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Data Analysis in Research: Types & Methods

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Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense.

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research.

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words.

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find “food” and “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended text analysis methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other.

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

Content Analysis: It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
Discourse Analysis: Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
Grounded Theory: When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

Fraud: To ensure an actual human being records each response to the survey or the questionnaire
Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
Procedure: To ensure ethical standards were maintained while collecting the data sample
Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

Count, Percent, Frequency
It is used to denote home often a particular event occurs.
Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

Mean, Median, Mode
The method is widely used to demonstrate distribution by various points.
Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

Range, Variance, Standard deviation
Here the field equals high/low points.
Variance standard deviation = difference between the observed score and mean
It is used to identify the spread of scores by stating intervals.
Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

Percentile ranks, Quartile ranks
It relies on standardized scores helping researchers to identify the relationship between different scores.
It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided sample without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected sample to reason that about 80-90% of people like the movie.

Here are two significant areas of inferential statistics.

Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
Cross-tabulation: Also called contingency tables, cross-tabulation is used to analyze the relationship between multiple variables. Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing audience sample il to draw a biased inference.
Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

LEARN ABOUT: Average Order Value

QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

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The Power of Percentages: An In-Depth Exploration of Percentages and their Applications

Percentages are everywhere in our daily lives, from calculating taxes to understanding probabilities. In fact, we cannot go a day without encountering a percentage in some form. Despite this, many people still struggle to understand the concept of percentages. This article seeks to provide a comprehensive overview of percentages and their applications, with a special focus on their significance in scientific research and the world of business.

First, we will define percentages and their prevalence in our daily lives. Then, we will take a brief look at the history of percentages and their origin. Following this, we will examine the use of percentages in the world of business and explain their relevance. Lastly, we will discuss the significant role of percentages in scientific research.

Through the course of this article, we aim to help the reader gain a better understanding of percentages and their applications. Whether it is calculating discounts or analyzing scientific data, percentages play a crucial role in our lives. We hope this comprehensive overview will enable the reader to comprehend the power of percentages and their impact on our world.

Understanding Percentages: The Basics

Percentages are an essential part of modern life, and their importance cannot be overstated. Simply put, a percentage is a portion of a whole, expressed as a fraction of 100. For example, a percentage can be used to express a portion of a group, such as the percentage of women in a particular profession or the percentage of homeowners in a given area.

To calculate a percentage, you divide the number in question by the total and then multiply the result by 100. For instance, if you want to know what percentage of a group is female, you would divide the number of women by the total number of people and then multiply the result by 100.

Percentages are used in countless ways in daily life. For example, they are commonly used to express grades, such as scoring a 95% on an exam. Percentages are also used in finance and economics to express interest rates and changes in the stock market.

A percentage can also be used to show a change over time, which is known as percentage change. It is calculated by dividing the difference between the two values by the original value and then multiplying the result by 100. This is an important calculation in business and finance, as it is often used to track growth or decline in a particular metric, such as sales or revenue.

It is also essential to distinguish between percentage increase and decrease. A percentage increase represents the amount by which something has grown, whereas a percentage decrease shows how much it has declined.

Understanding the basics of percentages is a crucial foundation for their application in various fields, and it is essential to master this skill for success in specific industries.

The Versatility of Percentages: Applications in Daily Life

Percentages are ubiquitous in our daily lives, with applications in finance, economics, sports, and health. Here are a few ways that percentages are used in everyday scenarios:

Finance and economics: Percentages play a crucial role in the world of business and finance. Bank loans often have a percentage as an interest rate, and credit card companies charge a percentage as interest on unpaid balances. Understanding how percentages work is vital when it comes to making informed financial decisions.
Sales, discounts, and deals: When shopping, we often come across deals and discounts that are presented as percentages. Retailers use percentages as a way to attract customers with the promise of discounts. It is important to understand how percentages work, as a deal that may seem good could end up costing more money in the long run.
Sports statistics: Percentages are regularly used in sports to analyze player performance, such as batting averages in baseball or shooting percentages in basketball. Percentages give a more accurate representation of a player's skill compared to raw numbers, as they take into account the number of attempts made.
Usage of percentages in health: Percentages play a significant role in the healthcare industry. Weight loss programs often use percentages to track progress, such as the percentage of body fat lost or the percentage of pounds lost. Percentages are also used in medication dosages, as the amount given is typically a percentage of body weight.

The Power of Percentages in Data Analysis and Statistics

Percentages play a vital role in data analysis and statistics. They provide a quick and easy way to present and compare data, which makes them ideal for understanding trends and patterns. Here, we will explore how percentages are used in data analysis and statistics.

Calculation of Percentages in Charts, Graphs, and Tables

Graphs, charts, and tables are popular ways of presenting data. Percentages are often used to compare data in these formats. They allow the data to be presented in a meaningful and straightforward way, making it easier for people to understand.

For example, a line graph can show the percentage of people who own a car in a particular state over a period. A table can show the percentage of people who prefer different ice cream flavors. A pie chart can show the percentage of sales for different products.

Use of Percentages in Polling and Survey Results

Polls and surveys are essential ways of gathering data and opinions. Percentages are used in polls and surveys to present the results in an understandable way. For example, a poll could have a question, "Who do you think will win the election?" The answers to this question can be presented as a percentage.

Suppose a survey question is "How many people prefer to work from home?" The results can be presented in the form of a percentage, giving a clear indicator of the preference of the population .

Advantages and Limitations of Using Percentages in Data Interpretation

Percentages have numerous advantages, including an easy way of communicating results, making comparisons, and understanding trends. However, there are some limitations to their use. For instance, percentages can be distorted when used in small sample sizes or when measuring subjective data such as feelings or opinions.

It is crucial to understand the limitations of percentages when interpreting data. This understanding can help us prevent biased and flawed conclusions from being drawn. At the same time, it can help us see trends and changes in data accurately.

Understanding Percentages in Business and Marketing

Percentages play a crucial role in business and marketing analytics. They are utilized to analyze sales and plan future marketing strategies.

Use of Percentages in Business and Marketing Analytics Businesses use percentages to analyze data for accurate decision-making. Percentages help in identifying the trends, patterns, and gaps in the market. By using percentages, companies can understand consumer behavior and preferences. They can create informed business strategies targeted at specific demographics.

Importance of Percentages in Sales Projections and Forecasting Percentages are vital in forecasting sales. Companies use sales forecasts to estimate future sales accurately. Accurate sales projections help a business maintain inventory levels, avoid surplus and waste, and make better financial decisions.

Impact of Percentages on Decision-Making Processes Percentages have a significant impact on decision-making processes in businesses. Companies make data-driven decisions based on the results obtained from percentages. Percentages can influence decisions regarding product improvements, market expansions, and pricing strategies.

Discussion on Ethical Use of Percentages in Advertising Percentages are often utilized in advertising to convince potential customers to purchase products or services. However, the ethical use of percentages can be debatable in some instances, such as when it comes to using false or misleading percentages in advertisements. It is crucial to ensure that the percentages used in advertising are accurate and not manipulated to deceive customers.

Conclusion: Harnessing the Power of Percentages

In conclusion, percentages are a fundamental concept in our daily lives, influencing nearly every aspect of our existence. From simple financial transactions to complex scientific discoveries, percentages play an essential role in shaping our understanding of the world around us.

Throughout this article, we have explored the history, calculation, and function of percentages, examining examples of how percentages are applicable in various scenarios. We discussed the advantages and limitations of using percentages in data interpretation, the ethical use of percentages in advertising, as well as innovations in percentage calculation and analysis.

The future of percentages looks promising, with the potential for presenting and communicating percentages through technology. As we continue to advance in our understanding of percentages, we must keep in mind the significance of their impact on society. Proper utilization of percentages can contribute positively to various industries and enable us to make more informed decisions as a society.

1. What is a percentage and why is it important?

A percentage is a way of expressing a fraction where the denominator is 100. It is prevalent in our daily lives, from calculating discounts in shopping to understanding scientific research. Percentages help us understand proportions and changes in values.

2. What is the difference between percentage increase and decrease?

Percentage increase refers to the amount that a value has grown in comparison to its initial value, while percentage decrease refers to the amount that a value has shrunk in comparison to its initial value. They are calculated using the percentage change formula.

