Crutch Words To Avoid For Clearer Communication In Writing

/in /by


Crutch words are those little expressions that sneak into our speech and writing without us realizing it. You know, words or phrases like “just,” “basically,” “um,” and “you know.” They fill space, give us time to think, and sometimes soften our tone, but too many of them can weaken our message and make us sound uncertain. Whether in casual dialogue or formal prose, these words are often used as a cushion when we’re unsure of what to say next. The more we rely on them, the more they can start negatively impacting the flow and clarity of our communication.

I’ve noticed that once people become aware of crutch words, they start hearing them everywhere , in speeches, emails, and even professional writing. It’s not that using them is always bad, but knowing when they add nothing of value helps you tighten your language and boost confidence. By the end of this article, you’ll understand what crutch words are, why they appear, and how to reduce them naturally without sounding robotic.

Key Takeaways

  1. The article begins by emphasizing that crutch words like “just,” “um,” and “you know” are common habits that can weaken communication, and recognizing their presence is the first step toward clearer expression.
  2. It explains that people often rely on these fillers out of nervousness, habit, or a desire to sound polite, but learning to pause instead of filling silence can make speech and writing sound more confident and intentional.
  3. Through examples such as “like,” “literally,” and “basically,” the piece highlights how common crutch words appear in both everyday speech and writing and why being mindful of their frequency helps maintain clarity and focus.
  4. The article provides clear steps to eliminate crutch words—record yourself, identify patterns, replace fillers with pauses, focus on your next idea, and revise sentences to delete unnecessary words while retaining a natural tone.
  5. It concludes that while crutch words are part of normal communication, becoming aware of them, practicing intentional silence, and editing with care can significantly strengthen both speech and prose, improving overall confidence and precision.

What Are Crutch Words?

Crutch words are filler expressions we insert into speech or writing when we need a moment to collect our thoughts. They often sound harmless , small bits like “literally,” “so,” or “well.” But when overused, they distract from the message. Think of them as verbal habits that serve as a pause or a bridge between ideas.

People use crutch words for different reasons. Sometimes it’s out of habit; other times it’s a way to sound polite or less direct. In writing, they can make a sentence feel conversational but may also weaken the tone. In speech, they can make us seem hesitant or less confident. If you’ve ever found yourself saying nothing of real meaning while speaking, chances are, a few of these words were involved.

Why We Use Crutch Words

It’s easy to overuse words like “um” or “you know” when we’re nervous, distracted, or trying to sound casual. Our brains move faster than our mouths, and crutch words act as a small pause , a way to catch up. This behavior is deeply human; it’s how we manage the silence that makes us uncomfortable.

There’s also a psychological reason behind it. Many speakers tend to fill the silence because they fear it signals uncertainty. However, silence can actually demonstrate control and thoughtfulness. In fact, public speaking organizations like Toastmasters International encourage learning how to replace fillers with intentional pauses. It’s a habit that takes awareness and practice to change, but once you do, your confidence and tone naturally improve.

Common Crutch Words

Before we break them down, let’s first acknowledge what crutch words do. They’re words we often use as a cushion , sometimes it’s a filler word, sometimes it’s a redundant expression that softens what we’re saying. Below, we’ll go through some of the most common examples in detail and talk about why they appear so frequently in the English language.

1. “Um” and “Ah”

These are perhaps the most recognized filler words. They usually appear when a speaker needs a moment to think. While harmless in small doses, too many of them can become a distraction. Replacing them with a short pause makes your sentences sound more deliberate and thoughtful.

2. “Like” and “You Know”

These informal words are common in casual speech, especially among younger speakers. Phrases like “I was, like, really tired” or “You know what I mean?” can make sentences feel cluttered. They’re not wrong, but they can weaken your message if used excessively. If you pay attention, you’ll notice how often people use them without realizing.

3. “Just” and “Basically”

Writers often use “just” to soften statements, such as “I just wanted to ask…” It sounds polite, but it can make a message feel tentative. “Basically” works as an unnecessary adverb, often adding no new information. Deleting them makes a sentence stronger and clearer.

4. “Literally” and “Really”

‘Literally’ has become one of the most overused words in modern English. People often use it to exaggerate rather than describe something factual. Similarly, “really” serves as emphasis but can lose its effect when repeated. In both speech and prose, trimming these words improves clarity.

5. “Well” and “So”

These two words often start a sentence. While they can set a conversational tone, they don’t always add meaning. It’s fine to use them for rhythm, but be mindful of how often they appear , especially in formal writing or presentations.

How Crutch Words Affect Communication

Crutch words can influence how others perceive you. Too many fillers make it harder for listeners to focus on the main idea. They can also give the impression that you’re unsure or not fully prepared. In writing, they take up space and can make sentences longer than necessary, which may affect the rhythm of your prose.

But not all crutch words are bad. Used sparingly, they can help soften the tone, making speech sound more natural. The key lies in balance. When you’re aware of why you use crutch words, it becomes easier to control them instead of letting them control you.

You might want to check out this detailed post on How to Avoid Using Filler Words to learn more about practical ways to reduce these verbal habits.

Recognizing Your Own Crutch Words

The first step in changing any habit is awareness. Try recording yourself while talking or reading your writing aloud. Notice the words or phrases you repeat. Once you identify patterns, you’ll know you’ve found your crutch.

Here are a few quick ways to track them:

  • Highlight repeated words in your manuscript.
  • Ask someone to point out fillers during a conversation.
  • Practice short pauses instead of using a filler word.

If you pay attention to your language, you’ll quickly see which words show up too often. The goal isn’t to delete every crutch word but to use them with intention.

How to Eliminate Crutch Words

Eliminating crutch words doesn’t mean speaking like a robot. It means becoming comfortable with silence and learning how to pause with purpose. Here’s how you can start:

  • Replace filler words with a deliberate pause.
  • Focus on the next idea before speaking.
  • Practice with a friend or record yourself.
  • Read your writing out loud to spot redundancy.

Toastmasters International recommends using pauses to project confidence. The silence gives listeners time to absorb your words while giving you time to think. In writing, revise each sentence and ask if every word adds value. Delete the ones that don’t.

A helpful resource for spotting overused terms in prose is this guide on Signal Words, which shows how transition phrases can replace unnecessary fillers.

Improving Your Writing and Speech

When editing a piece of writing, scan for words that repeat or feel redundant. Sometimes these are adverbs or phrases that say nothing new. Removing them sharpens the tone. Reading the text aloud helps identify awkward spots where crutch words weaken the flow.

For example:

  • “I just think we should maybe start over.” → “We should start over.”
  • “Basically, it’s like a better version.” → “It’s a better version.”

Writers often overuse these fillers because they’re trying to sound conversational. But even natural-sounding dialogue benefits from precision. If you’re interested in exploring how language evolves, you might enjoy reading How Many Words Did Shakespeare Invent, which shows how intentional word choice can shape English over time.

Conclusion

Crutch Words are part of how we speak and write; they make us human. The problem comes when we overuse them to the point where they distract or dilute our message. The good news is that awareness changes everything. By slowing down, paying attention, and revising with care, you can use words more purposefully. Whether you’re a speaker, student, or writer, learning to remove these unnecessary fillers helps you express ideas with more confidence and clarity. If you’d like to explore more about modern word habits, you might find Young Words for Old People a fun and insightful read.

Crutch Words FAQs

What’s the difference between a crutch word and a filler word?

A crutch word is any term or phrase used to fill silence or buy time to think. A filler word does the same thing but often serves no grammatical purpose. Both can weaken a sentence if used too often.

Do crutch words always make you sound less confident?

Not always. Using them occasionally can make speech feel natural. But frequent use can make you sound unsure or distracted.

Can removing crutch words make writing sound unnatural?

If you remove too many, yes. The goal is to keep your tone genuine, not mechanical. Focus on balance , keep what adds rhythm, delete what adds nothing.

How long does it take to stop using crutch words?

It varies. With awareness and practice, most people notice improvement in a few weeks. Paying attention to your tone and practicing pauses makes a big difference.



academhelper.com academhelper.com

 
"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"
ORDER NOW

How To Use Shall And Will: A Comprehensive Grammar Guide

/in /by


Many people learning English wonder how to use shall and will correctly. These two words are small but carry a lot of meaning in the English language, especially when forming the future tense. They’ve been part of the English grammar system for centuries, but their usage has shifted depending on whether you’re in the United Kingdom or the United States. If you’ve ever asked yourself why we say I will go but sometimes see I shall go, you’re not alone. In this guide, we’ll explore their differences, how they function as auxiliary verbs, and when each is appropriate in spoken English and writing. By the end, you’ll feel confident using them naturally in any sentence or context.

