Measurement, Scaling and Sampling | Unit -4 | Business Research Methods | BBA/BBS/BBM/BIM | SG

Unit 4: Measurement, Scaling and Sampling

Variables

Variables are elements, traits, or conditions that can exist in different amounts or types and can change or vary. They are the fundamental components that researchers study to understand relationships, patterns, and influences. Customer satisfaction, training, motivation, performance are some examples of variables.

Understanding the types of variables and their roles is crucial for designing effective research studies and analyzing data accurately.

Types of Variables

1. Independent Variable: These are variables that are manipulated or controlled by the researcher to observe their effect on other variables.

o Example: Advertising Spend: Imagine a company like Coca-Cola deciding to increase their advertising spend. The advertising spend is the independent variable because it's the factor they control to see how it affects other variables.

2. Dependent Variable: These are variables that are measured to see how they are influenced by the independent variables.

o Example: Sales Revenue: Sticking with our Coca-Cola example, the sales revenue is the dependent variable because it depends on the changes made to the advertising spend.

3. Moderating Variable: These are variables that influence the strength or direction of the relationship between independent and dependent variables.

o Example: Season: Suppose Coca-Cola notices that their advertising campaign is more effective during summer than winter. The season is the moderating variable because it affects the strength of the relationship between advertising spend and sales.

Measurement

Nature of Measurement

Measurement in research refers to the process of assigning numbers or labels to various attributes, traits, or phenomena according to specific rules. This allows researchers to quantify abstract concepts, making them easier to analyze and interpret. Accurate measurement is essential for obtaining reliable and valid data that can inform decision-making and draw meaningful conclusions.

Example: Imagine you want to measure customer satisfaction for a new product. You can't directly measure satisfaction, but you can assign numbers to represent different levels of satisfaction through surveys.

Measurement Scales

Measurement scales are tools used in research to assign numbers or labels to variables in a systematic way, allowing researchers to quantify and analyze data.

1. Nominal Scale: A nominal scale categorizes data without any order or ranking. It assigns labels or names to different categories.

2. Ordinal Scale: An ordinal scale categorizes data with a meaningful order but without equal intervals between categories. It ranks data but doesn't quantify the difference between ranks.

3. Interval Scale: An interval scale not only categorizes and orders data but also has equal intervals between values. However, it lacks a true zero point.

4. Ratio Scale: A ratio scale has all the properties of an interval scale, and it also has a true zero point, allowing for the calculation of ratios.

 

Scale Construction for Attitude Measurement

When measuring attitudes, researchers often use scales to quantify abstract concepts like opinions, beliefs, or preferences. The process of scale construction involves:

1. Defining the Concept:

o Clearly define what you want to measure (e.g., customer satisfaction).

2. Generating Items:

o Create questions or statements that reflect the concept (e.g., “I am satisfied with the product quality”).

3. Selecting a Response Format:

o Choose how respondents will answer (e.g., rating scale, multiple-choice).

4. Testing and Refining:

o Pilot test the scale, analyze results, and make necessary adjustments.

Example: A company like Starbucks might create a customer satisfaction survey to measure attitudes towards their new coffee blend.

 

Scales Commonly Used in Business Research

1. Rank Order Rating Scale:

o Respondents are asked to rank items in order of preference.

o Example: A market research firm asks participants to rank five smartphone brands from most to least preferred.

2. Semantic Differential Scale:

o Uses a series of bipolar adjectives (e.g., good-bad, happy-sad) to measure attitudes.

o Example: A car manufacturer might use a semantic differential scale to assess consumer perceptions of a new car model. Respondents rate the car on a scale between pairs like “Stylish-Not Stylish” or “Reliable-Unreliable”.

3. Likert Scale:

o Consists of a series of statements that respondents indicate their level of agreement with, typically on a 5 or 7-point scale.

o Example: A retail company might use a Likert scale to measure employee satisfaction. Statements like “I feel valued at work” would be rated from “Strongly Disagree” to “Strongly Agree”.

 

Validity of Measurement

Validity refers to the extent to which a measurement instrument accurately measures what it is intended to measure. It's about the truthfulness and accuracy of the measurement.