3. How are percentages used in data analysis?

Percentages can provide insights into trends and patterns in data analysis. They are used to calculate proportions and rates, and to compare different groups or categories in a dataset. Percentages are often presented in charts, graphs, and tables to make the data more accessible and informative.

4. How are percentages used in marketing and advertising?

Percentages are important in marketing analytics as they help to measure the success of marketing campaigns and predict future sales. They are used to calculate conversion rates, click-through rates, and customer acquisition rates. However, it is important to use percentages ethically and not mislead customers with false advertising claims.

5. What is the future outlook for percentages?

The future of percentages is likely to involve innovation in calculation and analysis methods, as well as the use of technology to improve visualization and communication of percentage data. With the increasing importance of data in decision-making processes, percentages will continue to play a crucial role in understanding and interpreting numerical information.

June 22, 2023

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14 Quantitative analysis: Descriptive statistics

Numeric data collected in a research project can be analysed quantitatively using statistical tools in two different ways. Descriptive analysis refers to statistically describing, aggregating, and presenting the constructs of interest or associations between these constructs. Inferential analysis refers to the statistical testing of hypotheses (theory testing). In this chapter, we will examine statistical techniques used for descriptive analysis, and the next chapter will examine statistical techniques for inferential analysis. Much of today’s quantitative data analysis is conducted using software programs such as SPSS or SAS. Readers are advised to familiarise themselves with one of these programs for understanding the concepts described in this chapter.

Data preparation

In research projects, data may be collected from a variety of sources: postal surveys, interviews, pretest or posttest experimental data, observational data, and so forth. This data must be converted into a machine-readable, numeric format, such as in a spreadsheet or a text file, so that they can be analysed by computer programs like SPSS or SAS. Data preparation usually follows the following steps:

Data coding. Coding is the process of converting data into numeric format. A codebook should be created to guide the coding process. A codebook is a comprehensive document containing a detailed description of each variable in a research study, items or measures for that variable, the format of each item (numeric, text, etc.), the response scale for each item (i.e., whether it is measured on a nominal, ordinal, interval, or ratio scale, and whether this scale is a five-point, seven-point scale, etc.), and how to code each value into a numeric format. For instance, if we have a measurement item on a seven-point Likert scale with anchors ranging from ‘strongly disagree’ to ‘strongly agree’, we may code that item as 1 for strongly disagree, 4 for neutral, and 7 for strongly agree, with the intermediate anchors in between. Nominal data such as industry type can be coded in numeric form using a coding scheme such as: 1 for manufacturing, 2 for retailing, 3 for financial, 4 for healthcare, and so forth (of course, nominal data cannot be analysed statistically). Ratio scale data such as age, income, or test scores can be coded as entered by the respondent. Sometimes, data may need to be aggregated into a different form than the format used for data collection. For instance, if a survey measuring a construct such as ‘benefits of computers’ provided respondents with a checklist of benefits that they could select from, and respondents were encouraged to choose as many of those benefits as they wanted, then the total number of checked items could be used as an aggregate measure of benefits. Note that many other forms of data—such as interview transcripts—cannot be converted into a numeric format for statistical analysis. Codebooks are especially important for large complex studies involving many variables and measurement items, where the coding process is conducted by different people, to help the coding team code data in a consistent manner, and also to help others understand and interpret the coded data.

Data entry. Coded data can be entered into a spreadsheet, database, text file, or directly into a statistical program like SPSS. Most statistical programs provide a data editor for entering data. However, these programs store data in their own native format—e.g., SPSS stores data as .sav files—which makes it difficult to share that data with other statistical programs. Hence, it is often better to enter data into a spreadsheet or database where it can be reorganised as needed, shared across programs, and subsets of data can be extracted for analysis. Smaller data sets with less than 65,000 observations and 256 items can be stored in a spreadsheet created using a program such as Microsoft Excel, while larger datasets with millions of observations will require a database. Each observation can be entered as one row in the spreadsheet, and each measurement item can be represented as one column. Data should be checked for accuracy during and after entry via occasional spot checks on a set of items or observations. Furthermore, while entering data, the coder should watch out for obvious evidence of bad data, such as the respondent selecting the ‘strongly agree’ response to all items irrespective of content, including reverse-coded items. If so, such data can be entered but should be excluded from subsequent analysis.

Data transformation. Sometimes, it is necessary to transform data values before they can be meaningfully interpreted. For instance, reverse coded items—where items convey the opposite meaning of that of their underlying construct—should be reversed (e.g., in a 1-7 interval scale, 8 minus the observed value will reverse the value) before they can be compared or combined with items that are not reverse coded. Other kinds of transformations may include creating scale measures by adding individual scale items, creating a weighted index from a set of observed measures, and collapsing multiple values into fewer categories (e.g., collapsing incomes into income ranges).

Univariate analysis

Univariate analysis—or analysis of a single variable—refers to a set of statistical techniques that can describe the general properties of one variable. Univariate statistics include: frequency distribution, central tendency, and dispersion. The frequency distribution of a variable is a summary of the frequency—or percentages—of individual values or ranges of values for that variable. For instance, we can measure how many times a sample of respondents attend religious services—as a gauge of their ‘religiosity’—using a categorical scale: never, once per year, several times per year, about once a month, several times per month, several times per week, and an optional category for ‘did not answer’. If we count the number or percentage of observations within each category—except ‘did not answer’ which is really a missing value rather than a category—and display it in the form of a table, as shown in Figure 14.1, what we have is a frequency distribution. This distribution can also be depicted in the form of a bar chart, as shown on the right panel of Figure 14.1, with the horizontal axis representing each category of that variable and the vertical axis representing the frequency or percentage of observations within each category.

With very large samples, where observations are independent and random, the frequency distribution tends to follow a plot that looks like a bell-shaped curve—a smoothed bar chart of the frequency distribution—similar to that shown in Figure 14.2. Here most observations are clustered toward the centre of the range of values, with fewer and fewer observations clustered toward the extreme ends of the range. Such a curve is called a normal distribution .

Lastly, the mode is the most frequently occurring value in a distribution of values. In the previous example, the most frequently occurring value is 15, which is the mode of the above set of test scores. Note that any value that is estimated from a sample, such as mean, median, mode, or any of the later estimates are called a statistic .

Bivariate analysis

Bivariate analysis examines how two variables are related to one another. The most common bivariate statistic is the bivariate correlation —often, simply called ‘correlation’—which is a number between -1 and +1 denoting the strength of the relationship between two variables. Say that we wish to study how age is related to self-esteem in a sample of 20 respondents—i.e., as age increases, does self-esteem increase, decrease, or remain unchanged?. If self-esteem increases, then we have a positive correlation between the two variables, if self-esteem decreases, then we have a negative correlation, and if it remains the same, we have a zero correlation. To calculate the value of this correlation, consider the hypothetical dataset shown in Table 14.1.

After computing bivariate correlation, researchers are often interested in knowing whether the correlation is significant (i.e., a real one) or caused by mere chance. Answering such a question would require testing the following hypothesis:

$H_0:\quad r = 0$

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What Is Statistical Analysis?

Statistical analysis is a technique we use to find patterns in data and make inferences about those patterns to describe variability in the results of a data set or an experiment.

In its simplest form, statistical analysis answers questions about:

Quantification — how big/small/tall/wide is it?
Variability — growth, increase, decline
The confidence level of these variabilities

What Are the 2 Types of Statistical Analysis?

Descriptive Statistics: Descriptive statistical analysis describes the quality of the data by summarizing large data sets into single measures.
Inferential Statistics: Inferential statistical analysis allows you to draw conclusions from your sample data set and make predictions about a population using statistical tests.

What’s the Purpose of Statistical Analysis?

Using statistical analysis, you can determine trends in the data by calculating your data set’s mean or median. You can also analyze the variation between different data points from the mean to get the standard deviation . Furthermore, to test the validity of your statistical analysis conclusions, you can use hypothesis testing techniques, like P-value, to determine the likelihood that the observed variability could have occurred by chance.

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Statistical Analysis Methods

There are two major types of statistical data analysis: descriptive and inferential.

Descriptive Statistical Analysis

Descriptive statistical analysis describes the quality of the data by summarizing large data sets into single measures.

Within the descriptive analysis branch, there are two main types: measures of central tendency (i.e. mean, median and mode) and measures of dispersion or variation (i.e. variance , standard deviation and range).