A Brief History of “Shall” and “Will”

To appreciate how these words are used today, it helps to look at where they came from. “Shall” is the older of the two, tracing its roots back to Old English, where it expressed obligation or determination. “Will” emerged later from a word meaning “to want” or “to wish.” Originally, “shall” was used to state that something must happen, while “will” was used to express intent or desire.

In modern English, however, this line has blurred. Over time, people started to use “will” more frequently, especially in American English, while “shall” remained more common in British English. Even dictionaries and grammar guides note that “shall” sounds slightly archaic, though it still appears in formal statements, contracts, and law.

This evolution reflects how the English language adapts to modern speech patterns. The shift from “shall” to “will” shows how native speakers simplify their communication without losing meaning.

How to Use Shall and Will

Before diving deeper, it’s helpful to get an overview of how to use shall and will. Both are modal verbs used to express future actions or intentions. The good news is that the rules are quite simple once you get the hang of them. We shall go through them in detail below.

1. General Rule for Shall and Will

Traditionally, shall is used with the first person pronouns (I and we), while will is used with the second and third person (you, he, she, it, they). For example:

  • I shall call you tomorrow.
  • We shall visit Paris next summer.
  • He will arrive later tonight.
  • They will help us with the project.

However, when emphasis or determination is intended, this pattern is reversed:

  • I will not give up!
  • You shall pay for this!

So, the general rule is simple, but context can flip the tone. The difference between “shall” and “will” often lies in how strong or formal the speaker wants the sentence to sound.

2. Using “Shall” in Formal English

In standard British and US English, “shall” still appears in legal writing, contracts, and formal propositions about the future. For example:

  • The tenant shall pay rent on the first day of each month.
  • The committee shall decide by majority vote.

Here, “shall” indicates obligation, almost like saying “something must happen.” It’s also used in polite or formal statements, such as:

  • Shall I open the window?
  • Shall we begin the meeting?

These uses show that “shall” can sound polite or official, making it a preferred choice in formal English grammar.

3. Using “Will” in Everyday English

In spoken English, “will” dominates. It’s simpler, natural, and used for most situations that involve the future tense. You’ll hear it everywhere:

  • I’ll see you tomorrow.
  • He’ll call once he’s home.
  • They’ll start the movie soon.

When you say “I’ll” or “he’ll,” that’s a contraction of “I will” or “he will.” Contractions like these are very common in casual conversation because they make speech smoother.

Compared to “shall,” “will” is easier to use and more flexible. Whether you’re talking about plans, promises, or negative sentences about the future, “will” fits almost anywhere.

3. How “Shall” and “Will” Express Future Time

Both words form the future tense when used as auxiliary verbs before the base form of the main verb:

  • I shall ask her tomorrow.
  • We will finish it soon.

While both express future actions, “will” often conveys intention, and “shall” implies commitment or obligation. In uses of English verb forms, this distinction helps clarify your context and tone.

4. Affirmative and Negative Sentences with Shall and Will

You can use both in affirmative and negative sentences. For example:

  • I shall go to the store tomorrow.
  • I shan’t go to the store tomorrow. (shan’t = shall not)
  • He will go if it stops raining.
  • He won’t go if it doesn’t.

Notice how negative sentences about the future use shan’t or won’t as contractions. “I shan’t” sounds archaic or British, while “I won’t” is preferred in modern English.

5. When to Use “Shall” for Offers, Suggestions, and Promises

“Shall” isn’t only about obligation, it’s also useful when you make an offer or suggestion:

  • Shall we go for coffee?
  • Shall I help you with that?

It can also express determination or promise:

  • You shall get your reward.

This use highlights how “shall” can convey a polite tone or a sense of duty.

6. Examples and Common Mistakes

Many learners confuse when to use shall versus “will.” Here are some practical examples:

✅ I shall call the doctor tomorrow. (Formal tone)
✅ I will call the doctor tomorrow. (Normal, everyday tone)
✅ Shall we start the class? (Polite question)
❌ Will we start the class? (Incorrect if meant as a polite offer)

To improve your fluency, avoid overusing “shall” in spoken English, it can sound old-fashioned unless you’re making a formal statement or writing for law or official documents.

Difference Between Shall and Will

The difference between shall and will lies in tone and tradition. “Will” is the dominant choice for expressing future time in both American English and modern English, while “shall” adds formality or politeness.

In British English, “shall” remains part of standard British speech, especially in offers or suggestions (Shall we?). But in the United States, “will” is preferred in nearly all contexts.

Sometimes, both words are used interchangeably without changing meaning. For instance:

  • I shall be there at six.
  • I will be there at six.

Both are correct, but “shall” sounds more formal or British.

If you’d like to learn how small word choices affect tone in writing, check out this helpful guide on crutch words that explains how to keep your sentences clear and purposeful.

Common Contractions and Spoken English

In everyday conversation, “shall” and “will” often appear in shortened forms. These contractions make speech sound natural and fluent. Examples include:

  • I’ll = I will
  • He’ll = He will
  • We’ll = We will
  • I shan’t = I shall not

While I’ll and he’ll are common, shan’t is rarely heard outside the United Kingdom. Many native speakers never use “shan’t,” even though it’s grammatically correct.

When writing formally, say, in a report or an excuse letter, avoid contractions altogether. 

Shall and Will in Modern English

Today, shall is only used in limited contexts. You’ll find it mainly in:

  • Legal and policy documents (The company shall provide safety training.)
  • Formal writing (Shall we proceed?)
  • Religious or poetic texts (Thou shalt not kill.)

Most of the time, people simply use “will.” It’s the go-to word in modern English for all person pronouns, including second and third person.

Still, knowing how to use “shall” correctly helps when you’re reading formal statements or writing in a law context. It also keeps your grasp of English modal auxiliary verbs well-rounded.

Formal and Legal Usage of Shall and Will

In law, “shall” often expresses duty or obligation. For example:

  • The employee shall report any conflict of interest immediately.

In this case, “shall” means the person must do it. This isn’t optional, it’s mandatory. In contrast, “will” in legal documents might simply describe future time reference, not a requirement.

That’s why dictionaries of English define “shall” as being used to express obligation, while “will” is used to predict actions or intentions.

You’ll also find shall in formal rules or procedural writing. For instance, if you’re preparing slides and want to use precise language, check out the guide on PowerPoint rules for presentations for structured communication tips.

Common Learner Challenges

Learners often get confused about how shall sounds compared to “will.” Here are some common problems:

  • Using “shall” in casual talk when “will” sounds better.
  • Forgetting that “shall” can sound archaic in American English.
  • Mixing affirmative and negative sentences incorrectly (e.g., I won’t shall go).

To avoid these mistakes:

  • Remember that “shall” works better for formal or polite questions.
  • Use “will” for almost everything else in spoken English.
  • Listen to native speakers and note which one they prefer.

If you’re curious about tone when writing about personal or sensitive subjects, here’s a great related read on How to Write About Disability, it covers how language choice affects clarity and empathy.

Tips to Learn English Usage Naturally

Here’s how to make the use of shall and “will” second nature:

  1. Read British English and American English materials to spot differences.
  2. Practice writing short sentences using both words.
  3. Record yourself to hear how shall sounds in speech.
  4. Refer to a dictionary to confirm the form used in examples.
  5. Practice with affirmative and negative sentences to get comfortable.

If you’re learning the Tamil language or Turkish language, you’ll notice that grammatical tense markers work differently, but the idea of predicting the future remains universal.

Conclusion

Learning how to use shall and will is easier than it seems. Both words help express future actions, but “will” dominates in modern English while “shall” adds formality or obligation. Once you learn the difference, you’ll know exactly when each fits the context, whether in speech, law, or polite offers. Keep practicing, and you’ll find that using these modal verbs becomes as natural as speaking itself.

FAQs

1. What is the main difference between “shall” and “will”?
“Shall” sounds formal and often implies obligation or politeness, while “will” is more common for everyday speech and general future statements.

2. Do people still use “shall” today?
Yes, but mostly in the United Kingdom, legal writing, and formal contexts. In casual talk, people prefer “will.”

3. How do you know when to use “shall” instead of “will”?
Use “shall” for offers, suggestions, or when something is required by rule or law. Use “will” in all other cases.

4. Is “shall” used in American English?
Rarely. In American English, “will” is almost always used, even in the first person.



academhelper.com academhelper.com

 
"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"
ORDER NOW

What Is Data Cleansing in Research? Definition, Process & Examples

/in /by


What Is Data Cleansing In Research?

Data cleansing, also known as data cleaning, refers to the process of detecting, correcting, or removing inaccurate, incomplete, or irrelevant information from a dataset.

It means making sure your research data is accurate, consistent, and usable. The data cleansing definition centres on improving the overall quality of data by eliminating errors and inconsistencies that could affect the outcome of a study.