Types of Validity:

Content Validity

Definition: Ensures that the measurement covers all relevant aspects of the concept being measured.

Example: If you’re measuring job satisfaction, your survey should include questions about various aspects like work environment, salary, and work-life balance. Omitting any significant aspect would compromise the content validity of the survey.

Construct Validity

Definition: Indicates how well a test or tool measures the theoretical construct it's supposed to measure.

Example: A personality test should accurately measure traits like extraversion, agreeableness, and openness. If the test only measures extraversion, it lacks construct validity.

Types of Construct Validity:

Convergent Validity: Assesses whether different measures of the same construct are correlated. If two different surveys measure customer satisfaction and yield similar results, they have high convergent validity.

Discriminant Validity: Assesses whether measures of different constructs are not correlated. If a survey measuring customer satisfaction is not correlated with a survey measuring employee performance, it has high discriminant validity.

Criterion-related Validity

Definition: Assesses how well one measure predicts an outcome based on another measure.

Example: A sales aptitude test should predict actual sales performance. If high test scores correlate with high sales, the test has high criterion-related validity.

Types of Criterion-related Validity:

Predictive Validity: Assesses how well a measurement tool predicts future outcomes. An entrance exam predicts students' future academic performance. If high exam scores correlate with high GPA, the exam has high predictive validity.

Concurrent Validity: Assesses how well a measurement tool correlates with an outcome measured at the same time. A new fitness assessment tool is validated by comparing it with current physical fitness levels. If the tool's scores correlate with current fitness levels, it has high concurrent validity.

By understanding and applying these types of validity, researchers can ensure that their measurement instruments provide accurate and reliable data, leading to meaningful and actionable insights. 

 

Reliability of Measurement

Reliability refers to the consistency and stability of a measurement instrument. It means that the instrument yields the same results under consistent conditions.

1. Test-Retest Reliability:

o Measures the stability of a test over time.

o Example: A leadership assessment should produce similar results when taken by the same individuals at different times.

2. Inter-rater Reliability:

o Measures the consistency of different observers or raters.

o Example: If multiple judges are scoring a business presentation, their scores should be consistent.

3. Internal Consistency:

o Measures the extent to which items on a test measure the same construct.

o Example: In a customer satisfaction survey, all questions related to satisfaction should be highly correlated.

Sources of Measurement Problems

Measurement problems can arise from various sources, affecting the validity and reliability of the data.

1. Ambiguous Questions:

o Vague or confusing questions can lead to inconsistent responses.

o Example: A survey question like “Do you enjoy work?” without specifying what aspects of work can yield different interpretations.

2. Respondent Bias:

o Biases such as social desirability or acquiescence can skew results.

o Example: Respondents might give socially acceptable answers rather than their true opinions.

3. Measurement Instrument Flaws:

o Poorly designed tools or instruments can lead to inaccurate measurements.

o Example: A poorly calibrated scale can give incorrect weight measurements.

4. Environmental Factors:

o External factors like noise, lighting, or interviewer’s demeanor can influence responses.

o Example: A noisy survey environment can distract respondents and affect their answers.

o 

Sampling 

Sampling refers to the process of selecting a subset of individuals or items from a larger population to study and make inferences about the entire population. The main goal of sampling is to gather data that accurately represents the population without having to study the entire group.  Imagine you want to study the buying behavior of customers at a large retail store. Instead of surveying every customer, you select a sample of 500 customers to represent the larger customer base. By analyzing the data from this sample, you can make inferences about the buying behavior of all customers.

Sampling Process

The sampling process involves selecting a subset of individuals from a larger population to make inferences about the entire population. Here are the main steps:

1. Define the Population:

o Identify the group you want to study. For example, if you’re researching customer satisfaction for a coffee shop, your population might be all the customers who visit the shop.

2. Determine the Sample Size:

o Decide how many individuals you need to include in your sample to get reliable results. As you might decide to survey 200 customers out of a total population of 1,000.