For example, you can calculate the average exam results in a class using central tendency or, in particular, the mean. In that case, you’d sum all student results and divide by the number of tests. You can also calculate the data set’s spread by calculating the variance. To calculate the variance, subtract each exam result in the data set from the mean, square the answer, add everything together and divide by the number of tests.

Inferential Statistics

On the other hand, inferential statistical analysis allows you to draw conclusions from your sample data set and make predictions about a population using statistical tests.

There are two main types of inferential statistical analysis: hypothesis testing and regression analysis. We use hypothesis testing to test and validate assumptions in order to draw conclusions about a population from the sample data. Popular tests include Z-test, F-Test, ANOVA test and confidence intervals . On the other hand, regression analysis primarily estimates the relationship between a dependent variable and one or more independent variables. There are numerous types of regression analysis but the most popular ones include linear and logistic regression .

Statistical Analysis Steps

In the era of big data and data science, there is a rising demand for a more problem-driven approach. As a result, we must approach statistical analysis holistically. We may divide the entire process into five different and significant stages by using the well-known PPDAC model of statistics: Problem, Plan, Data, Analysis and Conclusion.

statistical analysis chart of the statistical cycle. The chart is in the shape of a circle going clockwise starting with one and going up to five. Each number corresponds to a brief description of that step in the PPDAC cylce. The circle is gray with blue number. Step four is orange.

In the first stage, you define the problem you want to tackle and explore questions about the problem.

Next is the planning phase. You can check whether data is available or if you need to collect data for your problem. You also determine what to measure and how to measure it.

The third stage involves data collection, understanding the data and checking its quality.

4. Analysis

Statistical data analysis is the fourth stage. Here you process and explore the data with the help of tables, graphs and other data visualizations. You also develop and scrutinize your hypothesis in this stage of analysis.

5. Conclusion

The final step involves interpretations and conclusions from your analysis. It also covers generating new ideas for the next iteration. Thus, statistical analysis is not a one-time event but an iterative process.

Statistical Analysis Uses

Statistical analysis is useful for research and decision making because it allows us to understand the world around us and draw conclusions by testing our assumptions. Statistical analysis is important for various applications, including:

Statistical quality control and analysis in product development
Clinical trials
Customer satisfaction surveys and customer experience research
Marketing operations management
Process improvement and optimization
Training needs

More on Statistical Analysis From Built In Experts Intro to Descriptive Statistics for Machine Learning

Benefits of Statistical Analysis

Here are some of the reasons why statistical analysis is widespread in many applications and why it’s necessary:

Understand Data

Statistical analysis gives you a better understanding of the data and what they mean. These types of analyses provide information that would otherwise be difficult to obtain by merely looking at the numbers without considering their relationship.

Find Causal Relationships

Statistical analysis can help you investigate causation or establish the precise meaning of an experiment, like when you’re looking for a relationship between two variables.

Make Data-Informed Decisions

Businesses are constantly looking to find ways to improve their services and products . Statistical analysis allows you to make data-informed decisions about your business or future actions by helping you identify trends in your data, whether positive or negative.

Determine Probability

Statistical analysis is an approach to understanding how the probability of certain events affects the outcome of an experiment. It helps scientists and engineers decide how much confidence they can have in the results of their research, how to interpret their data and what questions they can feasibly answer.

You’ve Got Questions. Our Experts Have Answers. Confidence Intervals, Explained!

What Are the Risks of Statistical Analysis?

Statistical analysis can be valuable and effective, but it’s an imperfect approach. Even if the analyst or researcher performs a thorough statistical analysis, there may still be known or unknown problems that can affect the results. Therefore, statistical analysis is not a one-size-fits-all process. If you want to get good results, you need to know what you’re doing. It can take a lot of time to figure out which type of statistical analysis will work best for your situation .

Thus, you should remember that our conclusions drawn from statistical analysis don’t always guarantee correct results. This can be dangerous when making business decisions. In marketing , for example, we may come to the wrong conclusion about a product . Therefore, the conclusions we draw from statistical data analysis are often approximated; testing for all factors affecting an observation is impossible.

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StatPearls [Internet].

Exploratory data analysis: frequencies, descriptive statistics, histograms, and boxplots.

Jacob Shreffler ; Martin R. Huecker .

Affiliations

Last Update: November 3, 2023 .

Definition/Introduction

Researchers must utilize exploratory data techniques to present findings to a target audience and create appropriate graphs and figures. Researchers can determine if outliers exist, data are missing, and statistical assumptions will be upheld by understanding data. Additionally, it is essential to comprehend these data when describing them in conclusions of a paper, in a meeting with colleagues invested in the findings, or while reading others’ work.

Issues of Concern

This comprehension begins with exploring these data through the outputs discussed in this article. Individuals who do not conduct research must still comprehend new studies, and knowledge of fundamentals in analyzing data and interpretation of histograms and boxplots facilitates the ability to appraise recent publications accurately. Without this familiarity, decisions could be implemented based on inaccurate delivery or interpretation of medical studies.

Frequencies and Descriptive Statistics

Effective presentation of study results, in presentation or manuscript form, typically starts with frequencies and descriptive statistics (ie, mean, medians, standard deviations). One can get a better sense of the variables by examining these data to determine whether a balanced and sufficient research design exists. Frequencies also inform on missing data and give a sense of outliers (will be discussed below).

Luckily, software programs are available to conduct exploratory data analysis. For this chapter, we will be examining the following research question.

RQ: Are there differences in drug life (length of effect) for Drug 23 based on the administration site?

A more precise hypothesis could be: Is drug 23 longer-lasting when administered via site A compared to site B?

To address this research question, exploratory data analysis is conducted. First, it is essential to start with the frequencies of the variables. To keep things simple, only variables of minutes (drug life effect) and administration site (A vs B) are included. See Image. Figure 1 for outputs for frequencies.

Figure 1 shows that the administration site appears to be a balanced design with 50 individuals in each group. The excerpt for minutes frequencies is the bottom portion of Figure 1 and shows how many cases fell into each time frame with the cumulative percent on the right-hand side. In examining Figure 1, one suspiciously low measurement (135) was observed, considering time variables. If a data point seems inaccurate, a researcher should find this case and confirm if this was an entry error. For the sake of this review, the authors state that this was an entry error and should have been entered 535 and not 135. Had the analysis occurred without checking this, the data analysis, results, and conclusions would have been invalid. When finding any entry errors and determining how groups are balanced, potential missing data is explored. If not responsibly evaluated, missing values can nullify results.

After replacing the incorrect 135 with 535, descriptive statistics, including the mean, median, mode, minimum/maximum scores, and standard deviation were examined. Output for the research example for the variable of minutes can be seen in Figure 2. Observe each variable to ensure that the mean seems reasonable and that the minimum and maximum are within an appropriate range based on medical competence or an available codebook. One assumption common in statistical analyses is a normal distribution. Image . Figure 2 shows that the mode differs from the mean and the median. We have visualization tools such as histograms to examine these scores for normality and outliers before making decisions.

Histograms are useful in assessing normality, as many statistical tests (eg, ANOVA and regression) assume the data have a normal distribution. When data deviate from a normal distribution, it is quantified using skewness and kurtosis. [1] Skewness occurs when one tail of the curve is longer. If the tail is lengthier on the left side of the curve (more cases on the higher values), this would be negatively skewed, whereas if the tail is longer on the right side, it would be positively skewed. Kurtosis is another facet of normality. Positive kurtosis occurs when the center has many values falling in the middle, whereas negative kurtosis occurs when there are very heavy tails. [2]

Additionally, histograms reveal outliers: data points either entered incorrectly or truly very different from the rest of the sample. When there are outliers, one must determine accuracy based on random chance or the error in the experiment and provide strong justification if the decision is to exclude them. [3] Outliers require attention to ensure the data analysis accurately reflects the majority of the data and is not influenced by extreme values; cleaning these outliers can result in better quality decision-making in clinical practice. [4] A common approach to determining if a variable is approximately normally distributed is converting values to z scores and determining if any scores are less than -3 or greater than 3. For a normal distribution, about 99% of scores should lie within three standard deviations of the mean. [5] Importantly, one should not automatically throw out any values outside of this range but consider it in corroboration with the other factors aforementioned. Outliers are relatively common, so when these are prevalent, one must assess the risks and benefits of exclusion. [6]

Image . Figure 3 provides examples of histograms. In Figure 3A, 2 possible outliers causing kurtosis are observed. If values within 3 standard deviations are used, the result in Figure 3B are observed. This histogram appears much closer to an approximately normal distribution with the kurtosis being treated. Remember, all evidence should be considered before eliminating outliers. When reporting outliers in scientific paper outputs, account for the number of outliers excluded and justify why they were excluded.