Researchers use data cleansing to prepare their datasets for analysis, which helps improve the precision and reliability of their findings.

Why Is Data Cleansing Important In Research?

Data directly impacts the credibility of research findings. Errors, missing values, or duplicate entries can distort results, leading to inaccurate conclusions or even invalid studies.

For example: if a dataset includes repeated survey responses or incorrectly recorded values, the statistical analysis could produce misleading trends or patterns..

Clean data helps researchers maintain the integrity of their work. Below are the key benefits of data cleansing in research:

  • Improved accuracy of results: By removing incorrect or inconsistent data, researchers can produce more reliable and valid outcomes.
  • Better data consistency: Standardising data formats ensures that variables are comparable and analysis is smooth.
  • Better decision-making: Clean data provides a solid foundation for drawing insights, supporting hypotheses, and making informed research-based decisions.

Common Data Quality Issues In Research

Here are some of the most common problems:

  • Often occurs when participants skip questions in surveys or sensors fail to record data. Missing values can reduce the statistical power of the analysis and bias the results.
  • Repeated records can inflate sample sizes or distort averages, leading to inaccurate outcomes.
  • Variations in date formats, currencies, measurement units, or capitalisation make data difficult to merge or compare accurately.
  • Unusual values that deviate from the rest of the dataset may indicate recording errors or exceptional cases that require investigation.
  • Mistyped numbers, misclassified categories, or wrong labels can significantly affect data integrity.

Step-By-Step Guide To The Data Cleaning Process

Below is a detailed, step-by-step guide on how to clean data systematically for research purposes.

Step 1. Data Inspection

The first step in any data cleaning process is to inspect your dataset thoroughly. This involves scanning for missing values, duplicates, inconsistencies, or outliers that could distort your results. 

Researchers typically use descriptive statistics (mean, median, range) and data visualisation tools (such as histograms or box plots) to identify unusual trends or anomalies. 

For example, if a participant’s age is listed as 250, that’s an obvious error that needs correction. Data inspection helps you understand the scope of your data quality issues before proceeding to deeper cleaning steps.

Step 2. Data Standardisation

Once errors are identified, the next step is data standardisation, which ensures that all data follows a consistent structure and format. This means unifying things like date formats (e.g., converting “10/19/25” and “19-Oct-2025” into one format), measurement units (e.g., converting all heights to centimetres), capitalisation (e.g., “Male” and “male” should be standardised), and categorical values. 

Standardisation makes data integration and analysis easier, especially when merging datasets from multiple sources. In research, standardised data prevents confusion and promotes accuracy when applying statistical models.

Step 3. Data Validation

Data validation ensures that your dataset accurately represents the information it is supposed to capture. This step involves cross-checking your data with original sources, credible databases, or reference materials. 

For instance, if your dataset contains regional population data, you can validate it against official government statistics. Validation can also include logical checks, such as ensuring numerical values fall within expected ranges or that survey responses match predefined options. 

The goal is to confirm that your dataset is not only clean but also credible and verifiable.

Step 4. Handling Missing Data

Missing data is one of the most common data quality issues in research. How you handle it can significantly affect your analysis outcomes. There are several strategies:

Method Description
Deletion If the missing data is minimal and random, you may remove incomplete records.
Imputation Estimate missing values using statistical techniques such as mean substitution, regression, or advanced methods like multiple imputation.
Leaving it Blank (When Appropriate) In some qualitative or categorical datasets, it might be acceptable to leave missing values unfilled if they don’t impact the analysis.

Step 5. Removing Duplicates

Duplicate records can appear when data is entered multiple times or merged from different sources. These duplicates can inflate your sample size and distort analysis results. In this step, researchers use automated data-cleaning tools (like Excel’s “Remove Duplicates” function, Python’s Pandas library, or R scripts) to identify and eliminate redundant entries. 

It is important to review each duplicate before deletion to ensure you don’t lose unique or relevant information. This step ensures data integrity and prevents skewed findings.

Step 6. Verification

After cleaning, the final step is verification, a quality check to ensure that all errors and inconsistencies have been properly addressed. Researchers re-run descriptive statistics, visualisations, or integrity checks to confirm improvements in data accuracy and consistency. 

Verification also includes documenting every change made during the data cleaning process. This documentation helps maintain transparency, allowing others to understand how your dataset was refined and ensuring your work remains reproducible.

Researchers can choose between manual and automated data cleaning methods depending on the complexity and size of their datasets.

Method Description & Key Characteristics
Manual Data Cleaning Involves manually reviewing datasets to identify and correct errors. It is suitable for smaller datasets where human judgment (e.g., for qualitative data or open-ended responses) is essential. However, it is time-consuming and prone to human error on large datasets.
Automated Data Cleaning Uses algorithms and scripts to detect and fix issues quickly and consistently. It is ideal for large or complex datasets, ensuring faster and more accurate results. Tools and software automate repetitive tasks like removing duplicates and standardizing formats.

Common Tools Used for Data Cleansing

Microsoft Excel Great for basic cleaning, removing duplicates, filtering, sorting, and using formulas to identify inconsistencies.
OpenRefine A powerful open-source tool designed for cleaning messy data and transforming formats efficiently.
Python (Pandas) Widely used for advanced data manipulation and cleaning using code, ideal for quantitative research.
R Offers statistical and data management functions for data validation and cleaning.
SPSS and SAS Commonly used in academic and professional research to handle missing data, outliers, and inconsistencies with built-in cleaning functions.

Modern AI-Based Data Cleaning Tools

With the rise of artificial intelligence, several modern tools can now automatically detect and fix data issues using machine learning. Tools like Trifacta Wrangler, Talend Data Preparation, and IBM Watson Studio use AI to suggest cleaning actions, identify patterns, and improve data accuracy with minimal manual intervention. 

Examples Of Data Cleansing In Research

Below are some real-life data cleansing applications in research:

Example 1: Cleaning Survey Data

A researcher conducting an online survey may find multiple submissions from the same respondent or typographical errors in responses. The cleaning process would involve removing duplicate entries, fixing spelling mistakes, and ensuring all responses align with the defined variables.

Example 2: Handling Missing Values in Experimental Datasets

In an experiment measuring participant performance, some entries might be missing due to technical issues. Researchers can handle this by imputing the missing values using the mean or median of similar participants or by excluding incomplete cases if they’re minimal.

Example 3: Standardising Demographic Data

When collecting demographic information, data like gender or age might appear in different formats (e.g., “M” vs. “Male” or “25 yrs” vs. “25”). The researcher must standardise these values to maintain consistency, ensuring the data is compatible across different analyses and tools.

Best Practices For Effective Data Cleansing

Here are some key data cleaning best practices that help improve data quality management:

  • Always record the transformations, corrections, and assumptions made during data cleaning. This transparency ensures reproducibility and accountability in research.
  • Leverage data cleaning software and scripts to handle repetitive tasks efficiently and reduce the chance of manual mistakes.
  • Data should be periodically reviewed to identify recurring issues, outdated values, or inconsistencies before they accumulate.
  • Having more than one researcher review the dataset can help detect overlooked errors and improve objectivity.

Frequently Asked Questions



academhelper.com academhelper.com

 
"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"
ORDER NOW

Descriptive Statistics Explained | Types, Formulas, and Real-Life Examples

/in /by


What Are Descriptive Statistics

Descriptive statistics are a set of statistical tools used to describe, summarise, and present data in a meaningful way. Rather than drawing conclusions beyond the data itself, they focus on showing what the data reveals about a particular group or situation. 

In simple terms, descriptive statistics help transform raw data into clear insights through numbers, tables, and graphs. They 

  • Simplify complex information
  • Makes it easier to understand patterns and averages within a dataset
  • Serves as the first step in data analysis 
  • Allows researchers to summarise findings before moving on to deeper inferential techniques.

Example Of Descriptive Statistics In Research

Imagine you surveyed 100 students about their study hours per week. Using descriptive statistics, you could calculate the average (mean) number of study hours, find the most common (mode) value, and identify the spread (standard deviation) of the data. This summary gives a clear overview of students’ study habits without making predictions, which is where inferential statistics would come in.

Types Of Descriptive Statistics

Descriptive statistics are generally divided into four main types:

  1. Measures of central tendency
  2. Measures of dispersion
  3. Measures of frequency and distribution
  4. Measures of position

A. Measures of Central Tendency

These measures identify the centre or average point of a dataset. They summarise where most data points cluster. The three main types are:

  • Mean: The arithmetic average of all values.

Mean Example: If students scored 70, 75, and 80, the mean score is (70 + 75 + 80) ÷ 3 = 75.

  • Median: The middle value when data is arranged in order.

