...Measurement Scales Tina M. Biebl MKT / 441 Marketing Research University of Phoenix Introduction A poorly designed questionnaire may not obtain the results the firm or organization is looking for. There are four categories in which numbers are generally grouped. In increasing order of sophistication, they are (1) nominal numbers, (2) ordinal numbers, (3) intervally scaled numbers, and (4) ratio-scaled numbers. This paper will examine each briefly and how they can be used effectively in the design of a survey questionnaire. Qualitative Data Data that can be categorized into one of several non-numerical categories is qualitative. You are recording some quality that the data possesses. Care needs to be taken to avoid assigning numbers to the categories then computing with them. This type of data is either nominal or ordinal. Nominal The number we assign to some object, idea, or behavior is entirely arbitrary, although in some cases a tradition may establish the rules of assignment. If measurements are assigned arbitrary numbers, they are called nominal numbers, and their sole purpose in the analysis is to differentiate an item possessing one characteristic from an item possessing a different characteristic. Nominal data is a type of categorical data in which objects do not have a natural, meaningful order. You can count but not order or measure nominal data. Only calculations based on the frequencies of occurrence are valid. Nominal scales have no numeric...
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...the four key scales of measurement of these variables. Nominal, Ratio, Ordinal, and Interval. Each scale also contingent on the variable. Nominal scale of measurement is used with variables that have numbers with no value or that are not numeric. They cannot go in any order. “Nominal measurement scales denote to those measurements when the only meaningful results are the delineations that one thing is different from another (www.adprima.com).” Nominal scales label variables with no quantative worth. The advantage of using the nominal scale of measurement is classification, by names or labels only. The disadvantage is it is the nascent structure with no quantative value being found. Some examples of using a nominal scale are such as multiple choice questions, or such as going to the hospital and they ask you to rate your pain on a scale of one to ten. Having a true or false question or asking your gender on a questionnaire is also examples of the Nominal scale of measurement. The Ratio scale of measurement provides the decisive interval value and order. True zero can be identified which allows for one to calculate ratios which is a big advantage. Ratio scales” are the same as ordinal scales but with the important difference of one thing, the Ratio scale contains a zero, which allows for negative values to be expressed in relation to positive values (www.adprima.com).”Some examples of the ratio scale of measurement are height and weight measurements, map scales, breaking down...
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...Introduction There are four measurement scales, or types of data, nominal, ordinal interval and ratio. These four measurements are simple ways to categorize different types of variables. This paper will discuss the usage of each scale. Nominal Nominal scales are the most commonly used in marketing research. Nominal scales are used for labeling variables, without any quantitative value. In fact, Nominal scales could be called “labels”. Nominal are categories with numbers assigned to them to facilitate analysis. “A nominal scale partitions data into categories that are mutually exclusive and collectively exhaustive, implying that every bit of data will fit into one and only one category and that all data will fit somewhere on the scale.” (McDaniel, C. Jr., & G, R. (2007). Marketing Research (7th ed. p. 269) Nominal uses numerals to identify objects, individuals, events, or groups. This determines the equality or inequality of a situation. The classification that nominal falls under is male/female, buyer/non-buyer, etc. Most nominal data is collected via questions that provide the respondent a list of items to choose from. Examples of Nominal Scales Ordinal “Ordinal scales have the labeling characteristics of nominal scales plus an ability to order data.” (McDaniel, C. Jr., & G, R. (2007). Marketing Research (7th ed. p. 270). With ordinal scales, it is the order of the values that is important and significant. In most cases, Ordinal scales are typically measures...
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...Measurement Scales Antonio J. Velazquez Ayala MKT/441 11/10/2013 Prof. Luis Ríos Mesasurement Scales Un cuestionario mal diseñado puede no obtener los resultados que la empresa u organización está buscando. Existen cuatro categorías en las que generalmente se agrupan números. En orden creciente de sofisticación, son números (1) nominales, (2) números ordinales, números de escaladas (3) intervalos y (4) escala de cociente de números. Este artículo examinará brevemente cada uno y cómo pueden ser utilizados eficazmente en el diseño de un cuestionario. El número que asignemos a algún objeto, idea, o comportamiento es totalmente arbitrario, aunque en algunos casos una tradición podrá establecer las reglas de asignación. Si las mediciones se asignan a números arbitrarios, se llaman números nominales, y su único propósito en el análisis es distinguir un elemento que posee una característica de un elemento que posee una característica diferente. Datos nominales son un tipo de datos categóricos en que los objetos no tienen un orden natural, significativo. Usted puede contar pero no en orden o medida datos nominales. Sólo los cálculos basados en las frecuencias de ocurrencia son válidos. Las escalas nominales no tienen propiedades numéricas. La información cualitativa se obtiene de una escala nominal. Esto significa que los objetos se clasifican por su nombre solamente. El contar es la única operación que puede realizarse en una escala nominal. Ejemplos...