Boxplots can examine for outliers, assess the range of data, and show differences among groups. Boxplots provide a visual representation of ranges and medians, illustrating differences amongst groups, and are useful in various outlets, including evidence-based medicine. [7] Boxplots provide a picture of data distribution when there are numerous values, and all values cannot be displayed (ie, a scatterplot). [8] Figure 4 illustrates the differences between drug site administration and the length of drug life from the above example.

Image . Figure 4 shows differences with potential clinical impact. Had any outliers existed (data from the histogram were cleaned), they would appear outside the line endpoint. The red boxes represent the middle 50% of scores. The lines within each red box represent the median number of minutes within each administration site. The horizontal lines at the top and bottom of each line connected to the red box represent the 25th and 75th percentiles. In examining the difference boxplots, an overlap in minutes between 2 administration sites were observed: the approximate top 25 percent from site B had the same time noted as the bottom 25 percent at site A. Site B had a median minute amount under 525, whereas administration site A had a length greater than 550. If there were no differences in adverse reactions at site A, analysis of this figure provides evidence that healthcare providers should administer the drug via site A. Researchers could follow by testing a third administration site, site C. Image . Figure 5 shows what would happen if site C led to a longer drug life compared to site A.

Figure 5 displays the same site A data as Figure 4, but something looks different. The significant variance at site C makes site A’s variance appear smaller. In order words, patients who were administered the drug via site C had a larger range of scores. Thus, some patients experience a longer half-life when the drug is administered via site C than the median of site A; however, the broad range (lack of accuracy) and lower median should be the focus. The precision of minutes is much more compacted in site A. Therefore, the median is higher, and the range is more precise. One may conclude that this makes site A a more desirable site.

Clinical Significance

Ultimately, by understanding basic exploratory data methods, medical researchers and consumers of research can make quality and data-informed decisions. These data-informed decisions will result in the ability to appraise the clinical significance of research outputs. By overlooking these fundamentals in statistics, critical errors in judgment can occur.

Nursing, Allied Health, and Interprofessional Team Interventions

All interprofessional healthcare team members need to be at least familiar with, if not well-versed in, these statistical analyses so they can read and interpret study data and apply the data implications in their everyday practice. This approach allows all practitioners to remain abreast of the latest developments and provides valuable data for evidence-based medicine, ultimately leading to improved patient outcomes.

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Exploratory Data Analysis Figure 1 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Exploratory Data Analysis Figure 2 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Exploratory Data Analysis Figure 3 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Exploratory Data Analysis Figure 4 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Exploratory Data Analysis Figure 5 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

Cite this Page Shreffler J, Huecker MR. Exploratory Data Analysis: Frequencies, Descriptive Statistics, Histograms, and Boxplots. [Updated 2023 Nov 3]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Digital SAT Math

Course: digital sat math > unit 3, percentages | lesson.

Percents — Basic example
Percents — Harder example
Percentages: foundations

What are percentages?

Calculate percentages using part and whole values
Switch between equivalent forms of percentages
Calculate percent change

How do we calculate percentages?

Finding a percentage, calculating a percent value.

The part is 21 ‍ .
The whole is 24 ‍ .
The whole is 8 ‍ .
The percentage is 150 ‍ .
The part is what we need to find.

Finding complementary percentages

56 % ‍ of the marbles are blue.
So, 100 % − 56 % = 44 % ‍ the marbles are red.
45 ‍ is the part/whole/percentage .
117 ‍ is the part/whole/percentage .
We need to solve for the part/whole/percentage .
Your answer should be
an integer, like 6 ‍
a simplified proper fraction, like 3 / 5 ‍
a simplified improper fraction, like 7 / 4 ‍
a mixed number, like 1 3 / 4 ‍
an exact decimal, like 0.75 ‍
a multiple of pi, like 12 pi ‍ or 2 / 3 pi ‍

What forms can percentages take?

Converting percentages to decimals and fractions, switching between forms of percentages.

The ratio 50 : 100 ‍ , which reduces to 1 : 2 ‍ .
The fraction 50 100 ‍ , which reduces to 1 2 ‍ .
The decimal value 0.5 ‍ .

Translating percentage word problems

"what" means x ‍
"is" means = ‍
"of" means multiplied by
"percent" means divided by 100 ‍

In what form should I enter my answer?

a proper fraction, like 1 / 2 ‍ or 6 / 10 ‍
an improper fraction, like 10 / 7 ‍ or 14 / 8 ‍

How do we calculate percent changes?

Percentage word problems, calculating percent change.

Find the difference between the initial and final values.
Divide the difference by the initial value.
Convert the decimal to a percentage by multiplying the quotient by 100 ‍ .
$ 40 ‍ is the final value .
20 ‍ is the % ‍ change.
We're solving for the initial value ( x ‍ ).
63.89 ‍ is the initial/final/percent change .
68.86 ‍ is the initial/final/percent change .
We need to solve for the initial/final/percent change .
(Choice A) 362 ‍ A 362 ‍
(Choice B) 414 ‍ B 414 ‍
(Choice C) 468 ‍ C 468 ‍
(Choice D) 512 ‍ D 512 ‍
(Choice A) 3 ‍ A 3 ‍
(Choice B) 5 ‍ B 5 ‍
(Choice C) 8 ‍ C 8 ‍
(Choice D) 15 ‍ D 15 ‍
(Choice A) 3 % ‍ A 3 % ‍
(Choice B) 30 % ‍ B 30 % ‍
(Choice C) 43 % ‍ C 43 % ‍
(Choice D) 70 % ‍ D 70 % ‍

Things to remember

Convert the resulting decimal to a percentage.

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The Method of Data Analysis section outlines exactly which statistic will be used to answer each Research Question and/or Research Hypothesis. To complete this section, refer to the Research Questions and Research Hypotheses. For every research question, describe the descriptive statistic that is appropriate for answering the question. For every research hypothesis, describe the inferential statistic that is appropriate for analyzing the hypothesis. For simple statistics (e.g., percentage, mean, t-test), it is possible to also give the formula for the statistic. However, for more advanced statistics such as ANCOVA, the statistic is much too complex to describe the formula. The following general guidelines should help you determine which statistic is appropriate for each research question and hypothesis.

Note that in many research studies, a range of different statistics will be necessary. This means that researchers should examine each research question and hypothesis separately to consider which statistic is appropriate.

Research questions are always answered with a descriptive statistic: generally either percentage or mean. Percentage is appropriate when it is important to know how many of the participants gave a particular answer. Generally, percentage is reported when the responses have discrete categories. This means that the responses fall in different categories, such as female or male, Christian or Muslim, and smoker or non-smoker. Sometimes frequencies are also reported when the data has discrete categories. However, percentages are easier to understand than frequencies because the percentage can be interpreted as follows. Imagine there were exactly 100 cases in the sample. How many cases out of those 100 would fall in that category?

The mean is reported when it is important to understand the typical response of all the participants. Generally, mean is reported when the responses are continuous. This means that the data has numbers that continue from one point to the last point. For example, age is continuous because it can range from 0 to 100 or so. Scores on an exam are also continuous. In these cases, the mean describes the typical score across all participants.

Whenever a research hypothesis uses the word "relationship," it generally means that a correlation will be calculated. The correlation statistic examines the relationship between two continuous variables within the same group of participants. For example, the correlation would quantify the relationship between academic achievement and achievement motivation. The null hypothesis of a correlation is stated as "there is no significant relationship between academic achievement and achievement motivation."

When calculating the correlation, it is important to not just calculate the correlation, but also the significance of the correlation. The p-value determines whether the relationship is significant. If the p-value is greater than 0.05, then the null hypothesis is retained: there is indeed no relationship between the two variables. Since no significant relationship exists between the variables, then no further interpretation is necessary. If the p-value is less than 0.05, then the null hypothesis is rejected, meaning that there is a significant relationship between the two variables. (Read below for more information about interpreting the significance of a p-value.) The correlation (symbolized as r ) then can be interpreted.

