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Unit 8: Measurement and Scales Unit 9: Questionnaire and Design

A Detailed Report Presented to The Faculty of the College in Business Administration - Graduate School University of the Cordilleras

In Partial Fulfillment Of the Requirements for the Course ADM B001: Research and Quantitative Methods

Submitted by: Andawey, Juvylyn Arciaga, Francis Michael M. Calabias,Keith C. Cardona, Jomar M. Cardona, Rhea Lyn G. Hallig, Triken

October 20, 2020

UNIT 8: MEASUREMENT AND SCALES MEASURES OF OBJECTS, PROPERTIES, AND INDICANT OF PROPERTIES Reporter: Juvylyn Andawey Data Measurement Scales in Research - Scales of measurement in research and statistics are the different ways in which variables are defined and grouped into different categories. Sometimes called the level of measurement, it describes the nature of the values assigned to the variables in a data set. - two keywords in statistics; measurement and scale.  Measurement is the process of recording observations collected as part of a research.  Scaling is the assignment of objects to numbers or semantics. *These two words merged together refers to the relationship among the assigned objects and the recorded observations. Measurement Scale  used to qualify or quantify data variables in statistics. It determines the kind of techniques to be used for statistical analysis.  used to measure qualitative and quantitative data. With nominal and ordinal scale being used to measure qualitative data while interval and ratio scales are used to measure quantitative data. Characteristics of a Measurement Scale a. Identity - refers to the assignment of numbers to the values of each variable in a data set. Example: a questionnaire that asks for a respondent's gender with the options Male and Female for instance. The values 1 and 2 can be assigned to Male and Female respectively. Arithmetic operations can not be performed on these values because they are just for identification purposes. This is a characteristic of a nominal scale. b. Magnitude - is the size of a measurement scale, where numbers (the identity) have an inherent order from least to highest. They are usually represented on the scale in ascending or descending order. Examples: the position in a race which is arranged from the 1st, 2nd, 3rd to the least. This example is measured on an ordinal scale because it has both identity and magnitude. c. Equal intervals - means that the scale has a standardized order. Example: weight in a weighing scale and time in a clock A variable that has an identity, magnitude, and the equal interval is measured on an interval scale.

d. Absolute zero - is a feature that is unique to a ratio scale. It means that there is an existence of zero on the scale, and is defined by the absence of the variable being measured (e.g. no qualification, no money, does not identify as any gender, etc. Levels of Data Measurement The level of measurement of a given data set is determined by the relationship between the values assigned to the attributes of a data variable. For example, the relationship between the values (1 and 2) assigned to the attributes (male and female) of the variable (Gender) is "identity". This via. a nominal scale example.

By knowing the different levels of data measurement, researchers are able to choose the best method for statistical analysis. The different levels of data measurement are: nominal, ordinal, interval and ratio scales. SIMILARITIES AND BETWEEN THE FOURSCALE TYPES USED IN MEASUREMENT Reporter: Rhea Lyn G. Cardona In statistics, there are four data measurement scales: nominal, ordinal, interval, and ratio. These four data measurement scales (nominal, ordinal, interval, and ratio) are best understood with example, as you’ll see below. Nominal Let’s start with the easiest one to understand. Nominal scales are used for labeling variables, without any quantitative value. “Nominal” scales could simply be called “labels.” Here are some examples, below. Notice that all these scales are mutually exclusive (no overlap) and none of them have any numerical significance. A good way to remember all of this is that “nominal” sounds a lot like “name” and nominal scales are kind of like “names” or labels.

Examples of Nominal Scales Note: a sub-type of nominal scale with only two categories (e.g. male/female) is called “dichotomous.” If you are a student, you can use that to impress your teacher. Bonus Note #2: Other sub-types of nominal data are “nominal with order” (like “cold, warm, hot, very hot”) and nominal without order (like “male/female”). Ordinal With ordinal scales, the order of the values is what’s important and significant, but the differences between each one is not really known. Take a look at the example below. In each case, we know that a #4 is better than a #3 or #2, but we don’t know–and cannot quantify– how much better it is. For example, is the difference between “OK” and “Unhappy” the same as the difference between “Very Happy” and “Happy?” We can’t say. Ordinal scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort, etc. “Ordinal” is easy to remember because is sounds like “order” and that’s the key to remember with “ordinal scales”–it is the order that matters, but that’s all you really get from these. Advanced note: The best way to determine central tendency on a set of ordinal data is to use the mode or median; a purist will tell you that the mean cannot be defined from an ordinal set.

Interval Interval scales are numeric scales in which we know both the order and the exact differences between the values. The classic example of an interval scale is Celsius temperature because the difference between each value is the same. For example, the difference between 60 and 50 degrees is a measurable 10 degrees, as is the difference between 80 and 70 degrees.

