are also the 2 big mistakes that I did when I started my career and hence, wanted to share my learnings for the benefit of everyone.

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