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...Methods, Characteristics, Structure and Early History of Attitude Measurement Scales Abstract: Measurement of attitudes is beneficial in various aspects of day to day life. Business, social and government research often rely on the measurement of respondents attitudes to guide decision and policy making. Specific research designs and methods are required to ensure useful and valid quantitative data are end results of attitude research projects. Ground breaking researchers in the field of Sociology and Psychology developed the first reliable attitude research and scaling techniques over 50 years ago that are still in use today. An “attitude” is a theoretical entity constructed to characterize certain underlying response tendencies (Arul, 2012). As a hypothetical concept, attitudes cannot be measured directly. Any attempt to measure them “can only be inferential in nature: that is, we can only study behavior which is reasonably assumed to indicate the attitudes to be measured and quantify these indications so as to get an idea of how much individuals or groups differ in their psychological orientations toward a particular object or issue” (Arul, 2012, pg. 1). Attitudes have three components: affective, cognitive, and behavioral (Zikmund, Babin, Carr, Griffin, 2013). The affective part refers to person’s general feelings or sentiments toward an object. Put another way, a person’s attitudes are driven directly by their beliefs or thoughts. This cognitive...
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...La pregunta tres, La industria de los seguros presta mucha a los servicios para los dueños de mascotas en los EU según investigaciones realizadas y por ende pensamos implementar un seguro para nuestras mascotas en PR. En la actualidad existen una 20 compañías que ofrecen seguro de salud para mascotas en Estados Unidos y unas 5 en Canadá. Las pólizas para mascotas se han disparado en popularidad debido a que la tecnología moderna ahora permite que las mascotas sean sometidas a procedimientos y cirugías que eran inconcebibles hace poco tiempo. Nosotros haremos un plan de implementación de rápido aceptación. Sabemos que a la mayoría de las personas que tienen mascotas les interesa esto porque se gastan mucho dinero cada vez que se le enferma su mascota. Seguramente, si usted tiene una mascota, debe quererla y cuidarla como una integrante más de la familia. Entonces, si usted cuenta con un seguro médico, ¿por qué no proteger a su mascota de la misma manera? En nuestro producto le presentaremos uno de seguros médicos para sus mascotas. Las cubiertas son variadas tales como hospitalización, asistencia domiciliaria, cirugías y pruebas diagnósticas. Estos seguros son similares los seguros médicos que actualmente tenemos para las personas. La cobertura de asistencia veterinaria por enfermedad cubre los análisis de laboratorio, las pruebas diagnósticas, la anestesia y gastos luego de una cirujia, la intervención quirúrgica, los medicamentos y la hospitalización quirúrgica. ...
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...characteristic of an individual and then placing him directly on a scale that has been defined in terms of that characteristic and (ii) constructing questionnaires in such a way that the score of individual’s responses assigns him a place on a scale. It may be stated here that a scale is a continuum, consisting of the highest point (in terms of some characteristic e.g. preference, favourableness, etc) and the lowest point along with several intermediate points between these two extreme points. These scale point positions are so related to each other that when the first point happens to be the highest point, the second point indicates a higher degree in terms of a given characteristic as compared to the third point and the third point indicates a higher degree as compared to the forth and so on Scale classification base Scaling procedures may be broadly classified on one or more of the followings:- (1) Subject orientation (2) Response form (3) Degree of subjectivity (4) Scale properties (5) Number of dimension (6) Scale construction techniques Importance of scaling techniques We now take up some of the important scaling techniques often used in the context of research specially in context of social or business research. i. Rating scales: The rating scale involves qualitative description of a limited number of aspects of a thing or of traits of a person .when we use rating scale (or categorical scales), we judge an object in absolute terms against some specified...