Interval scales are nice because the realm of statistical analysis on these data sets opens up. For example, central tendency can be measured by mode, median, or mean; standard deviation can also be calculated. Like the others, you can remember the key points of an “interval scale” pretty easily. “Interval” itself means “space in between,” which is the important thing to remember–interval scales not only tell us about order, but also about the value between each item. Here’s the problem with interval scales: they don’t have a “true zero.” For example, there is no such thing as “no temperature,” at least not with celsius. In the case of interval scales, zero doesn’t mean the absence of value, but is actually another number used on the scale, like 0 degrees celsius. Negative numbers also have meaning. Without a true zero, it is impossible to compute ratios. With interval data, we can add and subtract, but cannot multiply or divide. Confused? Ok, consider this: 10 degrees C + 10 degrees C = 20 degrees C. No problem there. 20 degrees C is not twice as hot as 10 degrees C, however, because there is no such thing as “no temperature” when it comes to the Celsius scale. When converted to Fahrenheit, it’s clear: 10C=50F and 20C=68F, which is clearly not twice as hot. I hope that makes sense. Bottom line, interval scales are great, but we cannot calculate ratios, which brings us to our last measurement scale… Ratio Ratio scales are the ultimate nirvana when it comes to data measurement scales because they tell us about the order, they tell us the exact value between units, AND they also have an absolute zero–which allows for a wide range of both descriptive and inferential statistics to be applied. At the risk of repeating myself, everything above about interval data applies to ratio scales, plus ratio scales have a clear definition of zero. Good examples of ratio variables include height, weight, and duration. Ratio scales provide a wealth of possibilities when it comes to statistical analysis. These variables can be meaningfully added, subtracted, multiplied, divided (ratios). Central tendency can be measured by mode, median, or mean; measures of dispersion, such as standard deviation and coefficient of variation can also be calculated from ratio scales. CLASSIFICATION OF VARIABLES ACCORDING TO MEASUREMENTS Reporter: Triken Hallig A measurement variable is an unknown attribute that measures a particular entity and can take one or more values. It is commonly used for scientific research purposes. Unlike in mathematics, measurement variables can not only take quantitative values but can also take qualitative values in statistics. How we measure variables are called scale of measurements, and it affects the type of analytical techniques that can be used on the data, and conclusions that can be drawn from it. Measurement variables are categorized into four types, namely; nominal, ordinal, interval and ratio variables.

1. Nominal variable = is a type of variable that is used to name, label or categorize particular attributes that are being measured. It takes qualitative values representing different categories, and there is no intrinsic ordering of these categories. Examples of Nominal Variable What is your Gender?

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M- Male

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F- Female

What is your Political preference?

Where do you live?

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1- Independent

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1- Suburbs

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2- Democrat

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2- City

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3- Republican

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3- Town

Nominal Variable Classification Based on Collection Technique Open-ended technique gives respondents the freedom to respond the way they like. They are allowed to freely express their emotions. Example: -How do you see your future? -Tell me about the children in this photograph. -What is the purpose of government? -Why did you choose that answer? Closed-ended technique restricts the kind of response a respondent can give to the questions asked. Questionnaires give predefined options for the respondent to choose from. Example: Would you like vanilla ice cream? Have you ever met Joe before? Where did you go to college? NOTE: -Cannot be quantified. In other words, you can’t perform arithmetic operations on them, like addition or subtraction, or logical operations like “equal to” or “greater than” on them. -Cannot be assigned any order. 2. Ordinal variable is a type of measurement variable that takes values with an order or rank. It is the 2nd level of measurement and is an extension of the nominal variable. They are built upon nominal scales by assigning numbers to objects to reflect a rank or ordering on an attribute. Also, there is no standard ordering in the ordinal variable scale.

Example : High school class ranking: 1st, 9th, 87th… Socioeconomic status: poor, middle class, rich. The Likert Scale: strongly disagree, disagree, neutral, agree, strongly agree. Level of Agreement: yes, maybe, no. Time of Day: dawn, morning, noon, afternoon, evening, night. NOTE: -You don’t have to have the exact words “first, second, third….” Instead, you can have different rating scales, like “Hot, hotter, hottest” or “Agree, strongly agree, disagree.” - Ordinal variable data can be presented in tabular or graphical formats for a researcher to conduct a convenient analysis of collected data. 3. Interval variable is defined as a numerical scale where the order of the variables is known as well as the difference between these variables. Variables that have familiar, constant, and computable differences are classified using the Interval scale. It is easy to remember the primary role of this scale too, ‘Interval’ indicates ‘distance between two entities’, which is what Interval scale helps in achieving. Examples: Celsius Temperature. Fahrenheit Temperature. IQ (intelligence scale). SAT scores. Time on a clock with hands. NOTE: -

Interval scale is frequently used as a numerical value can not only be assigned to variables but calculation on the basis of those values can also be carried out. Interval scales hold no true zero and can represent values below zero. The interval variable is an extension of the ordinal variable. In other words, we could say interval variables are built upon ordinary variables. The variables are measured using an interval scale, which not only shows the order but also shows the exact difference in the value.

