data analysis in research format

The format of a research proposal varies between fields, but most proposals will contain at least these elements:

• Introduction

Literature review

• Research design

Reference list

While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take.

Proposal Format

The proposal will usually have a  title page  that includes:

• The proposed title of your project
• Your supervisor’s name
• Your institution and department

Introduction The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.. Your introduction should:

• Introduce your  topic
• Give necessary background and context
• Outline your  problem statement  and  research questions To guide your  introduction , include information about:  
• Who could have an interest in the topic (e.g., scientists, policymakers)
• How much is already known about the topic
• What is missing from this current knowledge
• What new insights will your research contribute
• Why you believe this research is worth doing

As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong  literature review  shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have done or said, but rather using existing research as a jumping-off point for your own.

In this section, share exactly how your project will contribute to ongoing conversations in the field by:

• Comparing and contrasting the main theories, methods, and debates
• Examining the strengths and weaknesses of different approaches
• Explaining how will you build on, challenge, or  synthesize  prior scholarship

Research design and methods

Following the literature review, restate your main  objectives . This brings the focus back to your project. Next, your  research design  or  methodology  section will describe your overall approach, and the practical steps you will take to answer your research questions. Write up your projected, if not actual, results.

Contribution to knowledge

To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters.

For example, your results might have implications for:

• Improving best practices
• Informing policymaking decisions
• Strengthening a theory or model
• Challenging popular or scientific beliefs
• Creating a basis for future research

Lastly, your research proposal must include correct  citations  for every source you have used, compiled in a  reference list . To create citations quickly and easily, you can use free APA citation generators like BibGuru. Databases have a citation button you can click on to see your citation. Sometimes you have to re-format it as the citations may have mistakes. 

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How to write data analysis in a research paper.

Data from quantitative research can be analyzed using statistical methods. From sample data, predictions about a population can be tested with probabilities and models. The statistical analysis of quantitative data involves examining trends, patterns, and relationships. Currently, statistical tools are widely used by researchers, scientists, academicians, government agencies, businesses, and several other organizations. Data collected through an experiment or a probability sampling method can be subjected to statistical tests. The sample size of a statistical test must be large enough to accurately reflect the true distribution of the population to be studied. When deciding which statistical test to use to write, one must be aware of whether the data is consistent with certain assumptions and which variables are involved.

The statistical analysis needs to be carefully planned at the very beginning of the research process to draw valid conclusions. A researcher paper must specify their hypotheses and determine the design of their study, the sample size, and the sampling procedure. Data collected from the sample can be organized and summarized using descriptive statistics. Using inferential statistics, it is possible to formalize the process of testing hypotheses and estimating the population size. Findings can then be interpreted and generalized. A descriptive statistic summarizes the characteristics of a set of data. Inferential statistics are used to test hypotheses and evaluate whether the data on which they are based can be generalized to a broader population.

Step 1: Plan your hypothesis and research design

Specifying your hypotheses and designing your research will help you collect valid data for statistical analysis.

Analyzing statistical hypotheses

Researchers often examine the relationship between variables within a population. A prediction is the starting point, and statistical analysis allows you to test that prediction. A statistical hypothesis is a method for formulating a prediction about a population in a prescribed manner. The null and alternative hypotheses for each research prediction can be verified using the sample data. Although the null hypothesis always predicts no relationship between variables, the alternative hypothesis describes the relationship you predict from your research.

How to plan your research design

The research design refers to your method for gathering and analyzing data. This helps you determine what statistical test you will need to test your hypothesis. Determine whether you are applying an experimental approach, a descriptive approach, or a correlational approach in your research design. Studies using analysis and correlation do not directly affect variables, but only measure them, but experimental studies influence variables directly. We can investigate the relationships between variables by using a correlational design. The descriptive design involves the use of statistical tests to draw inferences from sample data about the characteristics of a population or phenomenon.

Measuring variables

Plan your research design by operationalizing your variables and deciding how they will be measured. The level of measurement of your variables is an important consideration when conducting statistical analysis. Categorical data consist of groups, while quantitative data are concerned with amounts. Making the right statistical and hypothesis testing choices is based on identifying the measurement level.

Step 2: Obtain data from a representative sample

Once you have used an appropriate sampling procedure when conducting statistical analysis, you can extend your conclusions beyond your sample. Probability sampling involves selecting participants at random from the population to conduct a study. In non-probability sampling, some members of a population have a higher chance of being selected than other members due to factors such as convenience or self-selection.

Step 3: Analyze and summarize your data

Your data can be examined and summarized with descriptive statistics after you have collected all the data.

Step 4: Analyze hypotheses and make inferences using inferential statistics

Numbers that describe a sample are called statistics, while numbers that describe the entire population are called parameters. Using inference statistics, you can conclude the characteristics of a population based on a sample. Based on the null hypothesis, statistical tests determine where the sampled data would fall in an expected distribution. There are two main outcomes of these tests: You can determine your test statistic by comparing your results with the null hypothesis. The test statistic measures how much your data deviate from the null hypothesis. A p-value helps you determine whether you are likely to obtain your results if your null hypothesis is true among the population.

Step 5: Interpret your results

Interpreting your findings is the final step of our statistical analysis.

Statistical significance

Conclusions are drawn from hypothesis testing based on statistical significance. The p-value of your results is compared with a set significance level (0.05) to determine whether they are statistically significant. The likelihood that statistically significant results arise from chance is highly unlikely. Such a result is extremely unlikely to occur in the population if the null hypothesis is true.

Analysing data and interpreting the results requires practice and guidance

Organization

The organisation is the key to writing a good report. An outline should include: 1) the problem overview, 2) the data analysis and model approach, 3) the results of the data analysis, and 4) the substantive conclusions.

Problem Overview

• Provide a description of the problem.
• Are you attempting to answer a substantive question?
• It doesn’t have to be long, but it should be clear.

Data Analysis and Model Approach

• Was your approach to addressing the question data-driven? If so, how?
• Be specific in describing your approach.

A Data and Model section will sometimes include graphs or tables, and sometimes not. Include a plot if you believe it will assist the reader in understanding the problem or data set itself, rather than your conclusions. While these tables provide important information about the data and approach, they do not provide information about the results of the study.

The results of the Data Analysis

Provide figures and tables that must support your argument in your results section. Label the images add informative captions, and refer to them in the text by their numbered labels. The following items might be included here: pictures of the data, pictures of the fitted model, a table of coefficients, and summaries of the model.

The Substantive Conclusions

• What conclusions did you draw from this analysis?
• Is there an answer to the question you set out to address?

Factors to consider when analyzing your research data

To demonstrate a high standard of research practice, researchers should possess adequate skills for analysing data. To gain better insights into data, it is ideal for researchers to understand the rationale for selecting one statistical method over another. The methods used in research and data analysis differ in scientific fields; therefore, designing a survey questionnaire, choosing data collection methods, and choosing a sample play a crucial role at the outset of an analysis. Analysing data in research presents accurate and reliable information. The most important thing researchers should remember when analysing data is to remain open and unbiased toward unpredictable patterns, results, and expressions.

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Scopus q1 q2 q3 and q4  journals, interview with dr.kukiat tudpor: geographic information system-based health surveillance for community-dwelling older persons.

8 Types of Data Analysis

The different types of data analysis include descriptive, diagnostic, exploratory, inferential, predictive, causal, mechanistic and prescriptive. Here’s what you need to know about each one.

Data analysis is an aspect of data science and  data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and  modeling data to draw useful insights from it.

Types of Data Analysis

• Descriptive analysis
• Diagnostic analysis
• Exploratory analysis
• Inferential analysis
• Predictive analysis
• Causal analysis
• Mechanistic analysis
• Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including energy, healthcare and marketing, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in decision-making , providing a better, faster and more effective system that minimizes risks and reduces human biases .

That said, there are different kinds of data analysis with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book R for Data Science :

• Hypothesis Generation: This involves looking deeply at the data and combining your domain knowledge to generate  hypotheses about why the data behaves the way it does.
• Hypothesis Confirmation: This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.  

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data . Here’s what you need to know:

• Descriptive analysis is the very first analysis performed in the data analysis process.
• It generates simple summaries of samples and measurements.
• It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

2. Diagnostic Analysis  

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

• Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen. 
• Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.  
• Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies .  

Diagnostic Analysis Example

A footwear store wants to review its  website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic. 

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August. 

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.    

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or  exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

• EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
• It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection .

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses. 

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

• Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or  standard deviation to your estimation.
• The accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the central limit theorem .

Inferential Analysis Example

A psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven to nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions. 

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

• The accuracy of the predictions depends on the input variables.
• Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
• Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 United States election is a popular topic and many prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. This way, researchers can examine how a change in one variable affects another. Here’s what you need to know:

• To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
• Causal analysis is applied in randomized studies focused on identifying causation.
• Causal analysis is the gold standard in data analysis and scientific studies where the cause of a phenomenon is to be extracted and singled out, like separating wheat from chaff.
• Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example  

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome. 

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables . In some ways, it is a predictive analysis, but it’s modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science. Here’s what you need to know:

• It’s applied in physical or engineering sciences, situations that require high  precision and little room for error, only noise in data is measurement error.
• It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention. 

Mechanistic Analysis Example

Say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

8. Prescriptive Analysis  

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: 

• Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses. 
• Companies need advanced technology and plenty of resources to conduct prescriptive analysis. Artificial intelligence systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.  

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram,  algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an  algorithm seeks out similar content that is likely to elicit the same response and  recommends it on a user’s personal feed. 

More on Data Explaining the Empirical Rule for Normal Distribution

When to Use the Different Types of Data Analysis  

• Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
• Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies. 
• Exploratory data analysis helps you discover correlations and relationships between variables in your data.
• Inferential analysis is for generalizing the larger population with a smaller sample size of data.
• Predictive analysis helps you make predictions about the future with data.
• Causal analysis emphasizes finding the cause of a correlation between variables.
• Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
• Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes. 

A few important tips to remember about data analysis include:

• Correlation doesn’t imply causation.
• EDA helps discover new connections and form hypotheses.
• Accuracy of inference depends on the sampling scheme.
• A good prediction depends on the right input variables.
• A simple linear model with enough data usually does the trick.
• Using a variable to predict another doesn’t denote causal relationships.
• Good data is hard to find, and to produce it requires expensive research.
• Results from studies are done in aggregate and are average effects and might not apply to everyone.​

Frequently Asked Questions

What is an example of data analysis.

A marketing team reviews a company’s web traffic over the past 12 months. To understand why sales rise and fall during certain months, the team breaks down the data to look at shoe type, seasonal patterns and sales events. Based on this in-depth analysis, the team can determine variables that influenced web traffic and make adjustments as needed.