The correlation has two dimensions. The direction of the correlation is indicated by the sign of the correlation. If the correlation is positive, that means that as one variable increases, the other variable also increases. The greater the achievement motivation, the greater the academic achievement. However, a negative correlation means that as one variable increases, the other variable decreases. The more time a person spends watching television, the lower their academic achievement

The second dimension of a correlation is its strength . The strength of the correlation is indicated by the absolute value of the number (i.e., the value of the number itself without the positive or negative sign). The closer the absolute value is to 1, the stronger the relationship, while the closer the absolute value is to 0, the weaker the relationship. For example, a correlation of -0.71 and 0.87 are both strong correlations while correlations of -0.18 and 0.09 are both weak correlations

When the term "relationship" is used in a research hypothesis, sometimes a chi-square statistic may be calculated. Chi-square should be used when both of the variables are discrete, meaning that both variables are represented by categories, not numbers. For example, a chi-square would be used to determine if there is a relationship between gender and smoking status. Gender can only be represented as categories (male and female) as well as smoking status (smoker and non-smoker). However, most of the time, chi-square is misused. Some researchers will group participants into categories based on numerical data, such as taking academic achievement and grouping students into "high achievement" and "low achievement" categories based on their numerical scores on an examination. This is not correct. It is much better to keep the original scores on the exam and calculate a correlation, because it keeps the data in its original form. Researchers are more likely to get a significant result when original data is used, instead of grouping participants into artificial categories.

When a research hypothesis looks at the "effect of a treatment" or "difference between groups," then there are three possible statistics that can be used. The specific statistic depends on the research design. First, consider whether the study will administer the instrument once or twice (e.g., pre-post test experimental or quasi-experimental design). If the study will use a pre-post test design, then an Analysis of Covariance (ANCOVA) should be used. If the instrument will only be administered once, then consider how many groups will be used in the study (either treatment/control group or various groups for the causal-comparative design). If there will be only two groups, then a t-test should be used to compare the two groups. If there will be three or more groups, then the Analysis of Variance (ANOVA) should be used. More details for each of the statistics are given below. Also read more about the theory behind p-values to help you understand what this statistic means.

t -test When comparing two groups on one dependent variable, a t-test should be used. For example, use a t-test to compare a treatment group to a control group or to compare males and females.

One-way ANOVA: A one-way ANOVA compares multiple groups on the same variable. For example, a one-way ANOVA would be used to compare the achievement motivation of students in JS1, JS2, and JS3.
Factorial ANOVA: The factorial ANOVA compares the effect of multiple independent variables on one dependent variable. For example, a 2x3 factorial ANOVA could compare the effects of gender and grade level on achievement motivation. The first independent variable, gender, has two levels (male and female) and the second independent variable, class, has three levels (JS1, JS2, and JS3). This makes the factorial ANOVA a 2x3. Another study might have three treatment groups and three grade levels. Because the independent variables each have three levels, it would be a 3x3 ANOVA.

ANCOVA When using a pre-post test research design, the Analysis of Covariance allows a comparison of post-test scores with pre-test scores factored out. For example, if comparing a treatment and control group on achievement motivation with a pre-post test design, the ANCOVA will compare the treatment and control groups' post-test scores by statistically setting the pre-test scores as being equal.

Any of the statistics used to answer research hypotheses are called inferential statistics (correlation, chi-square, t-test, ANOVA, and ANCOVA). Educational researchers can never sample the entire population. Instead, a sample is chosen to represent the population. However, the researcher still wants to draw conclusions about the entire population even though only a sample actually participated in the study. In other words, the researcher wants to make inferences about the population based on the results from the sample. The purpose of inferential statistics is to determine whether the findings from the sample can generalize to the entire population, or whether the findings were simply the result of chance.

Imagine a room full of socks - socks from the floor to the ceiling, from the back of the room clear to the front door. You want to determine whether there are more white socks than green socks in the room. However, there are too many socks to count, so you decide to take a sample of socks. You count the number of white and green socks in the sample. Then, you would like to draw a conclusion about whether there are more white socks in the entire room based on your sample. The purpose of inferential statistics is to determine whether the colors chosen in the sample likely reflect the entire room or if your results from the sample of socks were due to chance.

What factors will determine whether the sample of socks adequately represents the entire room? First, the size of the sample. If only two socks were picked, they would very likely not represent the entire room. The larger the sample is, the more representative the sample will be of the entire room and the more accurate the conclusions will be for the entire room. This is why when conducting experiments, a larger sample is generally better (although not always). With large samples, the results will more likely reflect the entire population

The second factor that determines whether the sample of socks adequately represents the entire room is the actual size of the difference between white and green socks in the entire room. If there are only two more white socks than green socks in the entire room, then it will be very difficult to find a significant difference between white and green socks in the sample. In other words, because there is only a very small difference between green and white socks in reality, it will be practically impossible to find a significant difference in the sample. On the other hand, if there are thousands more white socks than green socks in the entire room, it should be relatively easy to find a significant difference in the sample. This means than when you are conducting a research study, try to ensure that there really might be a large difference between groups in reality. Otherwise, you will not find significant results. If conducting an experimental design, plan very well to make the treatment very effective. Very effective treatments result in a large changes in the dependent variable and increase the chance of finding a significant difference in the study. This is also why large sample sizes are not always best: if the sample size is too large, the treatment might not be very effective, which will decrease the chance of getting a significant result.

Another way of thinking about significance testing is this: imagine you wanted to determine if there was a difference between males and females in science achievement. To do this, you administer a science achievement test to 50 males and 50 females. Then you calculate the mean (average) science achievement score for the males and the mean (average) science achievement score for the females. It is practically impossible for the mean scores to be exactly identical. In other words, there will always be at least some small difference between the groups. However, this difference may be very small: perhaps the mean score for the males is 50.21 (out of 100) while the mean score for the females is 50.25. Yes, there is a difference between males and females. However, is this difference large enough to be significant, a meaningful difference? The inferential statistic will determine whether this difference is large enough to conclude that yes, the difference is significant and there is a meaningful difference between males and females in science achievement.

For the t-test, ANOVA, and ANCOVA, four statistics are important to report. First, the p-value determines whether the differences between the groups are significant. If the p-value is less than 0.05, then we say that the differences are significant and the null hypothesis can be rejected. For example, if the null hypothesis was that there is no significant difference between males and females on achievement motivation and the p-value is 0.02, then we reject the null hypothesis and say there is a significant difference between males and females in achievement motivation. However, if the p-value is greater than .05, then the statistic is not significant. This means the null hypothesis is retained: indeed, there is no difference between males and females in achievement motivation.

When reporting the p-value, the value of t (for t -test) or F (for ANOVA and ANCOVA) and the number of degrees of freedom must also be included. The mean scores and standard deviation for each of the groups on the dependent variable must also be reported, which helps the reader to interpret which group has the highest average on the dependent variable.

Any of the previously mentioned statistics can be calculated using the VassarStats website for free.

2.5: Percentage Frequency Distribution

Chapter 1: understanding statistics, chapter 2: summarizing and visualizing data, chapter 3: measure of central tendency, chapter 4: measures of variation, chapter 5: measures of relative standing, chapter 6: probability distributions, chapter 7: estimates, chapter 8: distributions, chapter 9: hypothesis testing, chapter 10: analysis of variance, chapter 11: correlation and regression, chapter 12: statistics in practice.

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Consider a relative frequency distribution table of hockey players with different heights. This table provides information about the fraction, or proportion, of data values under each class.

If this relative frequency is expressed in terms of percentage, it is called the percentage frequency distribution.

Suppose one is interested in the percentage of players with heights between 152 and 157 centimeters. To find out, multiply the corresponding relative frequency with 100 to get the percentage frequency. This indicates that 5 percent of players fall within the required height range.

Repeat the similar calculation for all the other relative frequencies to obtain the percentage frequencies under each class. Generally, the sum of all the percentage frequencies is equal to 100.

A percentage frequency distribution, in general, is a display of data that indicates the percentage of observations for each data point or grouping of data points. It is a commonly used method for expressing the relative frequency of survey responses and other data. The percentage frequency distributions are often displayed as bar graphs, pie charts, or tables.