Median Example: For scores 60, 70, 80, the median is 70.

  • Mode: The value that occurs most frequently.

Mode Example: If scores are 65, 70, 70, 80, the mode is 70.

B. Measures of Dispersion (Variability)

While central tendency tells us the “middle,” measures of dispersion explain how spread out the data is.

  • Range: The difference between the highest and lowest values.

Example: If the highest mark is 90 and the lowest is 60, the range is 30.

  • Variance: Shows how much each value differs from the mean.
  • Standard Deviation: The most common measure of variability, showing the average distance of each data point from the mean. A higher standard deviation indicates that values are more spread out, while a lower one means they are closer to the mean.

C. Measures of Frequency and Distribution

These describe how often each value or category appears in a dataset. Researchers use frequency tables, bar charts, histograms, and pie charts to visualise this distribution.

Example: A frequency table showing how many students fall into different grade ranges (A, B, C, D) helps identify performance trends quickly.

D. Measures of Position

These indicate where a particular value lies within a dataset.

  • Percentiles: Show the relative standing of a value. For example, scoring in the 90th percentile means performing better than 90% of participants.
  • Quartiles: Divide data into four equal parts, helping detect data spread and outliers.

Ranks: Assign numerical positions to values, often used in competitive analysis or performance ranking.

Descriptive Statistics Formulas And Examples

Below are the basic formulas for mean, median, mode, variance, and standard deviation, with simple numeric examples and step-by-step calculations.

1. Mean (Arithmetic Average)

Formula (population or sample mean):

Example dataset: 4, 8, 6, 5, 3

Step-by-step calculation

  1. Sum the values: 4 + 8 + 6 + 5 + 3 = 26
  2. Count the values: n = 5
  3. Divide: x = 26 ÷ 5 = 5.2

Result: Mean = 5.2

2. Median (Middle Value)

Procedure: Sort values and pick the middle. If n is even, median = average of the two middle values.

Example A (odd n): 4, 8, 6, 5, 3

  1. Sort: 3, 4, 5, 6, 8
  2. Middle value (3rd of 5) = 5

Example B (even n): 3, 4, 5, 6

  1. Sort: 3, 4, 5, 6 (already sorted)
  2. Middle two values = 4 and 5 → median = (4 + 5) ÷ 2 = 4.5

3. Mode (Most Frequent Value)

The value(s) that occur most often are called the Mode. A dataset may have one mode, multiple modes, or no mode.

Example: 2, 3, 3, 5, 7 → mode = 3 (appears twice)
Example (no mode): 4, 8, 6, 5, 3 → no value repeats → no mode

4. Variance (Average Squared Deviation)

There are two common versions:

  • Population variance (σ²)

Use the population formula when you have the entire population. Use a sample formula when your data is a sample from a larger population.

Example dataset (same as earlier): 4, 8, 6, 5, 3; mean x = 5.2

Step-by-step calculation of squared deviations

  1. Compute deviations from the mean:
    • 4 − 5.2 = −1.2 → squared = 1.44
    • 8 − 5.2 = 2.8 → squared = 7.847
    • 6 − 5.2 = 0.8 → squared = 0.640
    • 5 − 5.2 = −0.2 → squared = 0.040
    • 3 − 5.2 = −2.2 → squared = 4.844
  2. Sum squared deviations: 1.44 + 7.84 + 0.64 + 0.04 + 4.84 = 14.80
  3. Population variance (divide by N = 5): σ^2 = 14.80 ÷ 5 = 2.96
  4. Sample variance (divide by n − 1 = 4): s^2 = 14.80 ÷ 4 = 3.70

Results: Population variance = 2.96; Sample variance = 3.70

5. Standard Deviation (Square Root Of Variance)

SD Formulas:

Using the variance results above

  • Population standard deviation: σ = 2.96 ≈ 1.72
  • Sample standard deviation: s = 3.70 ≈ 1.92

Interpretation:

Standard deviation gives the average distance of observations from the mean. Smaller values indicate data points are closer to the mean, while larger values indicate they are more spread out.

Quick Reference

  1. Mean: X = Xn
  2. Median: Middle value after sorting (or average of middle two if even n)
  3. Mode: Most frequent value(s)
  4. Population variance: 2=(x –)N
  5. Sample variance: s2 = (x –x)n -1
  6. Standard deviation: = 2

Short Worked Example Summary (Dataset: 4, 8, 6, 5, 3)

  1. Mean = 5.2
  2. Median = 5
  3. Mode = none (no repeats)
  4. Population variance = 2.96 → Population SD ≈ 1.72

Here are some of the most widely used descriptive statistics tools that help summarise and interpret data efficiently.

1. Microsoft Excel

Descriptive statistics in Excel are simple to perform using built-in functions like AVERAGE, MEDIAN, MODE, STDEV, and VAR.

Researchers can also use the “Data Analysis Toolpak” to automatically generate detailed statistical summaries, including mean, standard deviation, and variance.

Excel’s charts and graphs, like bar charts and histograms, make it easy to visualise trends and compare data points.

2. SPSS (Statistical Package for the Social Sciences)

SPSS is a powerful statistical software widely used in academic and professional research. It allows users to compute descriptive statistics with just a few clicks, generating clear tables for mean, median, mode, and standard deviation.

It is handy for handling large datasets and creating detailed statistical reports that include both descriptive and inferential outputs.

3. R and Python

Both R and Python are advanced programming languages popular in data science and academic research.

They allow researchers to automate descriptive statistics, visualise data using packages like ggplot2 (R) or matplotlib (Python), and perform custom analyses.

For example, you can calculate means and standard deviations across thousands of data points in seconds while producing professional-quality visualisations.

4. Google Sheets or Online Calculators

For quick analysis, Google Sheets and free online descriptive statistics calculators offer accessible options.

Google Sheets supports basic statistical functions and simple charts, making it ideal for students and small-scale projects.

Online tools like GraphPad, CalculatorSoup, or Social Science Statistics are convenient for quick calculations when software access is limited.

Descriptive Vs Inferential Statistics

While descriptive statistics summarise existing data, inferential statistics go a step further by drawing conclusions about a larger population based on a sample. 

Comparison Table

Comparison Point Descriptive Statistics Inferential Statistics
Purpose Summarizes and organizes data collected from a sample or population. Makes predictions or generalizations about a larger population based on a sample.
Focus Describes what is known and visible in the dataset. Infers what is unknown and extends findings beyond the data collected.
Techniques Mean, median, mode, range, variance, standard deviation. t-tests, ANOVA, regression, correlation, and $chi^2$ (Chi-Square) tests.
Data Used The entire dataset or the sample itself. A sample is used to represent and make conclusions about a larger population.
Visuals Charts, tables, and graphs (histograms, box plots) to display data distribution. Confidence intervals, p-values, and hypothesis testing results.
Example Output “The average height of 100 students is 170 cm.” “We are 95% confident the average height of all students is between 168 and 172 cm.”

When to use descriptive vs inferential statistics?

  • Use descriptive statistics when you want to present and summarise data you already have (e.g., survey results, exam scores).
  • Use inferential statistics when you aim to predict or test hypotheses about a larger population based on sample data.

Examples

  • Descriptive example: “The average age of respondents was 28 years.”
  • Inferential example: “There is a significant difference between the average ages of male and female respondents.”

Frequently Asked Questions



academhelper.com academhelper.com

 
"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"
ORDER NOW

Variables – Essays UK

/in /by


Importance Of Variables In Research

Here is why variables are important in research. 

  • Variables form the core of every research study and guide the direction of data collection, analysis, and interpretation.
  • Variables help researchers create clear and measurable hypotheses. For example, Increased screen time leads to reduced sleep quality. Here, screen time and sleep quality are variables.
  • By manipulating or observing one variable (independent) and measuring another (dependent), researchers can test relationships. For instance, studying how a new teaching method (independent variable) affects student performance (dependent variable).
  • Clearly defined variables help produce consistent, repeatable, and accurate results. They reduce confusion and improve the credibility of findings.
  • Variables determine what type of data will be collected and what statistical tests can be used. Different types of variables (quantitative, categorical, continuous) influence how results are interpreted.

Main Types Of Variables In Research 

Below is a breakdown of the primary variable types:

Independent Variables

The independent variable is the factor that researchers deliberately change or manipulate to observe its effect on another variable. It is considered the cause in a cause-and-effect relationship.

Examples Of Independent Variables

  • In education research, a study might explore the impact of hours of study on students’ academic performance. 
  • In medical studies, researchers may investigate the effect of drug dosage on patient recovery rates. 

In marketing research, a project could analyse how advertising spend influences brand sales performance.

How To Identify Independent Variables

What factor is being changed or controlled by the researcher? The independent variable is always the variable that influences or predicts a change in another variable.