How do you know which data analysis method to use?

Selecting a data analysis method depends on the goals of the analysis and the complexity of the task, among other factors. It’s best to assess the circumstances and consider the pros and cons of each type of data analysis before moving forward with a particular method.

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Data analysis and findings

Data analysis is the most crucial part of any research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends. 

Data Analysis Checklist

Cleaning  data

* Did you capture and code your data in the right manner?

*Do you have all data or missing data?

* Do you have enough observations?

* Do you have any outliers? If yes, what is the remedy for outlier?

* Does your data have the potential to answer your questions?

Analyzing data

* Visualize your data, e.g. charts, tables, and graphs, to mention a few.

*  Identify patterns, correlations, and trends

* Test your hypotheses

* Let your data tell a story

Reports the results

* Communicate and interpret the results

* Conclude and recommend

* Your targeted audience must understand your results

* Use more datasets and samples

* Use accessible and understandable data analytical tool

* Do not delegate your data analysis

* Clean data to confirm that they are complete and free from errors

* Analyze cleaned data

* Understand your results

* Keep in mind who will be reading your results and present it in a way that they will understand it

* Share the results with the supervisor oftentimes

Past presentations

• PhD Writing Retreat - Analysing_Fieldwork_Data by Cori Wielenga A clear and concise presentation on the ‘now what’ and ‘so what’ of data collection and analysis - compiled and originally presented by Cori Wielenga.

Online Resources

• Qualitative analysis of interview data: A step-by-step guide
• Qualitative Data Analysis - Coding & Developing Themes

Beginner's Guide to SPSS

• SPSS Guideline for Beginners Presented by Hennie Gerber

Recommended Quantitative Data Analysis books

Recommended Qualitative Data Analysis books

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• Last Updated: Aug 23, 2024 12:44 PM
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Quantitative Data Analysis: Everything You Need to Know

11 min read

Does the thought of quantitative data analysis bring back the horrors of math classes? We get it.

But conducting quantitative data analysis doesn’t have to be hard with the right tools. Want to learn how to turn raw numbers into actionable insights on how to improve your product?

In this article, we explore what quantitative data analysis is, the difference between quantitative and qualitative data analysis, and statistical methods you can apply to your data. We also walk you through the steps you can follow to analyze quantitative information, and how Userpilot can help you streamline the product analytics process. Let’s get started.

• Quantitative data analysis is the process of using statistical methods to define, summarize, and contextualize numerical data.
• Quantitative analysis is different from a qualitative one. The first deals with numerical data and focuses on answering “what,” “when,” and “where.” However, a qualitative analysis relies on text, graphics, or videos and explores “why” and “how” events occur.
• Pros of quantitative data analysis include objectivity, reliability, ease of comparison, and scalability.
• Cons of quantitative metrics include the data’s limited context and inflexibility, and the need for large sample sizes to get statistical significance.
• The methods for analyzing quantitative data are descriptive and inferential statistics.
• Choosing the right analysis method depends on the type of data collected and the specific research questions or hypotheses.
• These are the steps to conduct quantitative data analysis: 1. Defining goals and KPIs . 2. Collecting and cleaning data. 3. Visualizing the data. 4. Identifying patterns . 5. Sharing insights. 6. Acting on findings to improve decision-making.
• With Userpilot , you can auto-capture in-app user interactions and build analytics dashboards . This tool also lets you conduct A/B and multivariate tests, and funnel and cohort analyses .
• Gather and visualize all your product analytics in one place with Userpilot. Get a demo .

Try Userpilot and Take Your Product Experience to the Next Level

• 14 Day Trial
• No Credit Card Required

What is quantitative data analysis?

Quantitative data analysis is about applying statistical analysis methods to define, summarize, and contextualize numerical data. In short, it’s about turning raw numbers and data into actionable insights.

The analysis will vary depending on the research questions and the collected data (more on this below).

Quantitative vs qualitative data analysis

The main difference between these forms of analysis lies in the collected data. Quantitative data is numerical or easily quantifiable. For example, the answers to a customer satisfaction score (CSAT) survey are quantitative since you can count the number of people who answered “very satisfied”.

Qualitative feedback , on the other hand, analyzes information that requires interpretation. For instance, evaluating graphics, videos, text-based answers, or impressions.

Another difference between quantitative and qualitative analysis is the questions each seeks to answer. For instance, quantitative data analysis primarily answers what happened, when it happened, and where it happened. However, qualitative data analysis answers why and how an event occurred.

Quantitative data analysis also looks into identifying patterns , drivers, and metrics for different groups. However, qualitative analysis digs deeper into the sample dataset to understand underlying motivations and thinking processes.

Pros of quantitative data analysis

Quantitative or data-driven analysis has advantages such as:

• Objectivity and reliability. Since quantitative analysis is based on numerical data, this reduces biases and allows for more objective conclusions. Also, by relying on statistics, this method ensures the results are consistent and can be replicated by others, making the findings more reliable.
• Easy comparison. Quantitative data is easily comparable because you can identify trends , patterns, correlations, and differences within the same group and KPIs over time. But also, you can compare metrics in different scales by normalizing the data, e.g., bringing ratios and percentages into the same scale for comparison.
• Scalability. Quantitative analysis can handle large volumes of data efficiently, making it suitable for studies involving large populations or datasets. This makes this data analysis method scalable. Plus, researchers can use quantitative analysis to generalize their findings to broader populations.

Cons of quantitative data analysis

These are common disadvantages of data-driven analytics :

• Limited context. Since quantitative data looks at the numbers, it often strips away the data from the context, which can show the underlying reasons behind certain trends. This limitation can lead to a superficial understanding of complex issues, as you often miss the nuances and user motivations behind the data points.
• Inflexibility. When conducting quantitative research, you don’t have room to improvise based on the findings. You need to have predefined hypotheses, follow scientific methods, and select data collection instruments. This makes the process less adaptable to new or unexpected findings.
• Large sample sizes necessary. You need to use large sample sizes to achieve statistical significance and reliable results when doing quantitative analysis. Depending on the type of study you’re conducting, gathering such extensive data can be resource-intensive, time-consuming, and costly.

Quantitative data analysis methods

There are two statistical methods for reviewing quantitative data and user analytics . However, before exploring these in-depth, let’s refresh these key concepts:

• Population. This is the entire group of individuals or entities that are relevant to the research.
• Sample. The sample is a subset of the population that is actually selected for the research since it is often impractical or impossible to study the entire population.
• Statistical significance. The chances that the results gathered after your analysis are realistic and not due to random chance.

Here are methods for analyzing quantitative data:

Descriptive statistics

Descriptive statistics, as the name implies, describe your data and help you understand your sample in more depth. It doesn’t make inferences about the entire population but only focuses on the details of your specific sample.

Descriptive statistics usually include measures like the mean, median, percentage, frequency, skewness, and mode.

Inferential statistics

Inferential statistics aim to make predictions and test hypotheses about the real-world population based on your sample data.

Here, you can use methods such as a T-test, ANOVA, regression analysis, and correlation analysis.

Let’s take a look at this example. Through descriptive statistics, you identify that users under the age of 25 are more likely to skip your onboarding. You’ll need to apply inferential statistics to determine if the result is statistically significant and applicable to your entire ’25 or younger’ population.

How to choose the right method for your quantitative data analysis

The type of data that you collect and the research questions that you want to answer will impact which quantitative data analysis method you choose. Here’s how to choose the right method:

Determine your data type

Before choosing the quantitative data analysis method, you need to identify which group your data belongs to:

• Nominal —categories with no specific order, e.g., gender, age, or preferred device.
• Ordinal —categories with a specific order, but the intervals between them aren’t equal, e.g., customer satisfaction ratings .
• Interval —categories with an order and equal intervals, but no true zero point, e.g., temperature (where zero doesn’t mean “no temperature”).
• Ratio —categories with a specific order, equal intervals, and a true zero point, e.g., number of sessions per user .

Applying any statistical method to all data types can lead to meaningless results. Instead, identify which statistical analysis method supports your collected data types.

Consider your research questions

The specific research questions you want to answer, and your hypothesis (if you have one) impact the analysis method you choose. This is because they define the type of data you’ll collect and the relationships you’re investigating.

For instance, if you want to understand sample specifics, descriptive statistics—such as tracking NPS —will work. However, if you want to determine if other variables affect the NPS, you’ll need to conduct an inferential analysis.

The overarching questions vary in both of the previous examples. For calculating the NPS, your internal research question might be, “Where do we stand in customer loyalty ?” However, if you’re doing inferential analysis, you may ask, “How do various factors, such as demographics, affect NPS?”

6 steps to do quantitative data analysis and extract meaningful insights

Here’s how to conduct quantitative analysis and extract customer insights :

1. Set goals for your analysis

Before diving into data collection, you need to define clear goals for your analysis as these will guide the process. This is because your objectives determine what to look for and where to find data. These goals should also come with key performance indicators (KPIs) to determine how you’ll measure success.

For example, imagine your goal is to increase user engagement. So, relevant KPIs include product engagement score , feature usage rate , user retention rate, or other relevant product engagement metrics .

2. Collect quantitative data

Once you’ve defined your goals, you need to gather the data you’ll analyze. Quantitative data can come from multiple sources, including user surveys such as NPS, CSAT, and CES, website and application analytics , transaction records, and studies or whitepapers.

Remember: This data should help you reach your goals. So, if you want to increase user engagement , you may need to gather data from a mix of sources.

For instance, product analytics tools can provide insights into how users interact with your tool, click on buttons, or change text. Surveys, on the other hand, can capture user satisfaction levels . Collecting a broad range of data makes your analysis more robust and comprehensive.

3. Clean and visualize your data

Raw data is often messy and contains duplicates, outliers, or missing values that can skew your analysis. Before making any calculations, clean the data by removing these anomalies or outliers to ensure accurate results.

Once cleaned, turn it into visual data by using different types of charts , graphs, or heatmaps . Visualizations and data analytics charts make it easier to spot trends, patterns, and anomalies. If you’re using Userpilot, you can choose your preferred visualizations and organize your dashboard to your liking.

4. Identify patterns and trends

When looking at your dashboards, identify recurring themes, unusual spikes, or consistent declines that might indicate data analytics trends or potential issues.

Picture this: You notice a consistent increase in feature usage whenever you run seasonal marketing campaigns . So, you segment the data based on different promotional strategies. There, you discover that users exposed to email marketing campaigns have a 30% higher engagement rate than those reached through social media ads.