The process of making a percentage frequency distribution involves the following few steps: note the total number of observations; count the total number of observations within each data point or grouping of data points; and finally, divide the total observations within each data point or grouping of data points by the total number of observations. However, it is to be noted that whenever the percentage frequencies are used in the relative frequency distribution, it is also sometimes termed as percentage frequency distribution.

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Percentages

Index numbers and indexes, means and medians.

One of the most frequent ways to represent statistics is by percentage. Percent simply means "per hundred" and the symbol used to express percentage is %. One percent (or 1%) is one hundredth of the total or whole and is therefore calculated by dividing the total or whole number by 100.

Example: 1% of 250 = (1 ÷ 100) x 250 = 2.5

To calculate a given percentage of a number, divide the total number by 100 and then multiply the result by the requested percentage:

Example: 12% of 250 = (250 ÷ 100) x 12 = 30

To calculate what percentage one number is of another number, change this equation around and multiply the first number by 100 and then divide the result by the second number:

Example: 30 as a % of 250 = (30 x 100) ÷ 250 = 12%

To determine a percentage of the total from a series of numbers, add the numbers in the series to find the total (i.e. the number equal to 100%) and carry out the above calculation for each number in the series:

Example: Given the series 30,150,70: The total would be 30 + 150 + 70 = 250 30 as a % of 250 = (30 x 100) ÷ 250 = 12% 150 as a % of 250 = (150 x 100) ÷ 250 = 60% 70 as a % of 250 = (70 x 100) ÷ 250 = 28% If the percentages for each number in the series are added together, they equal the percentage for the whole: 12% + 60% + 28% = 100%

To calculate the percentage difference between two numbers, the same basic calculations are used.

Example: To find the percentage change from 250 to 280, the difference between numbers is calculated:

280 – 250 = 30

and then expressed as a percentage of the first, or base, number:

(30 x 100) ÷ 250 = 12%

To determine the whole number (i.e. the value of 100%) when only the value of a given percentage:

Example: If 280 is known to be 112% then 1% must be 280 ÷ 112 = 2.5 and 100% must be (280 x 100) ÷ 112 = 250

To compare a number of different things, they need to be expressed on the same base:

Example: if the price of sausage increased from $2.99 per kilogram to $3.99 and the same quantity of wieners from $1.99 to $2.99, the two increases could be expressed as percentages.

Sausages: $3.99 - $2.99 = $1.00 $1.00 as a % of $2.99 is ($1.00 x 100) ÷ $2.99 = 33% Wieners: $2.99 – $1.99 = $1.00

$1.00 as a % of $1.99 is ($1.00 x 100) ÷ $1.99 = 50%

It is now easy to see that the price increase of wieners was much higher than that of sausages.

It should be remembered that comparing percentages which have significantly different bases can create a false impression.

Example: The change from one to two is 100% whereas the change from 5,000,000 to 6,000,000 is only 20%.

Index numbers are a statistician's way of expressing the difference between two measurements by designating one number as the "base", giving it the value 100 and then expressing the second number as a percentage of the first.

Example: If the population of a town increased from 20,000 in 1988 to 21,000 in 1991, the population in 1991 was 105% of the population in 1988. Therefore, on a 1988 = 100 base, the population index for the town was 105 in 1991.

An "index", as the term is generally used when referring to statistics, is a series of index numbers expressing a series of numbers as percentages of a single number.

Example: the numbers 50 75 90 110 expressed as an index, with the first number as a base, would be 100 150 180 220

Indexes can be used to express comparisons between places, industries, etc. but the most common use is to express changes over a period of time, in which case the index is also a time series or "series". One point in time is designated the base period—it may be a year, month, or any other period—and given the value 100. The index numbers for the measurement (price, quantity, value, etc.) at all other points in time indicate the percentage change from the base period.

If the price, quantity or value has increased by 15% since the base period, the index is 115; if it has fallen 5%, the index is 95. It is important to note that indexes reflect percentage differences relative to the base year and not absolute levels. If the price index for one item is 110 and for another is 105, it means the price of the first has increased twice as much as the price of the second. It does not mean that the first item is more expensive than the second.

Each index number in a series reflects the percentage change from the base period. It is important not to confuse an index point change and a percentage change between two index numbers in a series.

Example: if the price index for butter was 130 one year and 143 the next year, the index point change would be: 143 – 130 = 13 but the percentage change for the index would be: (143 – 130) x 100) ÷ 130 = 10%

These are both ways of expressing a series of numbers by a single number. The mean most frequently referred to in Statistics Canada's publications is the arithmetic mean. It is what most people call the "average" and is calculated by adding up the numbers in the series and dividing the total by however many numbers there are.

Example: If five children are aged respectively 3, 4, 5, 8 and 10 years old, their mean age is:

3 + 4 + 5 + 8 + 10 = 6 5

The median is the value of the middle number of a series ranked in order of size.

Example: Given the ages of five children as 5, 4, 8, 3 and 10, to find the median age the series would first have to be rearranged in order of size, i.e. 3, 4, 5, 8, 10 and the value of the middle number, i.e. 5, would be the median age.

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Concepts and Definitions

Percentages are one of the most commonly used statistics. They can be found in rates (e.g. unemployment rate , employment rate ) and discounts in shop windows. So what is per cent? “Per cent” means “out of every 100”. Percentage figures are derived by dividing one quantity by another with the latter rebased to 100. Percentages are symbolised by %. Besides being especially useful when making comparisons, they come in handy for studying a difference compared with a benchmark or initial value. Say we want to compare the increase in workers over the year in two cafés – the first, a single-branched café with 10 workers and the other, a chain of cafés with 100 workers. Over the year, both employed 2 more workers (net increase). Will it be fair to conclude that both cafés experienced the same expansion? Maybe not; we can use percentages to even the scores. Using percentages, the single-branched café grew by 20% while the chain’s growth was 2%.

How to Calculate Percentages

In general, to compute a value (value 1) as a percentage of another (value 2) we use the following formula:

The Complete Guide to Marketing Research

Basic data analysis.

Once the data has been cleaned, tidied, and (if required) weighted, tables need to be created and interpreted and turned into reporting. This consists of the following steps, but they can be done in any order: summary tables, crosstabs, significance testing, and filtering data.

Summary Tables

Creating a summary report.

All the questions in a survey.
All the administrative records stored as variables in the data file (e.g., the time when the interview was commenced, the time the interview took to complete, the unique ID variable of each respondent).

The basic information shown in summary tables by most programs is basically the same, other than formatting. The following two examples are from programs that are about as dissimilar as you can get: the traditional summary table is the type commonly used by professional market researchers. The second example shows a style of summary developed for less-experienced researchers.

The following screen is shown from Displayr . A separate page is created for each summary table (or, optionally, chart). All the tables are listed on the left of the screen. A text box highlights some of the key features of the table. Arrows and colors are used to highlight results that are significantly high or low.

MarketSight

The summary developed by MarketSight lists all the variables and questions, one after another, in a large table. The main statistics that are shown on the table – the percentages and the Sample Size are the same as those shown above. Additionally, the count is shown on every table automatically.

Interpreting a Summary Report

Categorical and numeric variables.

Generally, summary reports will show tables of percentages for categorical variables, such as age and gender, and tables showing averages for numeric variables. For example, in the summary report from MarketSight below we can see that the first table shows an average of a numeric variable and the second shows percentages and counts from a categorical variable.

Switching between categorical and numeric variables

In most programs it is necessary to change the metadata to switch between the average and percentages. Exceptions to this are:

In SPSS the user specifies whether to run a mean or frequency manually for each table.
In Q and Displayr you can change the metadata, or if it is showing a percentage you can use Statistics – Below or Statistics – Right to add averages to the table of percentages.

Multiple response questions

With multiple response questions there are a couple of different ways of computing percentages:

Percentage of respondents. The % column in the table below (which was computed using Q) shows the proportion of respondents to have selected a response (e.g., the 24% for AAPT is computed by dividing the 122 people to have selected this option by the 498 people that were shown the alternative (which, in this case, was the entire sample). Generally, it is this percentage that is used when reporting data from multiple response questions).
Percentage of responses. The % Responses value of 6% is computed by dividing 122 by the total of all of the counts (i.e.,122/(122 + 46 + … + 401)). This percentage is rarely used and is perhaps never actually useful, except as an input to data cleaning.