Dependent Variables

The dependent variable is the outcome or result that researchers observe and measure. It shows the effect of the change in the independent variable.

Examples Of Dependent Variables

  • In the study on the impact of hours of study on students’ academic performance, academic performance (measured through test scores) is the dependent variable.
  • In the research analysing the effect of drug dosage on patient recovery rates, the recovery rate is the dependent variable.
  • In the project exploring how advertising spend influences brand sales performance, sales performance is the dependent variable.

Relationship Between Independent & Dependent Variables

The dependent variable depends on the independent variable. For example, if the study examines how diet (independent variable) influences cholesterol levels (dependent variable), changes in diet will likely impact cholesterol readings.

Controlled Variables

Controlled variables are factors kept constant throughout the study to ensure that only the independent variable affects the results. They help maintain fairness and accuracy in experiments. Moreover, 

  • They eliminate alternative explanations for results.
  • They increase the reliability and validity of the research.

Examples Of Controlled Variables

  • In a plant growth study, the same type of plant, the same soil, and the same amount of sunlight were used.
  • In a classroom experiment, the same teacher, class duration, and curriculum were used for all groups.

Extraneous and Confounding Variables

Extraneous variables are any external factors that might influence the dependent variable but are not intentionally studied.

Confounding variables are a specific type of extraneous variable that changes systematically with the independent variable, making it difficult to determine which variable caused the effect.

Both can distort results and lead to false conclusions. Additionally, they reduce the internal validity of an experiment if not appropriately controlled. You can manage these variables through the following:

  • Use randomisation to distribute unknown factors evenly.
  • Apply control groups to compare outcomes.
  • Standardise procedures and environments.

Examples 

  • In the education study, an extraneous variable could be students’ motivation levels, as it might unintentionally affect academic performance. If highly motivated students also tend to study more, motivation becomes a confounding variable.
  • In medical research, stress levels could be a confounding variable if patients with higher stress recover more slowly, regardless of dosage.
  • In the marketing project, seasonal demand might act as a confounding variable, since higher sales could be caused by seasonal trends rather than increased advertising.

Other Common Types Of Variables In Research

Now we will discuss some other types of variables that are important in research.

Moderator Variables

A moderator variable affects the strength or direction of the relationship between an independent and a dependent variable. It does not cause the relationship but changes how strong or weak it appears.

Moderator Variables Examples

  • In a study examining the relationship between work stress and job satisfaction, social support can be a moderator variable.
  • In the effect of advertising frequency on customer engagement, age might moderate the relationship. 

Mediator Variables

A mediator variable explains how or why an independent variable influences a dependent variable. It serves as a middle link that clarifies the process of the relationship.

Mediator Variables Examples

  • In a study on education level and income, career opportunities may act as a mediator variable.
  • In research exploring exercise and weight loss, calorie burn may mediate the relationship.

Categorical Variables Vs Continuous Variables

Categorical Variables Continuous Variables
These variables represent groups or categories that have no inherent numerical meaning. They are used to classify data. These variables can take an infinite number of values within a given range and are measurable on a scale.
Examples: Gender (male/female), blood type (A, B, AB, O), or employment status (employed/unemployed). Examples: Height, weight, income, or temperature.

Quantitative & Qualitative Variables

Quantitative Variables Qualitative Variables
These involve numerical data that can be measured or counted. These describe non-numeric characteristics or qualities.
Examples: Number of products sold, test scores, or age in years. Examples: Hair colour, customer feedback, or political opinion.

Discrete Vs Continuous Variables

Discrete Variables Continuous Variables
These are countable variables that take specific, separate values with no in-between. These can take any value within a given range and can include fractions or decimals.
Examples: Number of students in a class, number of cars in a parking lot, or number of children in a family. Examples: Time taken to complete a task, body weight, or temperature.

How To Identify Variables In A Research Study

Here is a process explanation to find variables in your research problem:

  1. Underline the action (verb) and the measured outcome (noun). The action often points to the independent variable and the outcome to the dependent variable.
  2. If you can change one factor to see an effect on another, the first is likely the independent variable and the second the dependent variable.
  3. Any element described with numbers, scores, percentages, time, frequency, counts, or scales is likely a quantitative variable.
  4. Identify factors that the researcher keeps the same. These are controlled variables (or constants) and are important to list to preserve internal validity.
  5. Search for possible external influences. Note any extraneous or confounding variables that might affect the dependent variable if not controlled.
  6. Ask whether a third factor might change the strength/direction of the main relationship (moderator) or explain the mechanism linking the two variables (mediator).
  7. For each variable, classify it as categorical/nominal, ordinal, discrete, continuous, quantitative, or qualitative. This determines analysis methods.
  8. Specify exactly how each variable will be measured (e.g., “academic performance measured as percentage score on the end-of-term exam”).

Tips For Naming And Defining Variables Clearly

  • Use precise, concise names (e.g., WeeklyStudyHours, SystolicBP_mmHg, CustomerSatisfactionScore).
  • Include the measurement unit or scale in the name or definition (e.g., “Age in years”, “Sales growth as percentage change”).
  • Provide an operational definition for abstract concepts (e.g. “Motivation defined as score on the 10-item Motivation Scale”).
  • Differentiate closely related variables (e.g. AdvertisingSpend_USD vs AdvertisingFrequency_perWeek).
  • State the direction of measurement when relevant (e.g. “Higher scores indicate greater anxiety”).
  • Keep terms consistent across the study. Use the same variable names in the research question, methods, tables and codebook.
  • Document categories for categorical variables (e.g. Gender: 1 = Male, 2 = Female, 3 = Non-binary).
  • Pre-register or pilot test the variable definitions if possible to check clarity and feasibility.

Examples

1. Research title: The impact of hours of study on undergraduate exam performance.

Independent variable Hours of study per week (continuous; measured in hours).
Dependent variable Exam performance (continuous; measured as percentage score on the final exam).
Controlled variables Course level, instructor, and exam difficulty.
Possible confounder Prior GPA (may need to be controlled or included as a covariate).

2. Research title: Effect of daily 10 mg antihypertensive medication on systolic blood pressure

Independent variable Medication dosage (categorical/ controlled: 10 mg vs placebo).
Dependent variable Systolic blood pressure (continuous; mmHg measured at clinic visits).
Controlled variables Measurement time, cuff size, and patient posture.
Possible confounder Patient adherence to medication (monitor or measure).

3. Research title: How social support moderates the relationship between work stress and burnout among nurses

Independent variable Work stress (quantitative; score on validated stress scale).
Dependent variable Burnout (quantitative; score on Maslach Burnout Inventory).
Controlled variables Social support (quantitative; score on social support scale).
Possible confounder Shift type, years of experience, department.

4. Research title: The role of advertising spend in increasing online sales across peak and off-peak seasons

Independent variable Advertising spend per week (continuous; USD).
Dependent variable Online sales (continuous; weekly revenue USD).
Moderator variable Seasonality (categorical: peak vs off-peak).
Controlled variables Price, product range, website downtime.
Possible confounder Promotional discounts (track and control).

Frequently Asked Questions



academhelper.com academhelper.com

 
"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"
ORDER NOW

Population vs Sample – Definitions, differences, and examples

/in /by


Population Sample
All university students in the UK (the entire group of interest). 200 students selected from 10 UK universities (a subset of the population).
All customers of a national bank (the total pool). 500 customers surveyed from three major branches (a representation of the customers).
All employees of a multinational company (the entire workforce). 150 employees from the marketing and finance departments (a smaller, targeted group).
All households in a city (every unit in the target area). 250 households chosen randomly for a housing survey (a measured portion).
All patients with diabetes in a country (the complete patient group). 300 patients receiving treatment in five hospitals (a manageable subset for study).

What Is A Population In Research

A population refers to the complete group of individuals, items, or data that a researcher wants to study or draw conclusions about. It includes every element that fits the criteria of the research question.

The population is the entire set from which data could potentially be collected.

A research population has several key features:

Size It can be large (e.g., all university students in the UK) or small (e.g., all teachers in a single school), representing the total number of units of interest.
Scope It defines the boundaries of who or what is included, based on factors such as age, location, occupation, or behaviour (the criteria for belonging).
Inclusivity Every individual or element that meets the defined criteria is considered part of the population; it is the entire set from which a sample is drawn.

Types Of Populations

Researchers generally divide populations into two main categories:

Target Population

This refers to the entire group that the researcher aims to understand or draw conclusions about. 

For instance, if a study focuses on higher education trends, the target population might be all university students in the UK.

Accessible Population

This is the portion of the target population that the researcher can actually reach or collect data from. 

For example, if only students from 10 universities participate, that group represents the accessible population.