In this example, the pattern suggests that email promotions are more effective in driving feature usage.

If you’re a Userpilot user, you can conduct a trend analysis by tracking how your users perform certain events.

5. Share valuable insights with key stakeholders

Once you’ve discovered meaningful insights, you have to communicate them to your organization’s key stakeholders. Do this by turning your data into a shareable analysis report , one-pager, presentation, or email with clear and actionable next steps.

Your goal at this stage is for others to view and understand the data easily so they can use the insights to make data-led decisions.

Following the previous example, let’s say you’ve found that email campaigns significantly boost feature usage. Your email to other stakeholders should strongly recommend increasing the frequency of these campaigns and adding the supporting data points.

Take a look at how easy it is to share custom dashboards you built in Userpilot with others via email:

6. Act on the insights

Data analysis is only valuable if it leads to actionable steps that improve your product or service. So, make sure to act upon insights by assigning tasks to the right persons.

For example, after analyzing user onboarding data, you may find that users who completed the onboarding checklist were 3x more likely to become paying customers ( like Sked Social did! ).

Now that you have actual data on the checklist’s impact on conversions, you can work on improving it, such as simplifying its steps, adding interactive features, and launching an A/B test to experiment with different versions.

How can Userpilot help with analyzing quantitative data

As you’ve seen throughout this article, using a product analytics tool can simplify your data analysis and help you get insights faster. Here are different ways in which Userpilot can help:

Automatically capture quantitative data

Thanks to Userpilot’s new auto-capture feature, you can automatically track every time your users click, write a text, or fill out a form in your app—no engineers or manual tagging required!

Our customer analytics platform lets you use this data to build segments, trigger personalized in-app events and experiences, or launch surveys.

If you don’t want to auto-capture raw data, you can turn this functionality off in your settings, as seen below:

Monitor key metrics with customizable dashboards for real-time insights

Userpilot comes with template analytics dashboards , such as new user activation dashboards or customer engagement dashboards . However, you can create custom dashboards and reports to keep track of metrics that are relevant to your business in real time.

For instance, you could build a customer retention analytics dashboard and include all metrics that you find relevant, such as customer stickiness , NPS, or last accessed date.

Analyze experiment data with A/B and multivariate tests

Userpilot lets you conduct A/B and multivariate tests , either by following a controlled or a head-to-head approach. You can track the results on a dashboard.

For example, let’s say you want to test a variation of your onboarding flow to determine which leads to higher user activation .

You can go to Userpilot’s Flows tab and click on Experiments. There, you’ll be able to select the type of test you want to run, for instance, a controlled A/B test , build a new flow, test it, and get the results.

Use quantitative funnel analysis to increase conversion rates

With Userpilot, you can track your customers’ journey as they complete actions and move through the funnel. Funnel analytics give you insights into your conversion rates and conversion times between two events, helping you identify areas for improvement.

Imagine you want to analyze your free-to-paid conversions and the differences between devices. Just by looking at the graphic, you can draw some insights:

• There’s a significant drop-off between steps one and two, and two and three, indicating potential user friction .
• Users on desktops convert at higher rates than those on mobile or unspecified devices.
• Your average freemium conversion time is almost three days.

Leverage cohort analysis to optimize retention

Another Userpilot functionality that can help you analyze quantitative data is cohort analysis . This powerful tool lets you group users based on shared characteristics or experiences, allowing you to analyze their behavior over time and identify trends, patterns, and the long-term impact of changes on user behavior.

For example, let’s say you recently released a feature and want to measure its impact on user retention. Via a cohort analysis, you can group users who started using your product after the update and compare their retention rates to previous cohorts.

You can do this in Userpilot by creating segments and then tracking user segments ‘ retention rates over time.

Check how many users adopted a feature with a retention table

In Userpilot, you can use retention tables to stay on top of feature adoption . This means you can track how many users continue to use a feature over time and which features are most valuable to your users. The video below shows how to choose the features or events you want to analyze in Userpilot.

As you’ve seen, to conduct quantitative analysis, you first need to identify your business and research goals. Then, collect, clean, and visualize the data to spot trends and patterns. Lastly, analyze the data, share it with stakeholders, and act upon insights to build better products and drive customer satisfaction.

To stay on top of your KPIs, you need a product analytics tool. With Userpilot, you can automate data capture, analyze product analytics, and view results in shareable dashboards. Want to try it for yourself? Get a demo .

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Data analysis write-ups

What should a data-analysis write-up look like.

Writing up the results of a data analysis is not a skill that anyone is born with. It requires practice and, at least in the beginning, a bit of guidance.

Organization

When writing your report, organization will set you free. A good outline is: 1) overview of the problem, 2) your data and modeling approach, 3) the results of your data analysis (plots, numbers, etc), and 4) your substantive conclusions.

1) Overview Describe the problem. What substantive question are you trying to address? This needn’t be long, but it should be clear.

2) Data and model What data did you use to address the question, and how did you do it? When describing your approach, be specific. For example:

• Don’t say, “I ran a regression” when you instead can say, “I fit a linear regression model to predict price that included a house’s size and neighborhood as predictors.”
• Justify important features of your modeling approach. For example: “Neighborhood was included as a categorical predictor in the model because Figure 2 indicated clear differences in price across the neighborhoods.”

Sometimes your Data and Model section will contain plots or tables, and sometimes it won’t. If you feel that a plot helps the reader understand the problem or data set itself—as opposed to your results—then go ahead and include it. A great example here is Tables 1 and 2 in the main paper on the PREDIMED study . These tables help the reader understand some important properties of the data and approach, but not the results of the study itself.

3) Results In your results section, include any figures and tables necessary to make your case. Label them (Figure 1, 2, etc), give them informative captions, and refer to them in the text by their numbered labels where you discuss them. Typical things to include here may include: pictures of the data; pictures and tables that show the fitted model; tables of model coefficients and summaries.

4) Conclusion What did you learn from the analysis? What is the answer, if any, to the question you set out to address?

General advice

Make the sections as short or long as they need to be. For example, a conclusions section is often pretty short, while a results section is usually a bit longer.

It’s OK to use the first person to avoid awkward or bizarre sentence constructions, but try to do so sparingly.

Do not include computer code unless explicitly called for. Note: model outputs do not count as computer code. Outputs should be used as evidence in your results section (ideally formatted in a nice way). By code, I mean the sequence of commands you used to process the data and produce the outputs.

When in doubt, use shorter words and sentences.

A very common way for reports to go wrong is when the writer simply narrates the thought process he or she followed: :First I did this, but it didn’t work. Then I did something else, and I found A, B, and C. I wasn’t really sure what to make of B, but C was interesting, so I followed up with D and E. Then having done this…” Do not do this. The desire for specificity is admirable, but the overall effect is one of amateurism. Follow the recommended outline above.

Here’s a good example of a write-up for an analysis of a few relatively simple problems. Because the problems are so straightforward, there’s not much of a need for an outline of the kind described above. Nonetheless, the spirit of these guidelines is clearly in evidence. Notice the clear exposition, the labeled figures and tables that are referred to in the text, and the careful integration of visual and numerical evidence into the overall argument. This is one worth emulating.

Quantitative Data Analysis

In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have formulated at the earlier stages of your research process .

The same figure within data set can be interpreted in many different ways; therefore it is important to apply fair and careful judgement.

For example, questionnaire findings of a research titled “A study into the impacts of informal management-employee communication on the levels of employee motivation: a case study of Agro Bravo Enterprise” may indicate that the majority 52% of respondents assess communication skills of their immediate supervisors as inadequate.

This specific piece of primary data findings needs to be critically analyzed and objectively interpreted through comparing it to other findings within the framework of the same research. For example, organizational culture of Agro Bravo Enterprise, leadership style, the levels of frequency of management-employee communications need to be taken into account during the data analysis.

Moreover, literature review findings conducted at the earlier stages of the research process need to be referred to in order to reflect the viewpoints of other authors regarding the causes of employee dissatisfaction with management communication. Also, secondary data needs to be integrated in data analysis in a logical and unbiased manner.

Let’s take another example. You are writing a dissertation exploring the impacts of foreign direct investment (FDI) on the levels of economic growth in Vietnam using correlation quantitative data analysis method . You have specified FDI and GDP as variables for your research and correlation tests produced correlation coefficient of 0.9.

In this case simply stating that there is a strong positive correlation between FDI and GDP would not suffice; you have to provide explanation about the manners in which the growth on the levels of FDI may contribute to the growth of GDP by referring to the findings of the literature review and applying your own critical and rational reasoning skills.

A set of analytical software can be used to assist with analysis of quantitative data. The following table  illustrates the advantages and disadvantages of three popular quantitative data analysis software: Microsoft Excel, Microsoft Access and SPSS.

Advantages and disadvantages of popular quantitative analytical software

Quantitative data analysis with the application of statistical software consists of the following stages [1] :

• Preparing and checking the data. Input of data into computer.
• Selecting the most appropriate tables and diagrams to use according to your research objectives.
• Selecting the most appropriate statistics to describe your data.
• Selecting the most appropriate statistics to examine relationships and trends in your data.

It is important to note that while the application of various statistical software and programs are invaluable to avoid drawing charts by hand or undertake calculations manually, it is easy to use them incorrectly. In other words, quantitative data analysis is “a field where it is not at all difficult to carry out an analysis which is simply wrong, or inappropriate for your data or purposes. And the negative side of readily available specialist statistical software is that it becomes that much easier to generate elegantly presented rubbish” [2] .

Therefore, it is important for you to seek advice from your dissertation supervisor regarding statistical analyses in general and the choice and application of statistical software in particular.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of quantitative data analysis methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

[1] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6th edition, Pearson Education Limited.

[2] Robson, C. (2011) Real World Research: A Resource for Users of Social Research Methods in Applied Settings (3rd edn). Chichester: John Wiley.

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Top 10 Data Analysis Research Proposal Templates with Examples and Samples

Himani Khatri

In a world awash with data, the real challenge lies not in the abundance of information but in deciphering its true meaning, making sense of the chaos, and addressing pressing real-world problems. If you're a researcher or student, you know the struggle: the pain points of grappling with data quality, precision, and relevance. It's these very challenges that underscore the critical importance of crafting a well-structured data analysis research proposal.

Think of it as your toolkit, a roadmap to navigate the complexities of data-driven research and turn information into solutions. In this blog, we're here to help you master the art of creating a data analysis research proposal, providing you with the key to unlock the answers to those nagging questions, and offer solutions (Our editable templates) to problems that keep you up at night.