3. Select the Sampling Method:

o Choose a method for selecting your sample, such as probability or non-probability sampling.

4. Collect Data:

o Gather information from the selected sample. You might distribute surveys to the 200 customers and collect their responses.

5. Analyze Data:

o Analyze the collected data to draw conclusions about the entire population.

Types of Sampling 

Probability Sampling

Probability sampling methods ensure that every member of the population has a known, non-zero chance of being selected.

1. Simple Random Sampling:

o Every member of the population has an equal chance of being selected. For example, drawing names out of a hat to select participants for a survey.

2. Systematic Sampling:

o Select every nth member of the population. In a list of 1,000 customers, you might select every 10th customer for your sample.

3. Stratified Sampling:

o Divide the population into subgroups (strata) based on a characteristic, then randomly sample from each subgroup. A company wants to survey employees by department. They randomly select employees from each department to ensure representation.

4. Cluster Sampling:

o Divide the population into clusters (area), randomly select some clusters, and then sample all members within those clusters. 

Non-Probability Sampling

Non-probability sampling methods do not provide every member of the population with a known chance of being selected.

1. Convenience Sampling

o Select individuals who are easiest to reach. Surveying customers who visit a coffee shop on a particular day because they are readily available. A coffee shop owner wants quick feedback on a new menu item, so they survey customers who visit the shop that day.

2. Judgmental (Purposive) Sampling

o Select individuals based on the researcher’s judgment about who would be most useful or representative. A researcher chooses to interview industry experts to get insights into market trends. A market analyst selects experienced professionals in the tech industry to gather expert opinions on emerging trends.

3. Quota Sampling

o Ensure the sample reflects certain characteristics of the population by setting quotas. A survey aims to include a specific number of men and women in different age groups to match the population's demographics. 

4. Snowball Sampling

o Start with a small group of known individuals who meet the criteria, then ask them to refer others who also meet the criteria, expanding the sample like a rolling snowball. 

Sampling and Non-Sampling Errors

Sampling Errors

Sampling errors occur when there is a discrepancy between the sample results and the actual population characteristics. This type of error happens because only a subset of the population is studied.

1. Population Specification Error

o This error occurs when the researcher does not accurately define or understand who should be surveyed. It happens when there is a mismatch between the target population and the actual population being studied.

2. Selection Error

o This error occurs when the selection process introduces bias, resulting in a sample that is not representative of the population. It happens when some members of the population have a lower or higher chance of being selected.

3. Systematic Sampling Error

o This error arises from the method used to select the sample, leading to biased results. 

Non-Sampling Errors

Non-sampling errors occur due to factors other than the sampling process. These errors can happen at any stage of the research and can significantly impact the validity of the results.

1. Measurement Error

o Occurs when the instrument or method used to collect data is flawed.

o Example: Using poorly worded survey questions that confuse respondents.

2. Processing Error

o Happens during data processing, such as data entry or coding errors.

o Example: Entering data incorrectly into a spreadsheet.

3. Response Error

o Occurs when respondents provide inaccurate answers due to misunderstanding, bias, or dishonesty.

o Example: Respondents might overstate their income in a survey due to social desirability bias.

4. Non-response Error

o Happens when a significant portion of the selected sample does not respond.

o Example: If many customers refuse to participate in the survey, the results might not accurately represent the entire population.

Determination of Sample Size

Determining the appropriate sample size is crucial for obtaining reliable and valid results. Here are some key factors to consider:

1. Population Size

o The total number of individuals in the population being studied.

o Example: If you're studying customer satisfaction for a coffee shop, the population size is the total number of customers.

2. Margin of Error

o The amount of error you are willing to accept in your results.

o Example: A smaller margin of error (e.g., ±3%) requires a larger sample size.

3. Confidence Level

o The probability that your sample accurately reflects the population.

o Example: A 95% confidence level means that you can be 95% certain that the sample results represent the population.

4. Variability

o The degree of variation in the population.

o Example: If customer satisfaction levels are highly varied, a larger sample size is needed to capture the true average.

5. Sample Size Formula

o A common formula for determining sample size in simple random sampling.