Multiple response summary tables with messy data

When the data from a survey is ‘neat’, all the main data analysis programs used for analyzing surveys produce basically the same results. However, there are a couple of situations where programs can produce wildly different results.

Missing values

When multiple response data contains missing values the programs produce completely different results. Please see Counted Values and Missing Values in Multiple Response Questions for a discussion of how the programs differ and for instructions for making the programs produce more sensible results.

When not everybody is in a category

In a ‘tidy’ survey everybody is forced to have an answer in a multiple response question (i.e., people are not permitted to go onto the next question without selecting at least one of the alternatives). However, there are a few scenarios when not everybody will have a response:

When there are data integrity problems.
When people were not compelled to select an option.
When the multiple response question has been created by the user (e.g., if creating top 2 box scores).

In each of these situations different programs give different results. The two tables below are computed using data where everybody in the data has selected at least one category. And, as will occur with all of the standard programs, the results are the same. That is, the percentages on the table on the left, which has been computed using Q, are the same (bar rounding) as those on the right side of the second table, which was computed using SPSS. The only substantive difference between these tables relates to the bottom row, where Q shows a NET , which is the proportion of people to have selected one or more of the options, whereas SPSS shows the total.

The two tables below are also computed using Q and SPSS. Further, they use the same data as used in the tables above, except that only the first four categories have been included in the analysis. Note that the Q analysis is almost the same. The percentages for each brand remain the same. The only difference relates to the NET , which is 100% for the table above, but 93% for the table below, which is because only 93% of the sample have selected one of the four brands shown. By contrast, the results for SPSS are all different. In fact, they are all about 8% higher on the table below compared to the table above. The reason for this is that it uses a somewhat strange formula. The SPSS percentages have been computed by dividing the number of people to have selected any option by the number of people to have selected one or more options. Looking at the AAPT data, in the table above SPSS shows 8.8% which is computed as 44 / 498, where 498 is the proportion of people to have selected one or more option (i.e., the total sample). In the table below, however, 9.5% is shown which is 44 / 462, where 462 is the number of people to have selected one or more of the four brands used to construct the table.

It is important to appreciate that the discrepancy between the results is caused by having data where some people have not selected any of the categories. Where the data does not suffer from this problem, the different programs will give the same results. Additionally, the difference is one of those rare instances where one of the programs is producing numbers that are, in most situations, unhelpful (i.e., the results produced by the SPSS calculation in the second table are misleading, because most people would assume that they relate to the proportion of respondents to select the option and such an interpretation is incorrect. Unfortunately, the SPSS calculation is the ‘standard’ one and is used by most data analysis programs (which have generally been written under the assumption that people are compelled to choose at least one option).

The reason that the programs do it differently

As mentioned, in situations where the NET is 100% the two methods will get the same answer. The table-based method is the traditional approach. In a traditional survey the NET will always be 100%, because in a traditional survey run by a professional researcher there would always be a ‘None of these’ option and thus both methods get the same results. Thus, the traditional programs use the table-based method because it is faster to compute when there is no missing data. However, in situations where there is a chance that the data will be messy in some way the respondent-based method is preferable as it has the advantages that:

The possibility of a problem is flagged by the NET not being 100%.
The values that are estimated are sensible (i.e., it is much easier to explain that the percentage represents the proportion of respondents than it is to describe the percentage as representing the proportion amongst respondents that have selected at least one option).

Thus, as many of the traditional programs are developed under the assumption that the data is relatively clean they employ a method that is best in those situations, where as the more modern programs use the alternative method as it is safer in the modern world where the data is often messy.

How to switch between the different types of multiple response computations

In most programs it is possible to get the program to change the way that it computes the percentages on multiple response questions. In programs that use the respondent-based method the trick is to filter the table so that it only contains respondents that selected one or more options. In programs that use the table-based method the trick is to not tell the program that it is a multiple response question.

Using the summary report to guide data cleaning

At a minimum, the summary report should be reviewed to check that the results make sense, which essentially involves comparing results with things that are already known about the population being studied (this is discussed in detail in Checking Representativeness ), and that they are plausible (e.g., if a respondent claims to have 99 mobile phones then this suggests that there is a problem.

More thorough data cleaning involves checking that the two-way relationships between variables are sensible. For example, if checking data on firm profitability, it is useful to review the profitability per number of employees, as it may make sense for a firm to contribute 10 million dollars of profit to an industry, but it is less likely if the firm has 1 employee. This is done by creating Crosstabs . Typically, this is done as a part of the main data analysis rather than as a separate stage of data preparation.

The hard part of data cleaning is deciding what to do with “dirty” data. Consider as an example a data file that indicates that a person goes to the beach 99 times a month in summer. The options are to:

Determine that the problem is that the metadata is incorrect. For example, it may be that a value of 99 does not represent the number of trips to the beach instead indicates that the person did said “don’t know”. See Correcting Metadata .
Delete the incorrect value, replacing -99 with a special code indicating the data is invalid. This results in missing values and then there is often a need to use special analysis tools that can address the missing data. See Missing Values .
Change the value (e.g., replacing 99 with 9). See Recoding Variables .
Change the value to multiple values and assign probabilities to the different values. Although this can be the most appropriate thing to do, it is extraordinarily unusual for something like this to occur in a real-world commercial study and as such this approach, which is known as multiple imputation, is discussed no further.
Delete the entire record of data that is dirty, which involves making the assumption that this one error indicates all their data is wrong. See Deleting Respondents .

In order to work out which of these is appropriate we need to understand the cause of the poor data (e.g., key punching errors, corrupted data, respondent error), as if we clean the data without understanding the cause of the problems, we run the high risk that other data that we have not spotted as being dirty is inaccurate and that the “clean” data does not accurately represent the market.

A table showing the relationship between two questions in a survey is called a crosstab.

Reading crosstabs

The following table is a crosstab of age by whether or not somebody has a listed phone number.

This table shows the number of observations with each combination of the two questions in each cell of the table. The numbers of observations are often referred to as the counts. We can see, for example, that 185 people are aged 18 to 34 and do not have an unlisted phone number.

Column percentages

Column percentages are shown on the table above. These percentages are computed by dividing the counts for an individual cell by the total number of counts for the column. A column percent shows the proportion of people in each row from among those in the column. For example, 24% of all people without an unlisted phone number are aged 18 to 34 in the sample (i.e., 185 / 779 = 24%) and thus we can say that based on this sample we estimate that 24% of people with an unlisted phone number are aged 18 to 24.

Row percentages

Row percentages are computed by dividing the count for a cell by the total sample size for that row. A row percent shows the proportion of people in a column category from among those in the row. For example, as 185 people are aged 18 to 34 in the No column and there are a total 275 people aged 18 to 34 the row percentage is 67% (i.e., 185 / 275) and thus we can say that based on this sample we estimate that 67% of people aged 18 to 34 have an unlisted phone number.

Working out whether the table shows row or column percentages

Some crosstabs do not clearly label whether percentages are row or column percentages (e.g., the example below). When reading a table, the easiest way to check if it is showing row or column percentages is to check to see which direction the numbers add up to 100%. In the table above, the percentages add up to 100% in each column and, furthermore, this is indicated on the table by the NET , and thus it shows column percentages.

Checking to see if the percentages add up to 100% only works where the categories in the rows (or columns) are mutually exclusive. Where the data is from a multiple response question it is more difficult, as the percentages will add up to more than 100% (as people can be in more than one category). An example is shown in the table below, which shows two different types of column percentages:

The percentage of people to have selected each option( % Valid Cases ).
The percentage of options selected ( % Total Mentions ).

(See Counted Values and Missing Values in Multiple Response Questions for a more detailed discussion of how to interpret such tables).

In the crosstab below the percentages do not add up to 100% in either direction and there is nothing in the way the table is labelled to make it clear whether the table is showing row or column percentages.

In most cases when a percentage is shown on a crosstab it is a column percentage. This table shows column percentages. Where the trick of adding up the percentages does not work, as in this example, there are a few ways we can deduce whether a particular set of numbers is row or column percentages.