As we start this journey, let's draw inspiration from two illustrious examples, Google Flu Trends and Netflix's Recommendation Algorithm, which have not only captured the limelight but have tackled data-related pain points and transformed them into remarkable solutions. These examples will serve as guiding stars as we navigate the intricacies of data analysis to craft proposals that address real-world issues head-on.

Google Flu Trends : Conquering the Challenge of Data Accuracy

Imagine having the power to predict flu outbreaks with uncanny precision. Google Flu Trends did just that, tapping into the vast sea of search queries. But it wasn't just about innovation; it was also about recognizing the persistent pain point of data accuracy and modeling. The project revealed that behind every data analysis success story lies the challenge of ensuring data quality and building models that stand up to the rigorous demands of real-world problems.

Netflix's Recommendation Algorithm : Navigating the Data Overload Dilemma

In the world of entertainment, where options seem endless, Netflix's Recommendation Algorithm emerged as a winner. It tackled the overwhelming pain point of information overload by leveraging data to understand users better. The result? A recommendation system that not only improved user satisfaction but also demonstrated how data analysis can help individuals navigate through the ever-growing sea of choices and make their lives easier.

In these two case studies, we uncover the real-world challenges that data analysis can address, from accuracy dilemmas to information overload.

Let's explore the research proposal presentation templates now!

Template 1: Data Analysis in Research Proposal

Click Here to Download

Introducing this cover slide of the proposal that has been professionally designed and sets the stage for your entire research proposal. With ample space for an image, it captures your audience's attention from the start. Your proposal's credentials, both for the recipient and the preparer, can be displayed. Both researchers and professionals can take assistance to streamline the presentation creation process, leaving you more time to focus on your data analysis. Make a lasting impression and get your proposal noticed with this polished, easy-to-use template.

Template 2: Cover Letter for Research Data Analysis Proposal

Introducing this Cover Letter Slide, which will help you make a lasting impression in the world of research and analytics. We understand the importance of clear and concise communication in proposals. Our professionally crafted slide provides a perfect introduction, addressing your customers and outlining your company's objectives. Say goodbye to the hassle of creating proposals from scratch – with our ready-made slide, you can simply insert your details and be on your way to success. This cover letter helps you state that your experience and expertise will help your audience achieve their goals effortlessly. Don't miss this opportunity – grab this proposal slide and make a strong, confident start in the world of data analytics.

Template 3 – Project Context and Objectives of Research Data Analysis Proposal

This slide simplifies the process of impressing your clients. It explains your project's context and objectives, leaving a lasting impact on your audience.

Project Context: We provide a clear and concise space for explaining the background and significance of your research, setting the stage for your proposal.

Project Objectives: Clearly outline your research goals and what you aim to achieve, ensuring everyone understands your mission.

Make your research proposal shine with this template at your disposal.

Template 4: Scope of Work for Research Data Analysis Proposal

This slide outlines your research data analysis journey, making client presentations a breeze. Our scope of work slide covers all the essentials: Acquisition & Extraction, Examination, Cleaning, Transformation, Exploration, and Analysis, leading to the grand finale - Presenting and Sharing your findings. With clear and easy-to-understand visuals, impress your clients and streamline your workflow.

Template 5: Plan of Action for Research Data Analysis Proposal

Are you looking to present your research data analysis plan with clarity and professionalism? Our ready-made PowerPoint slide has got you covered. This user-friendly template features a visual diagram illustrating the entire process, from data collection through pre-processing, analysis, and classification. With easy-to-understand icons and clear labels, you can effectively convey your plan to your audience.

Template 6: Timeline for Research Data Analysis Project

Designed with simplicity, this timeline slide offers a user-friendly layout to help you convey complex ideas easily. It covers every crucial step of your analysis journey, from tackling business issues to final presentation. With vibrant visuals and customizable elements, you can effortlessly illustrate data understanding, preparation, exploratory analysis, validation, and visualization. Get it today!

Template 7: Key Deliverables for Research Data Analysis Proposal

With clear, concise visuals, this slide presents your key deliverables. From ‘Decision Mapping’ that outlines your project's path to ‘Analysis and Design’ for robust strategies, and ‘Implementation’ for real-world action, it's all here. Even better, it highlights ‘Ongoing Steps’ for sustained success. Why waste time on complex slides when you can have this ready-made gem? Elevate your presentations and win your audience over with this template at your disposal.

Template 8: Why Our Data Analytics Company?

This slide helps you showcase why people should choose your company rather than your competitors. Elucidate what makes your organization stand out from the rest by taking assistance of this readily-available PowerPoint slide. 

It lists down the strength that keeps your firm on the top in comparison with your rivals.

Some of the strengths mentioned in the slide are:

• Reduced churn rate
• Reduced operational cost
• Increased revenue
• Faster data analysis reporting

Template 9: Services Offered by Data Analytics Company 

This slide presents the services offered by data analysis company in a clear and precise way. Get your hands on this slide to present your offerings. The template encapsulates services like data collection services, data quality assess, data integration, policy analytics, social media and digital outreach, enterprise analytics, and more.

Template 10: Team Structure of Data Analysis Company

The slide presents team structure of data analytics company in a comprehensive format. A hierarchy chart makes it easy for organization to showcase their talented staff and the driving forces behind their firm’s success, this is where this template comes into assistance. Put your hands on this template to present head of advanced analytics, COE Support office, demand management, analytics development, analytics support, etc.

These templates are your one-stop solution for crafting compelling Research Data Analysis Proposals.

With a subscription to our service, you gain access to an extensive library of ready-made PowerPoint templates that will save you time and effort. But that's not all – if you require a personalized touch, our team can also design a custom proposal that perfectly aligns with your unique needs.

Why wait? Join our community of satisfied customers and supercharge your research endeavors today.

Subscribe now and get your hands on impactful presentations!

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Journal Article Reporting Standards (JARS)

APA Style Journal Article Reporting Standards offer guidance on what information should be included in all manuscript sections for quantitative, qualitative, and mixed methods research and include how to best discuss race, ethnicity, and culture.

Introducing Journal Article Reporting Standards for Race, Ethnicity, and Culture (JARS–REC)

JARS–REC were created to develop best practices related to the manner in which race, ethnicity, and culture are discussed within scientific manuscripts in psychological science.

Quantitative research

Use JARS–Quant when you collect your study data in numerical form or report them through statistical analyses.

Qualitative research

Use JARS–Qual when you collect your study data in the form of natural language and expression.

Mixed methods research

Use JARS–Mixed when your study combines both quantitative and qualitative methods.

Race, ethnicity, culture

Use JARS–REC for all studies for guidance on how to discuss race, ethnicity, and culture.

What are APA Style JARS?

APA Style Journal Article Reporting Standards (APA Style Jars ) are a set of standards designed for journal authors, reviewers, and editors to enhance scientific rigor in peer-reviewed journal articles. Educators and students can use APA Style JARS as teaching and learning tools for conducting high quality research and determining what information to report in scholarly papers.

The standards include information on what should be included in all manuscript sections for:

• Quantitative research ( Jars –Quant)
• Qualitative research ( Jars –Qual)
• Mixed methods research ( Jars –Mixed)

Additionally, the APA Style Journal Article Reporting Standards for Race, Ethnicity, and Culture ( Jars – Rec ) provide guidance on how to discuss race, ethnicity, and culture in scientific manuscripts. Jars – Rec should be applied to all research, whether it is quantitative, qualitative, or mixed methods.

• Race, Ethnicity, and Culture ( Jars – Rec )

Using these standards will make your research clearer and more accurate as well as more transparent for readers. For quantitative research, using the standards will increase the reproducibility of science. For qualitative research, using the standards will increase the methodological integrity of research.

Jars –Quant should be used in research where findings are reported numerically (quantitative research). Jars –Qual should be used in research where findings are reported using nonnumerical descriptive data (qualitative research). Jars –Mixed should be applied to research that includes both quantitative and qualitative research (mixed methods research). JARS–REC should be applied to all research, whether it is quantitative, qualitative, or mixed methods.

For more information on APA Style JARS:

• Read Editorial: Journal Article Reporting Standards
• View an infographic (PDF, 453KB) to learn about the benefits of JARS and how they are relevant to you
• Listen to a podcast with Drs. Harris Cooper and David Frost discussing JARS and implications for research in psychology

Many aspects of research methodology warrant a close look, and journal editors can promote better methods if we encourage authors to take responsibility to report their work in clear, understandable ways. —Nelson Cowan, Editor, Journal of Experimental Psychology: General

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Watch a video about JARS

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This video describes and discusses the updated APA Style Journal Article Reporting Standards.

Related products

Reporting Qualitative Research in Psychology

Journal article reporting standards for qualitative research

Reporting Quantitative Research in Psychology

Journal article reporting standards for quantitative research

Publication Manual, 7th Edition

The official source for writing papers and creating references in seventh edition APA Style

Jars resources

• History of APA’s journal article reporting standards
• APA Style JARS supplemental glossary
• Supplemental resource on the ethic of transparency in JARS
• Frequently asked questions
• JARS-Quant Decision Flowchart (PDF, 98KB)
• JARS-Quant Participant Flowchart (PDF, 98KB)

Jars articles

• Jars –Quant article
• Jars –Qual / Mixed article
• Jars – rec executive summary

Questions / feedback

Email an APA Style Expert if you have questions, feedback, or suggestions for modules to be included in future JARS updates.

APA resources

• APA Databases and Electronic Resources
• APA Journals
• Journal Author Resource Center
• Education and Career
• Psychological Science
• Open Science at APA
• How to Review a Manuscript

From the APA Style blog

Introducing APA Style Journal Article Reporting Standards for Race, Ethnicity, and Culture

These standards are for all authors, reviewers, and editors seeking to improve manuscript quality by encouraging more racially and ethnically conscious and culturally responsive journal reporting standards for empirical studies in psychological science.

APA Style JARS for high school students

In this post, we provide an overview of APA Style JARS and resources that can be shared with high school students who want to learn more about effective communication in scholarly research.

Happy 2022, APA Stylers!

This blog post is dedicated to our awesome APA Style users. You can use the many resources on our website to help you master APA Style and improve your scholarly writing.

APA Style JARS on the EQUATOR Network

The APA Style Journal Article Reporting Standards (APA Style JARS) have been added to the EQUATOR Network. The network aims to promote accuracy and quality in reporting of research.

APA Style JARS: Resources for instructors and students

APA Style Journal Article Reporting Standards (APA Style JARS) are a set of guidelines for papers reporting quantitative, qualitative, and mixed methods research that can be used by instructors, students, and all others reading and writing research papers.

• How it works

Thematic Analysis – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

Thematic analysis is one of the most important types of analysis used for qualitative data . When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the speaker.