4. Ratio variable is highest level of measurement. Variable measurement scale that not only produces the order of variables but also makes the difference between variables known along with information on the value of true zero. It is calculated by assuming that the variables have an option for zero, the difference between the two variables is the same and there is a specific order between the options. Example: What is your daughter’s current height? Less than 5 feet. 5 feet 1 inch – 5 feet 5 inches

5 feet 6 inches- 6 feet More than 6 feet What is your weight in kilograms? Less than 50 kilograms 51- 70 kilograms 71- 90 kilograms 91-110 kilograms More than 110 kilograms

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-

NOTE: The ratio scale has an absolute zero or character of origin. Height and weight cannot be zero or below zero. Ratio scale has the same properties as interval scales. You can use it to add, subtract, or count measurements. Ratio scales differ by having a character of origin, which is the starting or zero-point of the scale. To decide when to use a ratio scale, the researcher must observe whether the variables have all the characteristics of an interval scale along with the presence of the absolute zero value.

 Likert scale is a unidimensional scale that researchers use to collect respondents’ attitudes and opinions. Researchers often use this psychometric scale to understand the views of respondents Example:

NOTE: -When responding to an item on the Likert Scale, the user responds based explicitly on their agreement or disagreement level. These scales allow determining the level of agreement or disagreement of the respondents. Likert scale assumes that the strength and intensity of the experience are linear. Therefore, it goes from a complete agreement to a complete disagreement, assuming that attitudes can be measured.

UNIT 9: QUESTIONNAIRE AND DESIGN METHODS OF GATHERING DATA Reporter: Keith C. Calabias Data Gathering or Collection Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. The data collection is an essential part of research that is common to all fields of study including physical, social sciences, humanities, and business. The goal for all data collection is to capture quality evidence that then translates to rich data analysis and allows that building of a convincing and credible answer to questions that have been posed. Types of Data Qualitative Data These data are mostly non-numerical and usually descriptive or nominal in nature. The data collected are in the form of words and sentence. Qualitative approaches aim to address the ‘how’ and ‘why’ of a program and tend to use unstructured methods of data collection to fully explore the topic it tends to capture feelings, emotions, or subjective perception of something. These methods are characterized by the following attributes: • they tend to be open-ended and have less structured protocols • they rely more heavily on interactive interviews; respondents may be interviewed several times to follow up on a particular issue, clarify concepts or check the reliability of data. • they use triangulation to increase the credibility of their findings (i.e., researchers rely on multiple data collection methods to check the authenticity of their results). • their findings are not generalizable to any specific population, rather each case study produces a single piece of evidence that can be used to seek general patterns among different studies of the same issue. Advantages: 1. Provides depth and detail-looks deeper towards the attitudes, feeling and behaviors. 2. Creates openness- encourage participants to expand on their responses which it may create new topics not initially considered. 3. Simulates people’s individual experience- can be built up the participants feeling, thoughts and stand towards the topic 4. Attempt to avoid pre judgements- avoids drawing conclusion without considering the factors why such respond was given. Disadvantage 1. Less easy to generalize-because of few and selected participants it is not possible to generalize results to that population. 2. Difficult to make systematic comparison- participants tend to five widely different responses that are highly subjective. 3. Dependent on skills of the researcher-it needs a skilled moderator in conducting interview, focus group and observation. 4. Expensive and time consuming- conducting research with interviews through various methods is time consuming and costly.

Quantitative Data Quantitative data is numerical in nature and can be mathematically computed. Quantitative data measure uses different scales, which can be classified as nominal scale, ordinal scale, interval scale and ratio scale. They use a systematic standardized approach which makes it easier to make and the size of the effect can usually be measured. It relies on random sampling and structured data collection instruments that fit diverse experience into predetermined response categories. Advantages 1. Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results 2. Can allow for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. To accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability 3. Using standards means that the research can be replicated, and then analyzed and compared with similar studies Disadvantage 1. It collects a much narrower and sometimes superficial dataset 2. Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception 3. The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real-world yielding laboratory results as opposed to real world results 4. In addition, preset answers will not necessarily reflect how people really feel about a subject and in some cases might just be the closest match. 5. The development of standard questions by researchers can lead to 'structural' bias and false representation, where the data reflects the view of them instead of the participating subject. Mixed Methods Mixed methods approach is the combination of qualitative and quantitative data, techniques, and methods within a single research framework. Advantages 1. Compares quantitative and qualitative data- useful in understanding contradictions between the quantitative results and qualitative findings. 2. Reflects participants’ point of view- gives a voice to study and ensure that study findings are grounded in participants‘ experiences. 3. Foster scholarly interaction- adds breadth to multidisciplinary team research by encouraging the interaction of quantitative, qualitative, and mixed methods scholars. 4. Provides methodological flexibility-mixed methods have great flexibility and are adaptable to many study designs, such as observational studies and randomized trials, to elucidate more information than can be obtained in only quantitative research. 5. Collects rich and comprehensive data-it obtains more specific and more complete story or data than either method would alone provide.

Disadvantage 1. Increase complexity of evaluations- it requires careful planning to describe all aspect of research, including the study for qualitative and quantitative data, timing, and the plan for integrating data. 2. Reli...