Population Example

Imagine a study investigating the impact of online learning on academic performance. 

The population could be all university students in the UK. 

However, since it’s impossible to survey every student, researchers often select a smaller group, a sample, to represent this larger population accurately.

What Is A Sample In Research

A sample is a smaller group selected from a larger population to take part in a research study. It represents the characteristics of the entire population, and allows researchers to draw conclusions without studying everyone.

A sample is a subset of the population that helps make research more manageable and efficient.

Researchers use samples because studying an entire population is often time-consuming, expensive, and impractical. Sampling allows them to:

  • Collect data quickly and efficiently
  • Reduce research costs
  • Focus on quality data collection and analysis
  • Make generalisations about the whole population with a reasonable degree of accuracy

Types Of Samples

There are two main categories of sampling methods, each serving a specific research need:

Probability Samples

Every individual in the population has a known chance of being selected. This method reduces bias and increases representativeness.

Random Sampling Each member of the population has an equal chance of being selected. This is often achieved using random number generators.
Stratified Sampling The population is divided into subgroups (strata) based on a characteristic (e.g., gender, age), and samples are randomly taken from each group to ensure proportional representation.
Cluster Sampling The population is divided into clusters (e.g., schools, cities), and entire clusters are randomly selected for the study. All members within the chosen clusters are typically surveyed.

Non-Probability Samples

Selection is based on convenience or judgment rather than randomisation. This is often used in exploratory or qualitative studies.

Convenience Sampling Participants are chosen simply because they are easily accessible and available to the researcher (e.g., surveying students in your own class).
Purposive Sampling Participants are deliberately selected based on specific, pre-defined characteristics relevant to the study’s research question (e.g., interviewing only managers with 10+ years of experience).
Quota Sampling The researcher ensures that the sample includes specific proportions of subgroups (e.g., 50% male, 50% female) to mirror the population, but selection within those groups is non-random.

Sample Example

For instance, if the population includes all university students in the UK, the sample might be 200 students selected from ten different universities to participate in a survey about online learning.

How to calculate population sample size in research?

To calculate sample size, researchers use statistical formulas that consider:

  • Population size (total number of individuals)
  • Confidence level (usually 95%)
  • Margin of error (commonly 5%)
  • Expected variability or response rate

A commonly used formula is:

Where:

  • n = sample size
  • N = population size
  • Z = Z-score (1.96 for 95% confidence)
  • p = estimated proportion (0.5 if unknown)
  • e = margin of error



academhelper.com academhelper.com

 
"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"
ORDER NOW

Experimental Design – Essays UK

/in /by


What Is Experimental Design In Research

Experimental design in research is a structured plan used to test how changes in one factor (the independent variable) affect another factor (the dependent variable).

It involves creating a controlled setting where researchers can manipulate certain variables and measure the outcomes. 

The main goals of experimental design are control, manipulation, and observation:

Control Researchers aim to minimise the impact of external or unrelated variables (confounds) that could influence the results, ensuring the observed effect is due to the independent variable.
Manipulation The independent variable is deliberately changed or introduced by the researcher to observe its effect on the dependent variable.
Observation The outcomes are measured carefully and systematically to determine whether the manipulation caused any significant or measurable change in the dependent variable.

Examples Of Experimental Research

  • Psychology: Studying how different levels of sleep affect memory performance in adults.
  • Education: Testing whether interactive learning methods improve student engagement compared to traditional lectures.
  • Business: Conducting A/B testing to see which marketing campaign leads to higher sales conversions.

Principles Of Experimental Design

The four core principles are control, randomisation, replication, and comparison. These principles help eliminate bias and strengthen the validity of your findings. 

1. Control

Control refers to keeping all conditions constant except for the variable being tested. By controlling extraneous factors, researchers can be more confident that any changes in the dependent variable are due to the manipulation of the independent variable. 

For example:

when testing the effect of light on plant growth, temperature and water should be kept constant.

2. Randomisation

Randomisation means assigning participants or experimental units to groups purely by chance. This prevents selection bias and ensures that each participant has an equal opportunity to be placed in any group. Randomisation helps balance out unknown or uncontrollable factors that might otherwise affect the results.

3. Replication

Replication involves repeating the experiment under the same conditions to confirm that the results are consistent. When similar outcomes occur across multiple trials, the findings become more reliable and less likely to be due to random chance. Replication strengthens the credibility of your conclusions.

4. Comparison

Comparison is achieved by having at least two groups, typically an experimental group and a control group. This allows researchers to compare outcomes and determine whether the independent variable caused a measurable effect. Without comparison, it would be impossible to identify cause-and-effect relationships accurately.

Key Elements Of A Good Experimental Design

A strong experimental design is built on a clear structure and reliable measurement. Here are the key components:

Independent and Dependent Variables

Every experiment involves at least two types of variables. The independent variable is the one you intentionally manipulate, while the dependent variable is what you measure to observe the effect of that manipulation. 

For example, in a study on the impact of caffeine on concentration, caffeine intake is the independent variable, and concentration level is the dependent variable.

Hypothesis Formulation

A hypothesis is a clear, testable statement predicting the relationship between variables. It guides your entire experiment. 

For instance, the hypothesis “Increased caffeine intake improves short-term memory performance” can be tested and measured.

Experimental and Control Groups

In most experiments, participants are divided into two groups:

  • The experimental group, which receives the treatment or intervention.
  • The control group, which does not receive the treatment, serves as a baseline for comparison.

Sample Selection and Size

The sample should represent the larger population being studied. Additionally, determining an appropriate sample size ensures that results are statistically reliable and not due to random chance.

Data Collection Methods and Instruments

Depending on the study type, researchers may use surveys, tests, observations, sensors, or software to gather data. The choice of instrument should align with the research goals and the variables being studied.

Types Of Experimental Design

Below are the main types of experimental design commonly used in scientific and applied research.

Type 1: True Experimental Design

A true experimental design involves random assignment of participants to control and experimental groups. This randomisation helps eliminate bias and ensures that each group is comparable.

Examples 

Pre-test/Post-test Design Participants are tested before and after the treatment to measure change.
Solomon Four-Group Design Combines pre-test/post-test and control groups to reduce potential testing effects.

Type 2: Quasi-Experimental Design

In a quasi-experimental design, participants are not randomly assigned to groups. This design is often used when randomisation is impossible, unethical, or impractical, such as in educational or organisational research.

Although quasi-experiments are less controlled, they still provide valuable insights into causal relationships under real-world conditions.

Type 3: Factorial Design

A factorial design studies two or more independent variables simultaneously to understand how they interact and influence the dependent variable.

For example, a business study might test how both advertising media (social media vs. TV) and message style (emotional vs. rational) affect consumer behaviour.

This type of design allows researchers to explore complex relationships and interactions between multiple factors.

Type 4: Randomised Controlled Trials (RCTs)

Randomised controlled trials are a specialised form of true experimental design often used in medicine, psychology, and health sciences. Participants are randomly assigned to either the treatment or control group, and outcomes are compared to measure the treatment’s effectiveness.

RCTs are highly valued because they minimise bias and provide strong evidence for causation, making them the preferred choice for testing new drugs, therapies, or interventions.

How To Conduct An Experimental Design

Here’s a step-by-step guide to conducting an effective experimental design:

Step 1: Define the Research Problem and Objectives

Start by identifying the research problem you want to solve and setting clear objectives. This helps you focus your study and decide what kind of data you need. A well-defined problem ensures that your experiment remains purposeful and structured throughout.

Step 2: Formulate Hypotheses

Next, develop one or more testable hypotheses based on your research question. A hypothesis predicts how one variable affects another, for example, “Exercise improves mood in adults.” This statement gives direction to your study and helps determine what data to collect.

Step 3: Select Variables and Participants

Identify your independent and dependent variables, along with any control variables that must remain constant. Then, select participants who represent your target population. Ensure your sample size is large enough to produce meaningful, generalisable results.

Step 4: Choose the Experimental Design Type

Select the most suitable experimental design based on your research aims, ethical considerations, and available resources. You might choose a true, quasi, or factorial design depending on whether randomisation and multiple variables are involved.

Step 5: Conduct Pilot Testing

Before running the full experiment, perform a pilot test on a small scale. This helps you identify any design flaws, unclear instructions, or technical issues. Adjust your procedures or tools accordingly to ensure smooth data collection in the main study.

Step 6: Collect and Analyse Data

Run your experiment according to the planned procedures, ensuring consistency and accuracy. Once data collection is complete, use statistical methods to analyse results and determine whether your findings support or reject the hypothesis.

Step 7: Interpret and Report Findings

Finally, interpret what your results mean in the context of your research question. Discuss whether your hypothesis was supported, note any limitations, and suggest areas for future research. Present your findings clearly in a report or publication, using graphs, tables, and visual aids where necessary.