Moreover, with the help of this analysis, data can be simplified.  

Importance of Thematic Analysis

Thematic analysis has so many unique and dynamic features, some of which are given below:

Thematic analysis is used because:

• It is flexible.
• It is best for complex data sets.
• It is applied to qualitative data sets.
• It takes less complexity compared to other theories of analysis.

Intellectuals and researchers give preference to thematic analysis due to its effectiveness in the research.

How to Conduct a Thematic Analysis?

While doing any research , if your data and procedure are clear, it will be easier for your reader to understand how you concluded the results . This will add much clarity to your research.

Understand the Data

This is the first step of your thematic analysis. At this stage, you have to understand the data set. You need to read the entire data instead of reading the small portion. If you do not have the data in the textual form, you have to transcribe it.

Example: If you are visiting an adult dating website, you have to make a data corpus. You should read and re-read the data and consider several profiles. It will give you an idea of how adults represent themselves on dating sites. You may get the following results:

I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humor. Being a handyperson, I keep busy working around the house, and I also like to follow my favourite hockey team on TV or spoil my two granddaughters when I get the chance!! Enjoy most music except Rap! I keep fit by jogging, walking, and bicycling (at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times, and adventures together

I enjoy photography, lapidary & seeking collectibles in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception.

Development of Initial Coding:

At this stage, you have to do coding. It’s the essential step of your research . Here you have two options for coding. Either you can do the coding manually or take the help of any tool. A software named the NOVIC is considered the best tool for doing automatic coding.

For manual coding, you can follow the steps given below:

• Please write down the data in a proper format so that it can be easier to proceed.
• Use a highlighter to highlight all the essential points from data.
• Make as many points as possible.
• Take notes very carefully at this stage.
• Apply themes as much possible.
• Now check out the themes of the same pattern or concept.
• Turn all the same themes into the single one.

Example: For better understanding, the previously explained example of Step 1 is continued here. You can observe the coded profiles below:

Make Themes

At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.

Extracted Data Review

Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.

For better understanding, a mind-mapping example is given here:

Reviewing all the Themes Again

You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation. 

You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.

Corpus Data

Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.

When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:

Define all the Themes here

Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.

The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.

Make a Report

You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.

While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.  

Frequently Asked Questions

What is meant by thematic analysis.

Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.

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Home » Textual Analysis – Types, Examples and Guide

Textual Analysis – Types, Examples and Guide

Table of Contents

Textual Analysis

Textual analysis is the process of examining a text in order to understand its meaning. It can be used to analyze any type of text, including literature , poetry, speeches, and scientific papers. Textual analysis involves analyzing the structure, content, and style of a text.

Textual analysis can be used to understand a text’s author, date, and audience. It can also reveal how a text was constructed and how it functions as a piece of communication.

Textual Analysis in Research

Textual analysis is a valuable tool in research because it allows researchers to examine and interpret text data in a systematic and rigorous way. Here are some ways that textual analysis can be used in research:

• To explore research questions: Textual analysis can be used to explore research questions in various fields, such as literature, media studies, and social sciences. It can provide insight into the meaning, interpretation, and communication patterns of text.
• To identify patterns and themes: Textual analysis can help identify patterns and themes within a set of text data, such as analyzing the representation of gender or race in media.
• To evaluate interventions: Textual analysis can be used to evaluate the effectiveness of interventions, such as analyzing the language and messaging of public health campaigns.
• To inform policy and practice: Textual analysis can provide insights that inform policy and practice, such as analyzing legal documents to inform policy decisions.
• To analyze historical data: Textual analysis can be used to analyze historical data, such as letters, diaries, and newspapers, to provide insights into historical events and social contexts.

Textual Analysis in Cultural and Media Studies

Textual analysis is a key tool in cultural and media studies as it enables researchers to analyze the meanings, representations, and discourses present in cultural and media texts. Here are some ways that textual analysis is used in cultural and media studies:

• To analyze representation: Textual analysis can be used to analyze the representation of different social groups, such as gender, race, and sexuality, in media and cultural texts. This analysis can provide insights into how these groups are constructed and represented in society.
• To analyze cultural meanings: Textual analysis can be used to analyze the cultural meanings and symbols present in media and cultural texts. This analysis can provide insights into how culture and society are constructed and understood.
• To analyze discourse: Textual analysis can be used to analyze the discourse present in cultural and media texts. This analysis can provide insights into how language is used to construct meaning and power relations.
• To analyze media content: Textual analysis can be used to analyze media content, such as news articles, TV shows, and films, to understand how they shape our understanding of the world around us.
• To analyze advertising : Textual analysis can be used to analyze advertising campaigns to understand how they construct meanings, identities, and desires.

Textual Analysis in the Social Sciences

Textual analysis is a valuable tool in the social sciences as it enables researchers to analyze and interpret text data in a systematic and rigorous way. Here are some ways that textual analysis is used in the social sciences:

• To analyze interview data: Textual analysis can be used to analyze interview data, such as transcribed interviews, to identify patterns and themes in the data.
• To analyze survey responses: Textual analysis can be used to analyze survey responses to identify patterns and themes in the data.
• To analyze social media data: Textual analysis can be used to analyze social media data, such as tweets and Facebook posts, to identify patterns and themes in the data.
• To analyze policy documents: Textual analysis can be used to analyze policy documents, such as government reports and legislation, to identify discourses and power relations present in the policy.
• To analyze historical data: Textual analysis can be used to analyze historical data, such as letters and diaries, to provide insights into historical events and social contexts.

Textual Analysis in Literary Studies

Textual analysis is a key tool in literary studies as it enables researchers to analyze and interpret literary texts in a systematic and rigorous way. Here are some ways that textual analysis is used in literary studies:

• To analyze narrative structure: Textual analysis can be used to analyze the narrative structure of a literary text, such as identifying the plot, character development, and point of view.
• To analyze language and style: Textual analysis can be used to analyze the language and style used in a literary text, such as identifying figurative language, symbolism, and rhetorical devices.
• To analyze themes and motifs: Textual analysis can be used to analyze the themes and motifs present in a literary text, such as identifying recurring symbols, themes, and motifs.
• To analyze historical and cultural context: Textual analysis can be used to analyze the historical and cultural context of a literary text, such as identifying how the text reflects the social and political context of its time.
• To analyze intertextuality: Textual analysis can be used to analyze the intertextuality of a literary text, such as identifying how the text references or is influenced by other literary works.

Textual Analysis Methods

Textual analysis methods are techniques used to analyze and interpret various types of text, including written documents, audio and video recordings, and online content. These methods are commonly used in fields such as linguistics, communication studies, sociology, psychology, and literature.

Some common textual analysis methods include:

Content Analysis

This involves identifying patterns and themes within a set of text data. This method is often used to analyze media content or other types of written materials, such as policy documents or legal briefs.

Discourse Analysis

This involves examining how language is used to construct meaning in social contexts. This method is often used to analyze political speeches or other types of public discourse.

Critical Discourse Analysis

This involves examining how power and social relations are constructed through language use, particularly in political and social contexts.

Narrative Analysis

This involves examining the structure and content of stories or narratives within a set of text data. This method is often used to analyze literary texts or oral histories.

This involves analyzing the meaning of signs and symbols within a set of text data. This method is often used to analyze advertising or other types of visual media.

Text mining

This involves using computational techniques to extract patterns and insights from large sets of text data. This method is often used in fields such as marketing and social media analysis.

Close Reading

This involves a detailed and in-depth analysis of a particular text, focusing on the language, style, and literary techniques used by the author.

How to Conduct Textual Analysis

Here are some general steps to conduct textual analysis:

• Choose your research question: Define your research question and identify the text or set of texts that you want to analyze.
• F amiliarize yourself with the text: Read and re-read the text, paying close attention to its language, structure, and content. Take notes on key themes, patterns, and ideas that emerge.
• Choose your analytical approach: Select the appropriate analytical approach for your research question, such as close reading, thematic analysis, content analysis, or discourse analysis.
• Create a coding scheme: If you are conducting content analysis, create a coding scheme to categorize and analyze the content of the text. This may involve identifying specific words, themes, or ideas to code.
• Code the text: Apply your coding scheme to the text and systematically categorize the content based on the identified themes or patterns.
• Analyze the data: Once you have coded the text, analyze the data to identify key patterns, themes, or trends. Use appropriate software or tools to help with this process if needed.
• Draw conclusions: Draw conclusions based on your analysis and answer your research question. Present your findings and provide evidence to support your conclusions.
• R eflect on limitations and implications: Reflect on the limitations of your analysis, such as any biases or limitations of the selected method. Also, discuss the implications of your findings and their relevance to the broader research field.

When to use Textual Analysis

Textual analysis can be used in various research fields and contexts. Here are some situations when textual analysis can be useful:

• Understanding meaning and interpretation: Textual analysis can help understand the meaning and interpretation of text, such as literature, media, and social media.
• Analyzing communication patterns: Textual analysis can be used to analyze communication patterns in different contexts, such as political speeches, social media conversations, and legal documents.
• Exploring cultural and social contexts: Textual analysis can be used to explore cultural and social contexts, such as the representation of gender, race, and identity in media.
• Examining historical documents: Textual analysis can be used to examine historical documents, such as letters, diaries, and newspapers.
• Evaluating marketing and advertising campaigns: Textual analysis can be used to evaluate marketing and advertising campaigns, such as analyzing the language, symbols, and imagery used.

Examples of Textual Analysis

Here are a few examples:

• Media Analysis: Textual analysis is frequently used in media studies to examine how news outlets and social media platforms frame and present news stories. Researchers can use textual analysis to examine the language and images used in news articles, tweets, and other forms of media to identify patterns and biases.
• Customer Feedback Analysis: Textual analysis is often used by businesses to analyze customer feedback, such as online reviews or social media posts, to identify common themes and areas for improvement. This allows companies to make data-driven decisions and improve their products or services.
• Political Discourse Analysis: Textual analysis is commonly used in political science to analyze political speeches, debates, and other forms of political communication. Researchers can use this method to identify the language and rhetoric used by politicians, as well as the strategies they employ to appeal to different audiences.
• Literary Analysis: Textual analysis is a fundamental tool in literary criticism, allowing scholars to examine the language, structure, and themes of literary works. This can involve close reading of individual texts or analysis of larger literary movements.
• Sentiment Analysis: Textual analysis is used to analyze social media posts, customer feedback, or other sources of text data to determine the sentiment of the text. This can be useful for businesses or organizations to understand how their brand or product is perceived in the market.