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...Задачи по професионално студийно осветление 1. Използване на осветлени с цел намаляване на контраста, имитиране на естествена светлина. Избираме от два варианта: имитиране на облачен ден и имитиране на слънце в кадъра. Вариант 1, имитиране на облачен кадър: кадър без осветление: кадър с осветление: / Една светкавица в тавана, една в стената и софтбокс - задно странично./ Вариант 2, имитиране на слънце в кадъра: кадър без осветление и модел: кадър само със странично осветление: кадър само с предно осветление: финален кадър: /забележка - от лявата страна светлината е прекалено силна (не бива да е така) 2. Снимка с осветително тяло в кадъра... (за справка вижте: practical light) само лампата с лампата в кадър: /халогенка от посоката на лампата обърната в бяла повърхност и светкавица с пита в лицето/ слабата сянка е от халогенката, а плътната от светкавицата /експозиция по лампата – f 2.8, 1/8, исо 100 3. 3 бр кадри с рисуващо и запълващо осветление, като контраста на единия е 1:32, на втория 1:8 и на третия 1:2. (така че да има видима разлика между трите) Кадрите трябва да имат логично използване светлина и да не се вижда как просветляваме сенките /запълващото е до позицията на камерата, от страната на рисуващото. (това е най-логично падащата светлина, заради потъването на лявата част на лицето, като в същото време имаме и достатъчно просветляване от неосветената от рисуващото страна) / правилно поставено като посока запълващо при контраст...
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...Scaling Challenge #1: Passing the Hat AS a start-up, executives need to be prepared to wear more than one functional hat to be successful. As you scale, however, you must carefully oversee the process of passing those hats to new executives and managers that join the team. This looks easy on paper (just draw up the new org chart), but it can prove extremely challenging in practice. While it is easy to pass the hat in form, it may not be easy to pass the hat in substance when the on-boarding of the new hat owner is arduous, i.e., big learning curve, lots of important internal relationships, and so forth. As long as the old hat-owner offers greater knowledge and effectiveness in the relevant functional area, everyone in the organization will gravitate to her for decisions and support, regardless of what the new org chart says. The problem can be further exacerbated if the old hat owner isn’t all that willing to pass the hat in the first place or if the old hat owner is too willing and runs away from the responsibility faster than the new executive can get up to speed. Poor hat passing can result in confusion, frustration, conflict, executive turnover and ultimately poor business performance. Hat passing is tricky business that requires the buy-in all of all those affected, a solid foundation of respect between the two hat-passers, and a thoughtful approach to managing the transition. Without these fundamentals in place as you are scaling, you may find that you are dropping more...
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...Chapter Seven Answer 1. Describe the four type of scale? • Nominal scale , categorize the variable only in the way to measure differences • Ordinal scale , categorized and rank-order variables in some meaningful way • Interval Scale , categorized and rank-order variables in some meaningful way and measure the distance between any two points on the scale • Ration scale , categorized and rank-order variables and measure the distance between points and also taps the proportions in differences 2. How is the interval scale more sophisticated that the nominal scale and ordinal scale? As the interval scale not only group individual according to certain category and taps the orders of this category it’s also measure the magnitude of differences in preferences among the individuals , and measure of center tendency the arithmetic mean and dispersion and stander deviations 3. What is the ratio scale consider to be the most powerful of the four scale? As ratio scale not only magnitude of the differences but also their proportional, ratio scale provide more precision in quantifying data and greater flexibility in using powerful more powerful statistical test 4. Briefly describe the differences between attitude rating scales and rank rating scale and indicate when the two are used? • Rating scale have several response category, use when we need to elicit responses with regard to the object or event or person • Ranking Scale make comparison between objects, events m person...
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...sequentially and select elements from the list randomly. We would not know what the outcome would be, but each has an equal shot at being chosen. We can also program the computer to choose a random sample. Researchers may also use non probability sampling. In this type of sampling the researcher does not know the probability of each population’s elements inclusion in the sample (Dejong, 2011). Data Collection There are many different ways to collect data for research. Measures differ from one another in terms called levels of measurement. Nominal measures classify observations into mutually exclusive and exhaustive categories. They represent nominal variables such as sex, ethnicity, religion, and political party preferences. For the purpose of data collection a number might be assigned to each category. Ordinal measures are at a higher value then nominal numbers because categories have a fixed order. Interval measures share the characteristics of ordinal scales, mutually exclusive and exhaustive categories and an inherit order, but have equal spacing between the categories....
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...Instrument-calibration error, natural errors or personal error). - Can be eliminated by pre-calibrating against a known, trusted standard. - Affects accuracy 2. Random Errors - Results from fluctuations in the readings of a measurement apparatus, experimenter's interpretation of the instrumental reading or randomly changing conditions (weather, humidity, etc.). - Can be reduced by averaging multiple measurements. - Unbiased - Affects precision Uncertainties in Measuring Devices General rule of thumb used to determine the uncertainty in a single measurement when using a scale or digital measuring device. 1. Uncertainty in a Scale Measuring Device is equal to the smallest increment divided by 2. 2. Uncertainty in a Digital Measuring Device is equal to the smallest increment. In general, any measurement can be stated in the following preferred form: The measured value is just an estimate and thus it cannot be more precise than the uncertainty of the device. (i.e. The number of decimal places for the measured value must match the number of decimal places for the uncertainty, and in multiples of the uncertainty)” Example: The smallest increment in a meter rule (scale measuring device) is 0.1 cm. Following the general rule of thumb to determine the uncertainty in a scale measuring device, it would therefore be half of the smallest increment (∆ l = 0.1/2 = 0.05 cm). The uncertainty of a meter rule is ± 0.05 cm, thus for the length measured, l = 31.225 ± 0.05 cm (incorrect) l = 31.23...
