ICT 137: 1.1. Basic Terms in Research and Statistics Part 2 of 2

Summary

This lesson provides an in-depth exploration of basic terms in research and statistics, covering essential concepts like descriptive and inferential statistics, variables, and the critical distinction between populations and samples. Key terms are defined with precision, and practical examples demonstrate how to clearly define research parameters. The lecture emphasizes understanding data types (qualitative vs. quantitative) and levels of measurement (nominal, ordinal, interval, ratio). Misconceptions surrounding these terms, such as confusion between codes and data, are clarified. The material is structured to ensure clarity for researchers and students aiming to grasp fundamental statistical principles for effective decision-making.

Takeaways

• 😀 Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to make effective decisions.
• 😀 The study of statistics is divided into two main categories: descriptive statistics and inferential statistics.
• 😀 Descriptive statistics involve organizing, summarizing, and presenting data to inform, while inferential statistics use sample data to make inferences about larger populations.
• 😀 A population refers to all possible individuals, objects, or measurements of interest, whereas a sample is a subset of that population.
• 😀 Clearly defining your target population is crucial to avoid misconceptions and ensure accurate research conclusions.
• 😀 A variable is a characteristic or attribute being studied, and it can be either qualitative or quantitative.
• 😀 Qualitative variables cannot be measured on a natural numerical scale and are classified into categories, while quantitative variables are measured on a numerical scale.
• 😀 Quantitative variables can be either discrete (countable, finite) or continuous (measured, infinite range).
• 😀 Data can be classified into four levels of measurement: nominal, ordinal, interval, and ratio, each with unique properties and uses.
• 😀 Nominal data are categories with no specific order, while ordinal data have a ranked order. Interval data have consistent differences between values but no true zero, and ratio data have a meaningful zero point, allowing for comparisons.
• 😀 The choice of the correct level of measurement is important for data analysis, as it affects how statistical operations and inferences can be made.

Q & A

What is the definition of statistics as a field of study?

Statistics is defined as the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions. It is the study of data in various forms and how to draw conclusions from it.

What is the difference between descriptive and inferential statistics?

Descriptive statistics involves methods for organizing, summarizing, and presenting data in an informative way. Inferential statistics, on the other hand, involves using sample data to make inferences or predictions about a larger population.

How do the definitions of population differ between Lind et al. and Mendel Hall & Cinsic?

Lind et al. define a population as the collection of all possible individuals, objects, or measurements of interest, while Mendel Hall & Cinsic define it as a dataset that is usually large and targeted for study. The key difference lies in the scope and focus of the term 'population'.

What is the significance of clearly defining a population in research?

Clearly defining a population is crucial because it ensures that the study accurately reflects the target group, avoiding misconceptions and potential challenges when defending the research. It is important to specify the scope, such as academic year or specific program, to avoid ambiguity.

What is the experimental unit in research?

The experimental unit refers to the source from which measurements are obtained. It can be an individual, object, or measurement, depending on the context. Mendel Hall & Cinsic use this term to refer to the subject of measurement within a population.

What is the difference between qualitative and quantitative variables?

Qualitative variables are non-numeric and can only be classified into categories (e.g., gender, religion, favorite color). Quantitative variables, however, are numeric and measured on a scale (e.g., height, weight, scores), and they represent quantities or amounts.

Can qualitative data ever be converted into quantitative data? Give an example.

Qualitative data cannot be converted into quantitative data for arithmetic purposes. However, qualitative data can be coded for analysis, such as using numbers to represent categories. For example, coding 'female' as 1 and 'male' as 2, but this does not allow for arithmetic calculations.

What distinguishes discrete data from continuous data?

Discrete data can only take specific values and is often obtained by counting (e.g., number of students, shoe size). Continuous data, on the other hand, can take any value within a given range and is obtained through measurement (e.g., height, weight, time).

What are the different levels of data measurement?

The four levels of data measurement are nominal, ordinal, interval, and ratio. Nominal data consists of categories with no inherent order, ordinal data involves ranked categories, interval data has constant differences between values but lacks a meaningful zero, and ratio data has both a meaningful zero and constant differences.

Why is the Likert scale considered ordinal rather than interval data?

The Likert scale is considered ordinal because its categories (e.g., 'strongly agree', 'agree') only represent a ranking order, not the precise difference between them. Although numerical codes are used, these numbers are merely placeholders and do not indicate equal intervals between the categories.