Purpose of Textual Analysis

There are several specific purposes for using textual analysis, including:

• To identify and interpret patterns in language use: Textual analysis can help researchers identify patterns in language use, such as common themes, recurring phrases, and rhetorical devices. This can provide insights into the values and beliefs that underpin the text.
• To explore the cultural context of the text: Textual analysis can help researchers understand the cultural context in which the text was produced, including the historical, social, and political factors that shaped the language and messages.
• To examine the intended and unintended meanings of the text: Textual analysis can help researchers uncover both the intended and unintended meanings of the text, and to explore how the language is used to convey certain messages or values.
• To understand how texts create and reinforce social and cultural identities: Textual analysis can help researchers understand how texts contribute to the creation and reinforcement of social and cultural identities, such as gender, race, ethnicity, and nationality.

Applications of Textual Analysis

Here are some common applications of textual analysis:

Media Studies

Textual analysis is frequently used in media studies to analyze news articles, advertisements, and social media posts to identify patterns and biases in media representation.

Literary Criticism

Textual analysis is a fundamental tool in literary criticism, allowing scholars to examine the language, structure, and themes of literary works.

Political Science

Textual analysis is commonly used in political science to analyze political speeches, debates, and other forms of political communication.

Marketing and Consumer Research

Textual analysis is used to analyze customer feedback, such as online reviews or social media posts, to identify common themes and areas for improvement.

Healthcare Research

Textual analysis is used to analyze patient feedback and medical records to identify patterns in patient experiences and improve healthcare services.

Social Sciences

Textual analysis is used in various fields within social sciences, such as sociology, anthropology, and psychology, to analyze various forms of data, including interviews, field notes, and documents.

Linguistics

Textual analysis is used in linguistics to study language use and its relationship to social and cultural contexts.

Advantages of Textual Analysis

There are several advantages of textual analysis in research. Here are some of the key advantages:

• Systematic and objective: Textual analysis is a systematic and objective method of analyzing text data. It enables researchers to analyze text data in a consistent and rigorous way, minimizing the risk of bias or subjectivity.
• Versatile : Textual analysis can be used to analyze a wide range of text data, including interview transcripts, survey responses, social media data, policy documents, and literary texts.
• Efficient : Textual analysis can be a more efficient method of data analysis compared to manual coding or other methods of qualitative analysis. With the help of software tools, researchers can process large volumes of text data more quickly and accurately.
• Allows for in-depth analysis: Textual analysis enables researchers to conduct in-depth analysis of text data, uncovering patterns and themes that may not be visible through other methods of data analysis.
• Can provide rich insights: Textual analysis can provide rich and detailed insights into complex social phenomena. It can uncover subtle nuances in language use, reveal underlying meanings and discourses, and shed light on the ways in which social structures and power relations are constructed and maintained.

Limitations of Textual Analysis

While textual analysis can provide valuable insights into the ways in which language is used to convey meaning and create social and cultural identities, it also has several limitations. Some of these limitations include:

• Limited Scope : Textual analysis is only able to analyze the content of written or spoken language, and does not provide insights into non-verbal communication such as facial expressions or body language.
• Subjectivity: Textual analysis is subject to the biases and interpretations of the researcher, as well as the context in which the language was produced. Different researchers may interpret the same text in different ways, leading to inconsistencies in the findings.
• Time-consuming: Textual analysis can be a time-consuming process, particularly if the researcher is analyzing a large amount of text. This can be a limitation in situations where quick analysis is necessary.
• Lack of Generalizability: Textual analysis is often used in qualitative research, which means that its findings cannot be generalized to larger populations. This limits the ability to draw conclusions that are applicable to a wider range of contexts.
• Limited Accessibility: Textual analysis requires specialized skills and training, which may limit its accessibility to researchers who are not trained in this method.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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30 Years of Experimental Education Research in the Post-Soviet Space: A Meta-Analysis of Interventions

Description.

This is supplementary material to the article "30 Years of Experimental Education Research in the Post-Soviet Space: A Meta-Analysis of Interventions". This meta-analysis systematically evaluates the potential of available research in post-Soviet countries as a basis for an evidence-based approach to improving student achievement. The study was conducted on a selection of 41 publications describing educational interventions aimed at improving student achievement. The supplementary material provided here consists of three files that we would like to share with you: Supplementary_file_1 - xlsx database with coded characteristics of all studies and effect sizes included in the analysis; Supplementary_file_2 - a short version of the database in xlsx format with variables used in the multi-level analysis to calculate the pooled effect size and to examine moderators; Supplementary_file_3 - docx file with all R codes used for the analysis in the article.

• Open access
• Published: 28 August 2024

International aid management in Afghanistan’s health sector from the perspective of national and international managers

• Noorullah Rashed 1 , 2 ,
• Hamidreza Shabanikiya 1 , 4 ,
• Leili Alizamani 1 ,
• Jamshid Jamali 3 &
• Fatemeh Kokabisaghi 1 , 4  

BMC Health Services Research volume  24 , Article number:  1001 ( 2024 ) Cite this article

Metrics details

The primary purpose of international aid is to promote economic and social development around the world. International aid plays an important role in Afghanistan’s healthcare system. The purpose of this study is to investigate international aid management in Afghanistan’s health sector from the perspectives of national and international managers in 2022 and to provide recommendations for the improvement.

Design/methodology/approach

The study has a cross-sectional design. The study participants were chosen by random sampling. The sample size was determined based on Yaman’s formula at 110. The data collection tool was the questionnaire provided by International Health Partnership and Related Initiatives. The data were analyzed in two descriptive (mean and percentage) and analytical formats. Independent t-test, Mann-Whitney, Kolmogorov-Smirnov tests and Variance analysis were used to examine the relationships between demographic variables and the scores of each dimension.

The average scores given to different dimensions of aid management were as following: 1) the donners’ support of the national health strategy: 48/68 ± 16.14 (49%), 2) the predictable financing: 50/23 ± 16.02 (50%), 3) foreign aid on budget: 55/39 ± 20.15 (55%), 4) strengthening public financial management system: 38/35 ± 19.06 (38%), 5) strengthening the supply and procurement system: 40.97 ± 19.55 (41%), 6) mutual accountability: 46.50 ± 19.26 (46%), 7) technical support and training: 50.24 ± 17.33 (50%), 8) civil society involvement: 35.24 ± 18.61(35%), 9) private sector participation: 36 ± 17.55 (36%), and in total the average score was 44.52 ± 13.27 (44%). The difference between the scores given by two groups of managers was not significant. No meaningful relationship was observed between the total score and any of the demographic variables, but there was a weak relationship between work and management experience and total score. The correlation coefficient showed a statistically significant relationship between the different dimensions of the questionnaire. To sum up, the performance in all dimensions of aid management hardly reached 50%. Donors’ support for the national health strategy was not adequate. There were challenges in evidence-based decision-making, developing national health strategies, control and evaluation, the allocation of resources and use of procurement system. The priorities of donors and government were not always similar and mutual responsibility was lacking. Technical assistance and supporting multilateral cooperation are necessary.

Originality/value

Most studies on foreign aid focused on its effects on economic growth, poverty and investment and not aid management processes. Without proper aid management, parts of resources are wasted and aims of aid programs cannot be achieved. This study investigates aid management in a developing country from the perspectives of two main stakeholders, international and national managers.

Research limitations and implications

Data collection coincided with the change of government in Afghanistan. The situation might be different now. Still, this study provides areas for the improvement of aid management in the studied country. Future studies can build upon the findings of this research and conduct in-depth exploration of areas of aid effectiveness and designing detailed programs of improvement.

Practical implications

Instructions of the Paris Declaration on Aid Effectiveness need to be followed. Particularly, civil society involvement and private sector participation should receive attention. A joint plan for improvement and collaboration of different stakeholders is needed.

Peer Review reports

Introduction

Afghanistan’s history is characterized by internal conflicts and wars which destroyed the economy and country’s infrastructures, including the healthcare system [ 1 ]. Afghanistan is highly dependent on international aid. Dependence on international aid is defined when the aid accounts for at least 10% of the Gross Domestic Product (GDP), and in the absence of this aid, the government cannot perform its main functions [ 2 ]. In 2018, the World Bank estimated that international aid constitutes nearly 40% of Afghanistan’s GDP [ 3 ].

Foreign aid has been effective in improving Afghans’ access to education and health services but still 43% of Afghans do not have access to primary health services and 55% live below the poverty line [ 4 ]. The health financing system in this country is fragile due to high out-of-pocket payment and reliance on donors [ 5 ]. The country’s health sector is financed by 72% out-of-pocket payments, 19.4% donations, 5.1% government budget and 3.5 other sources [ 6 ]. Lack of cooperation between the government and donors on how to spend the aid, political instability, low domestic production and investment, drug mafia, and illiteracy decreased the effectiveness of aid in Afghanistan [ 7 ]. The health of Afghans has improved over the past decade; however, because of poor management of health system, corruption, low quality of health services, lack of monitoring and control, the absence of a comprehensive national policy on universal health services coverage and incomplete implementation of development programs, Afghanistan has the lowest health indicators among the countries in the region [ 4 ].

In recent years, a large amount of aid has been delivered to Afghanistan. There are limited studies addressing aid management and effectiveness in this country. Studies mostly focused on the impact of aid particularly economic effects. Better processes and structures prevent waste of resources that can be used for other priorities. Since international aid plays an important and fundamental role in Afghanistan’s healthcare system, and this system is dependent on it, the way international aid is managed is of great and undeniable importance. The current study examined international aid management in the health sector of Afghanistan from the perspectives of health system managers and donors. It provides areas that require attention of policy makers to increase effectiveness.

Literature review

The main purpose of foreign aid is to reduce poverty and increase economic growth and development in recipient countries [ 8 ]. Official development assistance has increased steadily over the past years. Economic growth is a determinant of social development. Studies showed that public expenditure on health and education, and proper income distribution contributed to human development. Study by Gomanee et al. showed effects of international aid on alleviating poverty and infant mortality [ 9 ].

This is difficult to determine the real impact of foreign aid because development is a multi-dimensional issue that can be influenced by multiple stakeholders. Moreover, the methodology and scope of the assessment can bring different results. Foreign aid effectiveness has been questioned in empirical studies [ 10 ]. In some cases, foreign aid has been remarkably effective. However, there are examples of aid failure [ 11 ]. A study in African recipient countries showed that foreign aid did not influence development growth [ 12 ]. Another study on 33 aid receiving countries showed that 1% increase in the health aid share of GDP reduced the infant mortality rate by 0.18%. It suggested that the proper management of health aid in developing countries can help to improve public health in these countries [ 11 ]. Another study showed that foreign aid had positive effects on reducing poverty. Aid targeted at pro-poor programs such as agriculture, education, health and other social services has been effective [ 13 ].