Frequently Asked Questions



academhelper.com academhelper.com

 
"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"
ORDER NOW

What Is Data Collection – Methods, Steps & Examples

/in /by


What Is Data Collection?

Data collection means gathering information in an organised way to answer a specific question or understand a problem.

It involves collecting facts, figures, opinions, or observations that help draw meaningful conclusions. Whether through surveys, interviews, or experiments, the goal is to get accurate and reliable information that supports your study.

If you use Spotify, you know that at the end of every year, you get a Spotify Wrapped. The only way they can show it to you is because they collect your listening data throughout the year. 

Importance Of Data Collection In Statistical Analysis

  • Data collection is the foundation of all research and statistical analysis.
  • Accurate data ensures that findings and conclusions are grounded in evidence.
  • Without reliable data, even advanced statistical tools cannot produce valid results.
  • Quality data helps researchers identify trends and test hypotheses effectively.
  • Well-collected data supports confident, informed decision-making in real-world contexts.

Why is accurate data important for valid research results?

Accurate data ensures that research findings are valid and trustworthy. When information is collected correctly, it reflects the actual characteristics of the population or phenomenon being studied. This allows researchers to draw meaningful conclusions and make informed recommendations. In contrast, inaccurate or incomplete data can distort results, leading to false interpretations and unreliable outcomes.

How does poor data collection affect statistical conclusions?

Poor data collection can lead to biased samples, missing values, or measurement errors, all of which negatively affect statistical results. 

For instance, if a study only collects responses from a small or unrepresentative group, the conclusions may not apply to the wider population. This weakens the reliability and credibility of the research.

Types Of Data In Research

Here are the two main types of data in research:

Primary Data

Primary data refers to information collected first-hand by the researcher for a specific study. It is original, fresh, and directly related to the research objectives. Since this data is gathered through direct interaction or observation, it is highly reliable and tailored to the study’s needs.

Here are some of the most commonly used methods of primary data collection:

  • Surveys and questionnaires
  • Interviews (structured or unstructured)
  • Experiments and field studies
  • Observations and focus groups

When to use primary data?

Researchers use primary data when they need specific, up-to-date, and original information. For example, a study analysing students’ learning habits during online classes would require primary data collected through surveys or interviews.

Secondary Data

Secondary data is information that has already been collected, analysed, and published by others. This type of data is easily accessible through journals, books, online databases, government reports, and research repositories. Common sources of secondary data include the following:

  • Academic publications and literature reviews
  • Institutional or government reports
  • Statistical databases and archived research

When to use secondary data?

Researchers often use secondary data when they want to build on existing studies, compare results, or save time and resources. For instance, a researcher analysing trends in global healthcare spending might use data from the WHO or World Bank databases.

Quantitative vs Qualitative Data Collection

In research, data collection methods are often classified as quantitative or qualitative.

  • Quantitative = measurable, numerical, and objective
  • Qualitative = descriptive, subjective, and interpretive

Quantitative data answers “how much” or “how many”, while qualitative data explains “why” or “how.”

What Is Quantitative Data Collection?

Quantitative data collection involves gathering numerical data that can be measured, counted, and statistically analysed. This method focuses on objective information and is often used to test hypotheses or identify patterns.

  • Surveys and questionnaires with closed-ended questions
  • Experiments with measurable variables
  • Statistical observations and numerical records

Example: A researcher studying student performance might use test scores or attendance data to analyse how study habits affect grades.

What Is Qualitative Data Collection?

Qualitative data collection focuses on non-numerical information such as opinions, emotions, and experiences. It helps researchers understand the why and how behind certain behaviours or outcomes.

  • In-depth interviews
  • Focus groups
  • Observations and case studies

Example: Interviewing students to explore their feelings about online learning provides rich, descriptive insights that numbers alone cannot capture.

Combining Both In Mixed-Method Research

Many researchers use a mixed-method approach, combining both quantitative and qualitative techniques. This helps validate findings and provides a more comprehensive understanding of the research problem.

Example: A study on employee satisfaction might use surveys (quantitative) to measure satisfaction levels and interviews (qualitative) to understand the reasons behind those levels.

Steps In The Data Collection Process

Here are the five essential steps in the data collection process:

Step 1: Define Research Objectives

The first step is to identify what you want to achieve with your research clearly. Defining the objectives helps determine the type of data you need and the best way to collect it. For example, if your goal is to understand customer satisfaction, you will need to collect data directly from consumers through surveys or feedback forms.

Step 2: Choose The Right Data Collection Method

Once objectives are clear, select a method that fits your research goals. You can choose between primary methods (such as interviews or experiments) and secondary methods (such as literature reviews or existing databases). The right choice depends on the research topic, timeline, and available resources.

Step 3: Develop Research Instruments

Create or select the tools you will use to collect data, such as questionnaires, interview guides, or observation checklists. These instruments must be well-structured, easy to understand, and aligned with your research objectives to ensure consistent results.

Step 4: Collect & Record Data Systematically

Gather the data in an organised and ethical manner. Record information carefully using reliable methods like digital forms, spreadsheets, or specialised software to avoid loss or duplication of data. Consistency at this stage ensures the accuracy of your results.

Step 5: Verify Data Accuracy & Validity

Finally, review and validate the collected data to identify and correct any errors, inconsistencies, or missing values. Verification ensures the data is accurate, reliable, and ready for statistical analysis. Clean and validated data lead to stronger, more credible research outcomes.

Frequently Asked Questions



academhelper.com academhelper.com

 
"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"
ORDER NOW

Analyzing the impact of trade wars on the Global Economy

/in /by


Trade wars defined by reciprocal rise in tariffs as well as non-tariff barriers among the countries have become common features increasingly considering international economic relations. Their core impacts are observed to be extended effectively beyond immediate tariff costs, which are also by affecting process of global supply chains, flows of investments and holistic stability in the economy. Thus, in this correspondence, the article at present is subject to analyse the multifaceted impacts of trade wars on the global economy. Moreover, by understanding these core dynamics, policymakers as well as business leaders would be able to navigate the complexities of global trade effectively in the era that is marked by economic nationalism alongside protectionism.

Trade wars are defined by governments in context of imposing quotas, tariffs as well as non-tariff barriers on imported goods. The purpose is to protect domestic industries and retaliating against practices that are perceived as unfair (Adjemian et al. 2021). This strategy therefore aims at decreasing deficits of trade and promotes local productions; however, often disputes might establish global trade patterns. Contextually, it is to be said that tariffs would increase the costs of raw materials that are imported and intermediate goods. That is also by forcing the industries for reconfiguration supply chains and absorbing higher expenses regarding production.

Figure 1: New Tariffs Impact

(Source: weforum.org, 2025)

On the other hand, it is also to be stated that retaliatory measures by the targeted nations have compounded these effects by leading towards escalating protectionism cycle (Benguria et al. 2022). These types of policies however breed uncertainties, deterring FDI in long-term and creating distortions in the market. While being intended to shield markets domestically, these measures have frequently leading towards decreasing efficiency level alongside strained international relations. That has ultimately undermined national and global economic stability both throughout the interconnected networks of supply.

On the other hand, it can be stated that global supply chain would be representing intricate networks that would be moving raw materials, alongside intermediary components and finished products across the borders (Kim and Margalit, 2021). Certainly, in this accordance, it is to be considered that trade wars would be disrupting these networks through introduction of tariffs that can increase the cost of moving goods, while compelling companies for reconfiguration of production process and sourcing strategies. These adjustments however have often resulted into inefficiencies as well as delays that can compromise timelines of productions and holistic performance of the economies. Moreover, it can be evaluated that higher transportation and logistics costs would be coupled with unpredictable shifts in supply chain (Park et al. 2021). That is also by decreasing the benefits of international specialisation and economies of scale. In addition to this, it is also to be noted that prolonging uncertainties would be discouraging investments in context of innovation along with technologies. Thereby, it would be hampering improvements of productivity level. However, as companies are gradually adapting to these challenges, the ripple effects are observed to be extending beyond individual firms (Brutger et al. 2023). That is also by undermining economic efficiency in long-term and competitiveness in the highly competitive global marketplace.

Significantly, in context of the current discussion of the article, exemplification of US-China Trade War can be given. This trade war has exemplified how tariffs that are escalated can create disruptions in the international markets (Fetzer and Schwarz, 2021). Thus, in this context, it can be seen that in the year 2018, United States had imposed tariffs on billions of dollars of imports from China, which had prompted China to be retaliating in similar manner. This specific escalation has further affected sectors like technology, agriculture and manufacturing.