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...Measuring Aids stigmas in people living with HIV/AIDS: the Internalized Aids-Related Stigma Scale. 1. Write a brief description and summary of your article. The objective is to measure the psychometric properties of instruments to measure Internalized Aids-Related Stigmas. This article talks about the psychometric measurement of people living with Internalized Aids Related Stigmas (IARS) and the effect that IARS has on communities and individuals. It reviews the notion that stigma is the real driver of poor uptake of testing and treatment services. 2. What type of research method was used to collect data for this study? Data was collected through 3 cities/countries. Men and woman diverse in ages, race and culture were recruited from medical centers in these cities/countries. Questions on the surveys covered age, race employment, marital status, hospitalization and the year and month they were tested positive. Items were selected from the IARS scale (Kalichman 2005) and reframed and reworded to show negative self-perceptions in being a person living with HIV/Aids. The question were answered with 1 = agree and 0 = disagree. The higher scores representing Internalized Stigma. 3. Why do you think the researchers chose this method for this study? The article focuses on the methodology of measuring IARS scale. The objective of these structures is to create and validate the items to measure IARS. The method has been used before and has proven to be consistent...
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...kHz, 278mVPP. In this lab, methods of improving measurement accuracy, dealing with floor noise and averaging were all explored. Overall this lab was a useful tool in understanding the lab equipment. RESULTS AND DISCUSSION: PART 1 - QUESTION 1 i) A 1 kHz, 2.78 VPP sinusoid wave is displayed with trigger level at 200 mV (rising edge). See Fig.1 Fig.1: Time Domain Representation of Signal Power dissipated in the load: P_load=〖V_rms〗^2/R , where V_rms=V_amp/√2 and V_amp=V_PP/2 So, V_amp=(2.78 "V" )/2=1.39 "V" and V_rms=(1.39 "V" )/√2=0.9829 "V" Hence, P_load=((〖0.9829 "V" )〗^2)/(50 "Ω" )=19.32 "mW" ii) When we set the triggering level to 1.5V, the resulting signal displayed is an unstable one. The reason for this is because the trigger level must be on the rising or falling edge. iii) A 1 kHz, 2.78 VPP sinusoid wave is displayed and the accuracy of the measurement is calculated. The volts/division is equal to 500 mV. See Fig.2 Fig.2: Accuracy of Time Domain Representation of Signal error = 2% Vfs , where Vfs= (number of spaces) x (volts/division) So, Vfs = 8 x 500 mV = 4 V Hence, error = 2% x 4 V = 80 mV Thus, Accuracy = 2.78±0.08VPP iv) The embedded functions on the oscilloscope are explored to improve the quality of the measurement. One of these functions was to use the ‘fine’ volts/Div to display the signal across the entire screen of the oscilloscope. As a result a more accurate measurement can be made. See Fig.3...
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...SL Chemistry Name______________________________________________ IB Guide to Writing Lab Reports Standard and Higher Level Chemistry 2010-2011 Table of Contents page 1 Explanations, Clarifications, and Handy Hints page 2 - 13 IB Laboratory Evaluation Rubric page 14 - 15 Formal Lab Report Format page 16 Error Analysis Types of Experimental Errors page 17 Error Analysis: Some Key Ideas page 18 Precision and Accuracy in Measurements A Tale of Four Cylinders Assessment of Errors and Uncertainties in IB Lab Reports Explaining Terms and Concepts in Error Analysis page 19 - 20 page 21 Mathematics of Evaluating Accuracy and Precision page 26 - 27 Rejection of Data page 28 More Examples of Propagating Error page 29 - 31 page 22 - 25 Typical Instrumental Uncertainties page 32 Checklist for Writing IB Lab Reports page 33 - 34 Please read carefully and keep this handy reference for future use in writing exemplary lab reports. Page 1 IB Guide to Writing Laboratory Reports Explanations, Clarifications, and Handy Hints The nature of science is to investigate the world around you. An inquiring mind is essential to science. Experiments are designed by curious minds to gain insight into wonder-producing phenomena. Hopefully, this process of designing experiments, doing experiments, thinking about experimental results, and writing lab reports will tremendously benefit YOU! IB Chemistry is the challenge...
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