Aid alone is not enough for achieving sustainable development. It can be effective in countries committed to improving public services and infrastructure and eradicating corruption [ 14 ]. Even though the foreign aid has been increased in recent years, the healthcare resources have not been enough to guarantee everyone’s access to primary healthcare. There is need for more foreign aid and national investment. The aid should be sustainable, predictable and long-lasting to support health promotion plans. The provision of aid-dependent healthcare services will be disrupted if the donors decrease or postpone the aid [ 15 ].

The impact of aid and its effectiveness can be influenced by the way aid is managed. There are many problems in the management of international aid. A large amount of the aid is not received by the recipient government and is spent on unnecessary activities, parallel programs, transaction costs, and donors’ office administration. Some aid programs do not focus on the needs and priorities of the recipient country. In addition to improving the situation of the disadvantaged groups in recipient countries, capacity and infrastructure building, and enhancing health system management, and procurement are necessary. They help health system to become independent in future and better use the resources. Some governments believe that conflicts in policymaking lead to the waste of resources. The donors do not have interest in capacity building [ 15 ]. Chung and Hwang believe that donors should not determine where and how the resources be used but collaborate with the government to assess the population needs and set the priorities [ 10 ].

A study in Syria showed that harmonization of aid and collaboration between stakeholders are perquisites of aid effectiveness. During 2016–2019, the aid to this country has not been harmonized and correlated with humanitarian needs instead aligning more with donor policies [ 16 ]. Another study in Pakistan found that foreign aid has had positive impact on health sector, although in long run, the effect was low. The reason might be that the aid has not been successful in institutional development. If the management of health system does not improve, the aid will create a debate burden [ 17 ]. In Ethiopia, the policy “one plan, one budget, one report” and foundation of country ownership and coordination of health partners, donors and governments resulted in accomplishments in healthcare [ 18 ].

Paris Declaration on International Aid Effectiveness 2005 offers a series of strategies to commit international donors to accountability and increase aid effectiveness. This document invites the developing countries to reduce poverty and improve the performance of institutions and eliminate corruption, and the donors to align with the goals of the recipient governments and cooperate with them, optimize the processes and share information to avoid duplication. Developing countries and donors should focus on the results and be accountable for them. Donors and recipient governments should take an integrated approach to aid effectiveness in policy making to improve quality of foreign aid [ 19 ].

Most studies focused on the effects of aid on economic growth, poverty and investment. The underlying assumption in Paris Declaration was that changes in process such as reducing aid fragmentation could increase the impact of aid. The Global Partnership for Effective Development Cooperation 2011 suggests the collaboration of governments, donors, private sector and civil society. Without proper management, international aid cannot help decreasing inequality and promoting development [ 20 ]. Therefore, it is necessary to study aid effectiveness, processes and management.

Data and method

Data and sample size.

This cross-sectional, descriptive and analytical study was conducted in 2022. The research population was the managers of health sector, both public and private, and international institutions based in Herat province of Afghanistan. The participants were chosen by random sampling. Due to the lack of similar studies, the sample size was determined based on Yaman’s formula and considering an error of 5% and the population size of 180, that made 110 people.

The inclusion criteria were at least two years of work experience in the health sector or international organizations. Incomplete questionnaires (more than 50% of the items have not been answered) were excluded from the study.

The data collection tool was the standard questionnaire of the International Health Partnership and Related Initiatives. It constitutes nine main dimensions, including donors’ support for the national health strategy, predictable financing, foreign aid on budget, public finance management system, procurement system, mutual accountability, technical support and training, civil society engagement and private sector participation, each of which has a number of subcategories and a total of 30 questions [ 21 ]. Due to the lack of an Afghan version of this questionnaire, it was translated to local language by two language experts. The content validity of the questionnaire was qualitatively assessed by 5 experts in the health sector in Afghanistan. The ambiguous items were corrected. The internal consistency of the questionnaire was evaluated by consulting 30 healthcare personnel. The stability, balance and homogeneity of the questions were measured through test and retest with the same people and calculating Cronbach’s alpha. The value of Cronbach’s alpha was 0.963, which is an acceptable value and shows the reliability of the tool.

Methodology

The data of this descriptive and analytical study was collected by self-administered approach. Descriptive studies (similar to this one) provide a detailed understanding of a phenomenon, while they might have limited generalizability and potential bias.

Questionnaires were presented to the study participants in person or by phone and email. All methods were performed in accordance with the relevant guidelines and regulations. In this study, the questions were scored from 1 to 5 (very poor to very good). The data was analyzed in two descriptive (mean and percentage) and analytical formats in SPSS. Independent variables were gender, education, managerial level and years of work experience. Scores given to each dimension of aid effectiveness were the dependent variables. Independent t-test (for data with normal distribution) or Mann-Whitney test (for data with non-normal distribution) were used to examine the relationships of the scores of dimensions and independent variables such as gender. Variance analysis was used to examine the relationships of scores and multivariate variables (such as education, age, work experience). Variance analysis shows the data’s volatility and consistency, which can impact the interpretations of the results. The normality of data distribution of quantitative variables was evaluated using the Kolmogorov-Smirnov test. It is used when there are two samples coming from two populations that can be different. The significance level of the tests was considered 5%.

Descriptive statistics

The average age of the study participants was 42.81 ± 8.36, the average work experience was 14.65 ± 6.06 years, and the average management experience was 10.25 ± 5. In addition, 96 people (87.3%) were men, 48.6% had a bachelor’s degree, and 41.3% a master’s degree. 73 people (67%) were middle-ranked managers. 74 people (67.3%) worked with international organizations and 85.5% completed a training course related to international aid. The knowledge of 15.1% of participants on international aid management was at average level. 64.5% of the participants received information about aid management at their workplace. 71 respondent (66.4%) studied medical and health programs (Table  1 ).

Empirical findings

The results of the survey showed that the highest scores were for foreign aid on budget (39/55 ± 20.15), technical support and training (42/50 ± 17.33), and predictable financing (23/50 ± 16.02) and the lowest score was in the field of civil society participation (35.24 ± 18.61) (Table  2 ). The performance in all dimensions of aid management hardly reached 50%.

More details about the dimensions of evaluation are provided in Table  3 . According to this table, the scores in all dimensions were in the range of 30–56. The lowest scores belonged to civil society participation. In general, the scores were very low and proved that all areas of aid management need improvement.

According to Table  4 , the managers of Afghanistan’s health sector and international organizations based in this country gave the lowest scores to the participation of civil society and the private sector in international aid programs, and the highest scores to considering the foreign aid in the budget. They had similar opinions about different dimensions of international aid management (Table  4 ).

The relationships between independent variables (gender and education) and the scores of different dimensions of aid management showed no meaningful difference. There were no changes in the dependent variable due the manipulation of these two independent variables. However, between managerial level and work experience with the scores, there was week relationship (Table  5 ).

The correlation coefficient showed that between the different dimensions of the questionnaire, there were meaningful relationships which mean the variables change together in the same direction. This indicates the strength of the linear relationship between variables. (Table  6 ).

In this cross-sectional study, international aid management in health sector of Afghanistan has been investigated from the perspective of the managers of health facilities and international organizations based in Herat province in 2022. The average age of study participants was 42.81 ± 36.8, the average work experience was 14.65 ± 6.06 years, and management experience of 10.25 ± 5.83 years. The majority of participants were men, had a bachelor’s degree and worked in middle management positions. A large number of participants worked with international organizations and mostly completed training course related to international aid management. Most of the participants were medical and health graduates. One third of them had fair knowledge about international aid management. The majority acquired the knowledge through work experience.

The managers of Afghanistan’s health system and international organizations believed that the management of international aid in health system of this country was at average level (score: 44.52 ± 13.27 (44% achievement).The performance was better in the dimension of aid on budget (55%) and the lowest was related to civil participation (36%). A study by the Organization for Economic Cooperation and Development (OECD) in 34 aid recipient countries showed that all countries were lagging behind the goals set in the Paris Declaration and needed more efforts and cooperation to improve the situation [ 22 ]. A study conducted in 2009 on the effectiveness of international aid in Afghanistan showed that the conditions in this country brough about challenges for the effectiveness of aid. These include: persistent insecurity, lack of national and international capacity, multiple and often inconsistent programs, ambiguous goals, unclear lines between military, humanitarian, and development interventions, widespread corruption, and lack of coordination among donors [ 23 ].

Donors’ support for the national health strategy was not adequate in Afghanistan (score: 50/23 ± 16.02 (50% achievement). There are challenges in developing national health strategies, control and evaluation of health services, evidence based decision-making and the use of national frameworks. A study in 2020, which investigated the impact of international aid on the growth of Afghanistan’s economy, found factors such as the non-cooperation of the Afghan government and donor countries as an obstacle to aid effectiveness. According to this study, in Afghanistan, there is neither an efficient and effective government institution, nor there are appropriate strategies on the use of international aid [ 24 ]. Similarly, the study on the international aid effectiveness in Ethiopia showed that the aid was scattered and there was no coordination between donors and the government and mutual accountability [ 25 ]. A study conducted on international aid dependence and political agreements in Afghanistan showed that aid was usually allocated based on the preferences of the donors rather than the priorities of the recipient country. Aid has largely focused on short-term goals, hindering medium- and long-term progress. Moreover, the aid may not be under the control of the recipient country [ 2 ]. Studies on foreign aid in other countries, including Nepal, showed that lack of attention to national preferences disrupted proper response to people’s needs [ 26 ].

Sometimes, the priorities are defined at global, regional or multi-country programs and often they are not completely aligned with national policies [ 27 ]. According to the World Health Organization (WHO), doners and the recipient countries might have different views on population needs [ 28 ]. Donors have different histories, experiences, and ideas that affect the projects they prefer to support. Sometimes, the lack of coordination and insularity greatly reduce the effectiveness of aid. For example, there are many international institutions and non-governmental organizations operating in Mali. Each of them has its own strategy, values, culture and work process. Acting in isolation and not integrating the goals with the national policies and structure and the lack of cooperation between the private and public sectors have reduced the effectiveness of aid in recent years [ 29 ]. In the allocation of the aid, the less considered issues are usually the goals of the recipient country [ 30 ]. The lack of coordination between donors is the most important challenge of aid management. Sustainable and effective change depends on the institutionalization of all policies at the local level [ 31 ]. A study by the African Development Bank in 2011 showed that the conflict of interests, weakness of the structures and the lack of capacity were the main challenges of international aid effectiveness. Short-term perspectives disrupt long term development plans [ 32 ].