Figure 2: Tariffs Escalation on US-China Bilateral Trade

(Source: weforum.org, 2025)

Moreover, American farmers are found to be suffering as access towards the Chinese markets have decreased, while on the other hand, Chinese manufacturers have incurred higher costs of production because of supply chain adjustments to be highly tariff-induced (Fajgelbaum et al. 2024). Certainly, in this accordance, it can be evaluated that the turning uncertainties have forced companies in revising strategies related to sourcing and diversification of supply chains, by altering trade patterns being long-established. Although specific domestic industries have seen benefits temporarily, holistic impacts had slowed the growth of the economy and increased the volatility in the market, while weakening confidence of the investors (Huang et al. 2023). This specific case therefore has highlighted the way protectionist measures regardless of its aim to support domestic industries would often create widespread uncertainties in the economy and disrupting global trade process and practices. Thus, it is serving as the cautionary statement for policymakers across the globe.

Moreover, in context of wider economic implications, it can be seen that trade wars would be extending their core impacts beyond individual industries. That would certainly influence broader global economy base. In addition to this, it is further to be stated that elevated tariffs would decrease volumes of international trade (Caliendo and Parro, 2022). Thereby, it might be dampening growth of the economies across the globe. Significantly, rising costs for the raw materials alongside finished goods would be rippling throughout the networks of production, leading towards higher prices for consumers and decreased purchasing power.

Figure 3: Wider Implications of Trade War

(Source: weforum.org, 2025)

Thus, with the mounting uncertainties, business investments would decline and confidence of the consumers would be eroding, which would ultimately slow the momentum of economy. Likewise; trade conflicts are also found to be straining diplomatic relations while fostering environment surrounding geopolitical tensions and instabilities (Ogunjobi et al. 2023). This uncertainty would discourage investments in long-term, specifically across the emerging markets and can equally disrupt the global financial markets.

In summary, it can be implied that trade wars would be having complex as well as might often have detrimental impacts on the global economic stance. They would be disrupting supply chains, elevating costs of production alongside discouraging investments. The exemplification of the US-China Trade War has exhibited how those conflicts could alter the dynamics of the market and forcing industries as well as the governments in adapting to new reality of rising uncertainties and shifting power of economy. Ultimately, on suggestive perspective, it can be stated that mitigating the adverse impacts of trade wars would certainly require balanced approach that would be considering national interests as well as global integration of economies both simultaneously. This balance is certain critical to foster sustainable growth and ensuring globalisation and its benefits to continually be shared vastly throughout the nations.

Adjemian, M.K., Smith, A. and He, W., 2021. Estimating the market effect of a trade war: The case of soybean tariffs. Food Policy, 105, p.102152.

Benguria, F., Choi, J., Swenson, D.L. and Xu, M.J., 2022. Anxiety or pain? The impact of tariffs and uncertainty on Chinese firms in the trade war. Journal of International Economics, 137, p.103608.

Brutger, R., Chaudoin, S. and Kagan, M., 2023. Trade wars and election interference. The review of international organizations, 18(1), pp.1-25.

Caliendo, L. and Parro, F., 2022. Trade policy. Handbook of international economics, 5, pp.219-295.

Fajgelbaum, P., Goldberg, P., Kennedy, P., Khandelwal, A. and Taglioni, D., 2024. The US-China trade war and global reallocations. American Economic Review: Insights, 6(2), pp.295-312.

Fetzer, T. and Schwarz, C., 2021. Tariffs and politics: evidence from Trump’s trade wars. The Economic Journal, 131(636), pp.1717-1741.

Huang, H., Ali, S. and Solangi, Y.A., 2023. Analysis of the impact of economic policy uncertainty on environmental sustainability in developed and developing economies. Sustainability, 15(7), p.5860.

Kim, S.E. and Margalit, Y., 2021. Tariffs as electoral weapons: The political geography of the US–China trade war. International organization, 75(1), pp.1-38.

Ogunjobi, O.A., Eyo-Udo, N.L., Egbokhaebho, B.A., Daraojimba, C., Ikwue, U. and Banso, A.A., 2023. Analyzing historical trade dynamics and contemporary impacts of emerging materials technologies on international exchange and us strategy. Engineering Science & Technology Journal, 4(3), pp.101-119.

Park, C.Y., Petri, P.A. and Plummer, M.G., 2021. The economics of conflict and cooperation in the Asia-pacific: RCEP, CPTPP and the US-China trade war. East Asian economic review, 25(3), pp.233-272.

weforum.org, (2025), This is how much the US-China trade war could cost the world, according to new research, Available at: https://www.weforum.org/stories/2019/06/this-is-how-much-the-us-china-trade-war-could-cost-the-world-according-to-new-research/ [Accessed on 07.02.2025]



academhelper.com academhelper.com

 
"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"
ORDER NOW

How to Simplify Complex Topics in Your University Assignments

/in /by


1. Grasp the Core Question Before Anything Else

Most students make the mistake of jumping straight into summarizing the material. They collect quotes, definitions, and data without grasping what it actually means. This only makes the topic seem heavier. Before you dive into research, step back and ask: What is this topic really about?

Take law students, for example. When they study cases like the Bard PowerPort lawsuit, it’s easy to get lost in the technicalities. With nearly 2,000 cases filed, it has become a significant point of study in product liability law. 

According to TorHoerman Law, the case involves a medical device allegedly causing injuries due to design defects. However, diving into it can be overwhelming, as the technical details, legal filings, and regulatory language can easily pull students off track.

But the essence of that case boils down to a simple, powerful question: who is responsible when a medical device harms a patient? Once that question is clear, the complexity around it starts to make sense.

Understanding the central issue helps you filter what matters and what doesn’t. Every paragraph you write should serve that main question. Everything else is decoration.

2. Rewrite It in Plain English

Here’s a trick most good writers use: once you understand the idea, try explaining it to a friend outside your field. If you can’t do that without stumbling, you don’t fully grasp it yet.

This approach mirrors the Feynman Technique, named after physicist Richard Feynman. He argued that true understanding shows when you can explain something in simple terms. This approach pushes you to remove jargon and unnecessary details until you’re left with the core idea. 

You’ll notice that technical terms often hide simple truths. “Habeas corpus,” for instance, just means the right not to be detained unlawfully. “Statistical significance” simply shows that a result probably didn’t happen by chance.

When you rewrite a paragraph in plain English first, then add the academic polish later, your argument becomes cleaner. Professors notice that. Clarity shows mastery. Confusion looks like bluffing.

3. Divide and Build, Don’t Drown

Complexity often feels heavy because it’s all tangled together. The best way to manage that weight is to divide your topic into logical parts and then build upward.

Start broad, then move inward. Say you’re writing about data privacy. You could structure it around three layers: what data is collected, how it’s used, and who protects it. Once those pillars are set, every piece of research fits under one of them. The same logic applies to any discipline.

Law students do this instinctively when they outline cases. They don’t memorize every word; they break each case into facts, issues, rules, and conclusions. That’s how they handle hundreds of pages of legal material efficiently. You can use that same method for essays in economics, psychology, or literature.

Dividing information turns an intimidating topic into a series of smaller, solvable puzzles. When you finish one section, you feel progress instead of panic, and that momentum matters.

4. Anchor Theory in Real Examples

Abstract concepts stay foggy until you connect them to the real world. That’s why examples are your best friends when simplifying difficult material. They give shape and emotion to ideas that otherwise live only in theory.

But to build strong, relevant examples, you need critical thinking. Psychology Today points out that the ability to think clearly, critically, and effectively is among the most important skills a person can have. However, research shows it’s becoming one of the most endangered. 

The way to sharpen it is simple but deliberate. Question your assumptions, look for patterns across disciplines, and test your reasoning instead of taking information at face value.

A psychology student explaining cognitive dissonance could point to how people justify risky behavior despite knowing the dangers. An engineering student might explain mechanical failure by describing a bridge collapse. Examples translate complexity into something the reader can see and feel.

5. Edit for Clarity, Not Just Grammar

Most students think editing means fixing typos and commas. That’s the surface level. Real editing means reading your work for clarity. Are your sentences carrying too many ideas at once? Are you using complicated phrasing to sound smarter? Are you assuming your reader already knows something they don’t?

Good editing trims all that fat. If you can say something in ten words instead of twenty, do it. Long sentences don’t make you sound more academic. They make you sound unsure.

Once you finish writing, step away for a few hours. Then review it with fresh eyes, as if someone else wrote it. If a sentence makes you pause or reread, it’s probably unclear. Simplify it.

A well-edited paper reads like a steady conversation- confident, clean, and easy to follow. Professors remember that clarity more than they remember how many sources you cited.

Frequently Asked Questions



academhelper.com academhelper.com

 
"Looking for a Similar Assignment? Get Expert Help at an Amazing Discount!"
ORDER NOW