The predictability of financing received an average score (55/39 ± 20.15 (55% achievement)) in this study which shows that the distribution of health financial resources, allocating aid based on the predetermined plans, and financing health centers through government’s long-term budget and the knowledge of the government on international donors’ programs are problematic. In a study by the Asian Development Bank in 2011, the predictability of development cooperation in Asian countries was evaluated at 78%, which was higher than Afghanistan [ 32 ]. To increase the predictability, it is necessary to have a comprehensive and transparent information system. A case study on international aid effectiveness in health sector of Ethiopia showed that no systematic and comprehensive data on the flow of aid was available [ 25 ]. In a study investigating the management of international aid in a developing country showed that transparency was an important indicator for identifying the problems, weaknesses and gaps in various areas of economic development. The study concluded that it is necessary to increase the involvement of interest groups in formulating strategies and policies [ 33 ].

According to the study participants, about 55% of international aid was placed in national budget. The donors set different strategies in this regard. For example, Italy recognizes the full ownership of the country’s health and medical institutions and gives the responsibility to implement the interventions to the local authorities in Afghanistan [ 34 ]. In contrast, spending a large part of Germany’s aid outside the Afghan government’s system has weakened the government and harmed the accountability of aid recipient institutions [ 35 ]. Similarly, conflicting programs or overlapping projects implemented by different donors reduced the effectiveness of aid according to Albanians [ 33 ]. In Africa, international aid does not flow through the government’s budget system, and is spent by non-governmental organizations or individuals. Local governments do not have enough information about the resources and projects [ 36 ]. Another study on the flow of aid in programs to fight tuberculosis, AIDS and malaria showed that there was no coherence between aid at the national level; aid was not flexible and a small part of it entered the government budget [ 27 ]. In a study that examined international aid management in Ethiopia, it was found that the government played an important role in coordinating international aid. In this country, there are specific national health programs in which the role of international aid is clear [ 25 ].

According to the respondents of this study, strengthening the financial management system of the public sector was not a priority for the donors (achieving 38% of the standard). WHO, in coordination with all key stakeholders in Afghanistan, helps to increase overall resources for health and improve the effectiveness of the investments [ 37 ]. However, the study by Dastan et al. about the determinants of financial protection in the health sector of Afghanistan showed that there was an urgent need to strengthen the overall health financing system in order to promote public health in this country [ 38 ]. Besharat Hossein reviewed the effects of international aid in Bangladesh and said the aid had little effectiveness due to the limited capacity of Bangladeshi institutions. If the government reforms its institutions and policies, foreign aid can contribute more effectively to the national economy [ 39 ]. In another study conducted by the United Nations Conference on Trade and Development (UNCTAD) on international aid allocated to less developed countries, found that donors’ financial resources can be hardly tracked due to the lack of a financial information system. The absence of transparency in spending resources reduced the donors’ trust [ 31 ]. A study in Sri Lanka showed that inefficiency of financial resources and weak institutions made foreign aid ineffective. In addition to effective policies, proper monitoring system supported by donors, and preventing the misuse of resources are needed [ 40 ].

Strengthening the supply system of the recipient country is an important part of aid management. It was scored 40.97 ± 19.55 (41% achievement). In Afghanistan, this aspect has not received enough attention. The donors’ support and use of the national procurement system need improvement. A study on the pros and cons of foreign aid in Albania indicated that donors were reluctant to use Albania’s public procurement systems. Strategic agreements between donors and the government, and forming working groups were suggested to adjust the aid flow [ 41 ]. The study of the Asian Development Bank on aid recipient countries showed that 47% of the aid flows through the public procurement systems. Further coordination between governments and donors is necessary [ 32 ]. The results of this study are similar to the present study.

In the current study, mutual responsibility of the donors and the government was not optimum (score:46.50 ± 19.26 (46% achievement)). There should be an evaluation system agreed with two parties. According to the report of the OECD, the mutual accountability in Afghanistan is a serious challenge, especially since the government and the donors insist on their own political goals, which creates an atmosphere of distrust and makes the implementation of programs difficult [ 22 ]. Asian Development Bank in 2011 indicated that countries were scored 54% in establishing mutual accountability and supporting the government in achieving its goals [ 32 ]. A study on foreign aid policy and its effect on Nepal’s growth showed that the capacity of country’s economy to implement programs was less than satisfactory due to the lack of proper information system and regular monitoring [ 42 ]. In Nigeria, the donors needed to monitor the implementation of plans and effective use of foreign aid. Without making political, economic and institutional reforms, the massive influx of foreign aid will be futile [ 43 ]. A review of foreign aid in Africa in 2012 concluded that responsible governance in this continent is a key to economic development [ 44 ].

Technical support and training help the recipient countries to better contribute in implementing the programs. Considering technical assistance in national programs and health strategies and supporting multilateral cooperation are necessary. The score of technical support in this study was 50.24 ± 17.33 (50%). The study of the Asian Development Bank showed that 45% of the donors paid attention to capacity building and education in recipient countries [ 32 ]. The Geneva Conference 2018 addressed the development of infrastructure and sustainable development in developing countries. The Kabul Conference 2010 focused on the rule of law and good governance and development. The International Monetary Fund supported establishing flexible and sustainable systems for health in Afghanistan [ 45 ]. In recent years, the spending on improving health sector management and policymaking has increased significantly. The aid focused on strengthening the health system through capacity building and planning [ 46 ]. In the absence of a proper support system, the aid is spent on daily affairs and does not lead to the transfer of technology and enhancing the capabilities of the country [ 47 ].

According to the WHO, low salaries and inappropriate working conditions discouraged the few skilled managers and entrepreneurs to participate in international aid projects in Afghanistan. The shortage of female healthcare providers is evident in this country [ 28 ]. The United States Agency for International Development (USAID) launched a midwifery training program to increase the number of female health workers and give women more access to necessary care. USAID created a system for monitoring and supported national diseases information system [ 48 ]. A study showed the need for skilled and knowledgeable managers committed to national values, and teamwork to determine priorities and establish a strong monitoring system. Unbalanced distribution of resources, lack of coordination, unnecessary costs, low efficiency and the lack of infrastructure are among the challenges of the country’s reconstruction process [ 49 ]. There have been various studies on the effectiveness of training provided by donors. The program of transferring technical skills to Afghan government employees by Germany has not been successful enough due to the lack of a monitoring system. Trained employees would not like to work in government facilities due to low wages. After acquiring the necessary skills, they are attracted to non-governmental organizations. Enhancing aid effectiveness requires a change in human resources strategies and enhancing security [ 35 ].

Civil society involvement in health sector programs and development is essential. The society should be empowered by receiving information, technical support and opportunities to participate. The Ministry of Health and the World Bank play important roles in supporting healthcare projects through non-governmental organizations [ 50 ]. However, this study showed that civil participation was not adequate (score: 35.24 ± 18.61, (35% achievement). A study in Albania concluded that the technical assistance and capacity building provided by donors and increasing the awareness of the civil society were among the benefits of aid assistance [ 41 ]. in Nepal, civil participation in country’s development is a challenge. Similar to Afghanistan, this country has religious and linguistic diversity, which together with its uneven terrain and inefficient government acts as an obstacle to national unity for growth [ 42 ]. Civil society needs information to participate in aid management. This information should be understood and analyzed by the civil society and encourage cooperation [ 51 ]. According to OECD, non-governmental organizations and the private sector are weak in developing countries. Lack of capacity hiders them to play their role in the development of the country [ 22 ].

Private sector participation received the lowest score (36 ± 17.55 (36% achievement), among different dimensions of aid management in Afghanistan. Private sector participation in the development and implementation of health sector policies needs donors’ support, information, and financial and technical assistance. The donors can achieve the goals of aid with the support of the private sector and the government. Because of people’s lack of trust to the government administrative system and the desire to achieve tangible results, the private sector compete with government organizations in attracting donated resources, but still they are depended on the support of the government. Some countries, such as the Netherlands, make financial support subject to allocating a part of the aid budget to non-governmental organizations. But, in low-income countries, this organizations do not have enough skills, information and power to cooperate with donors [ 52 ].

In recent years, the private sector has grown in Afghanistan. The government is determined to develop a solid policy framework and establish institutions and systems aimed at ensuring higher quality private services and a long-term and sustainable role for the private sector. Afghanistan is at the beginning of privatization; evidence shows that the Ministry of Health can promote a more efficient and effective private sector [ 53 ]. Based on the report of the UNCTAD, if donors cooperate with the private sector and civil society to set priorities and implement programs, the aid can be effective [ 31 ].

The performance in all dimensions of aid management hardly reached 50%. The managers of Afghanistan’s health sector and international organizations based in this country believed that international aid management in Afghanistan’s health sector needs to be improved. The standards of the Paris Declaration on Aid Effectiveness could be helpful in this regard. According to the studied managers, the best dimension of aid management was the inclusion of international aid in government budget. However, civil society involvement and the private sector participation in planning and implementing aid programs was not satisfactory.

This study showed the areas of aid management that needs improvement in Afghanistan. According to the results, in order to improve international aid management, it is necessary to improve the resources management with the cooperation of international donors, to strengthen health planning, and to develop an effective administrative and management system. Promoting transparency, accountability, and fighting against corruption are the perquisites of aid effectiveness. Economic and social development and investment in infrastructure and cooperation between the government and donors and the private sector will improve public governance. Finding ways to reduce the dependence of the health sector on international aid will be a sustainable solution. The government of Afghanistan should determine the needs of its population and direct the aid towards the priorities of the country which cannot be achieved with government budget.

Study limitations and future studies guidelines

Data collection coincided with the change of government in Afghanistan. The participants of the study stated that due to the extensive changes in administrative and management structures and unclear processes, their opinions addressed the situation before the changes in 2021. Still, this study provides areas for the improvement of aid management in the studied country. Future studies can build upon the findings of this research and conduct in-depth exploration of areas of aid effectiveness and designing detailed programs of improvement. A joint plan for improvement and collaboration of different stakeholders is needed.

Data availability

Data are not publicly available to preserve individuals’ privacy.

Abbreviations

Gross Domestic Product

Organization for Economic Cooperation and Development

United States Agency for International Development

World Health Organization

United Nations Conference on Trade and Development

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FK designed the study and supervised it; NR: collected data and wrote the report; HSH and JJ: designed methods and analysis; LA: wrote the paper;

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Rashed, N., Shabanikiya, H., Alizamani, L. et al. International aid management in Afghanistan’s health sector from the perspective of national and international managers. BMC Health Serv Res 24 , 1001 (2024). https://doi.org/10.1186/s12913-024-11260-0

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