The position of the sample sizes on a table. By convention, if the sample sizes appear at the top of the table then column percentages are being shown and if the sample sizes appear in a column then the row percentages are shown. In the example above the sample sizes are shown at the top, suggesting that the two percentages shown are different variants of column percentages.
The position of the % signs on a table. By convention, if % symbol only appears at the top of each column in a table then column percentages are being shown and if the % symbol appears at the beginning of each row then row percentages are shown.
The degree of variation in the totals of percentages. For example, in the table below we can see that the percentages vary quite a lot within each column, but within each row they are reasonably similar, which indicates that the table shows column percentages (similarly, if the variability was greater in the rows this would indicate that row percentages were shown).

Other statistics

Most commonly crosstabs show percentages. However, where the variables are not categorical , then other statistics such as averages, medians and correlations are shown in the cells of a crosstab.

Significance Tests

A significance test is a way of working out if a particular difference is likely to be meaningful or be a fluke.

Sampling error

Imagine doing a study of 200 consumers and finding that 41% said that Coca-Cola was their favorite soft drink. Now, imagine you did another study and found that in the next study 40% of people preferred Coca-Cola. And, imagine you did a third study and found that 43% of people preferred Coca-Cola. What can you infer from the differences between these studies? There are three explanations:

Explanation 1: The world changed in some way between each of these studies and the proportion of people preferring Coca-Cola dropped a little and then increased (i.e., moved from 41% to 40% and then up to 43%).

Explanation 2: The difference between the two studies is just random noise. More specifically, as each study only sampled 200 people it is to be expected that we should get small differences between the results of these. Or, to use the jargon, there is sampling error. [note 1]

Explanation 3 : A mix of explanations 1 and 2.

Significance tests

A significance test is a rule of thumb that is used to help to determine whether a difference between two numbers is likely to reflect a meaningful difference in the world at large (i.e., explanation 1 above), or, is merely a fluke caused by sampling error (i.e., explanation 2).

There are many thousands of different significance tests with exotic names like Wilk’s lambda, Fisher’s Exact Test and so on. However, when analyzing survey data there is generally no need to go into such specific detail about which test to use and when as most significance tests that are applied when analyzing real-world surveys are either exception tests or column comparisons.

Exceptions tests

Consider the following chart from Displayr . Reading across the Coca-Cola row we can see that:

65% of people aged 18 to 24 prefer Coca-Cola.
41% of people aged 25 to 29 prefer Coca-Cola.
43% of people aged 30 to 49 prefer Coca-Cola.
40% of people aged 50 or more prefer Coca-Cola.

That we get different results in each of the age groups is to be expected. The process of selecting people to participate in a survey means that by chance alone we expect that we will get slightly different results in the different age groups even if it was the case that there really is no difference between the age groups in terms of preference for Coca-Cola (i.e,. due to sampling error). However, the level of preference for 18 to 24 year olds is substantially higher. In the chart below the font color and the arrow indicate that this result is significantly high. That is, because the result has been marked as being statistically significant, the implication is that the much higher result observed for the 18 to 24s is not merely a fluke and signifies that in the population at large it is true that 18 to 24 year olds have a higher level of preference for Coca-Cola.

Looking elsewhere on the table we can see that: Diet Coke preference seems to be low among people aged 18 to 24, Pepsi scores relatively well among the 30 to 49 year olds and so on. Each of these results are examples of exception tests, which are statistical tests that identify results that are, in some way, exceptions to the norm. [note 2]

Now look carefully at the row for Coke Zero . The score for the 18 to 24 year olds is less than half that of the other age groups. However, it is not marked as being significant and thus the conclusion is that the relatively low score for 18 to 24 year olds may be a fluke and does not reflect a true difference in the population at large. The word ‘may’ has been italicized to emphasize a key point: there is no way of known for sure whether the low score among the younger people in the survey reflects a difference in the population at large or is just a weird result that occurs in this particular sample . Thus, all significance tests are just guides. They rarely prove anything and we always need to apply some commonsense when interpreting them.

Column comparisons

The table below shows exactly the same data from the same survey as shown above. However, whereas the chart above showed results as exceptions, this one instead shows a more complicated type of significance test called column comparisons. Each of the columns is represented by a letter, shown at the bottom of the page. Some of the cells of the table contain letters and these indicate that the result in the cell is significantly higher the results in the columns that are listed. For example, looking at the Coca-Cola row, the appearance of b c d indicates that the preference for Coca-Cola of 65% among the 18 to 24 year olds is significantly higher than the preference scores of the 25 to 29 year olds ( b ), the 30 to 49 year olds ( c ) and the people aged 50 or more ( d ). That the letter are in lowercase tells us that the difference is not super-strong (in the exception shown above, the length of the arrows communicates the degree of statistical significance).

Note that while many of the conclusions that we can get from this table are similar to those from the chart above, there are some differences. For example, in the chart above we drew the conclusion that the Diet Coke preference was significantly lower among the 18 to 24 year olds than among the population at large. However, the column comparisons tell us only that the 18 to 24s have a lower score than the 30 to 49s (i.e., we know this because the a for the 30 to 49s tells us that they have a stronger preference than the 18 to 24s who are represented as column a ).

Why do the two ways of doing the tests get different results? There are some technical explanations. [note 3] But all they really amount to is this: the different approaches use slightly different technical methods and, consequently, they get slightly different results. An analogy that is useful is to think about different ways of reporting news: we can get the same story reported on TV, in a newspaper and in a blog and each way will end up emphasising slightly different aspects of the truth.

The determinants of statistical significance

There are many different factors that influence whether a particular difference is reported as being statistically significant or not, including:

The size of differences being compared (i.e., the bigger the difference the more likely it will be significant). This is exactly the same idea that is discussed on in the page on Determining The Sample Size .
The sample size. Differences observed in larger sample sizes are more likely to be statistically significant.
The specific confidence level of the testing.
The number of technical assumptions that are made in the test (e.g., assumptions of normality). In the main, the fewer assumptions that are made the lower the chance that a result is concluded as being statistically significant.
If and how the data has been weighted . The greater the effect of the weighting the less likely that results will be statistically signicant.
The number of tables that are viewed and the size of the tables. The greater the amount of analyses that are viewed, the greater the chance of fluky results.
How the data has been collected (in particular, what approach to sampling was adopted).
The technical proficiency of the person that has written the software conducting the test. In particular, most formulas presented in introductory statistical courses only take into account the first three of the issues listed above and most commercial programs deal with the weighting incorrectly. The general ambiguity of statistical testing in terms of it not being able to give definitive conclusions combined with the large number of technical errors that are made in practical applications of significant tests again lead to the same conclusion presented earlier: statistical tests are nothing more and nothing less than a useful way of identifying interesting results that may reflect how the world works but also may just be weird flukes.
Jump up ↑ Or, to be more precise, sampling error is the difference between what we observe in a random sample and what we would have obtained had we interviewed in the population .
Jump up ↑ The term exception test is not a standard term. The closest there is to a standard term for such a test is studentized residuals in contingency tables, but even this is a pretty obscure term.
Jump up ↑ In particular, the exceptions test has more statistical power due to the pooling of the sample, the columns comparisons are not transitive and there are smaller sample sizes for column comparisons than for exception tests.

Correlation

Creating filters.

Filters are created using rules regarding which respondents should be included and which should be excluded from the analysis. While there are some nuances, generally filters are created by various AND and OR rules. For example, your rule may be to include people that are aged under 50 AND are males , or, the rule may be aged under 50 OR are males . There is no consistence between the different data analysis programs in terms of how filters are created. In SPSS, for example, filters have to be created by typing an expression. For example, a filter of males under 50 would be entered as q2 <= 7 & q3 == 1 , where q2 and q3 are Variable Names and 7 and 1 are specific values that represent age and gender categories respectively.

By contrast, Q instead uses the same basic logic, but presented in a ‘tree’ type format (on the left), whereas Displayr uses a less-flexible but easier-to-use grid of checkboxes.

Filter variables

Almost all programs treat the creation of a filter as being equivalent to creating a new variable, where the variable contains two categories, one representing the people in the filter and one representing the people not in the filter group. Typically, these are added to the data file allowing them to be re-used.

Applying filters

Once a filter has been created it can usually be re-used by selecting it from a list of saved filters. The only prominent exception to this is SPSS, in which you need to create a new filter but can do so by using the older filter (e.g., if the previously-created was called var001 then the expression for the new filter if re-using it would be var001 . Another difference between SPSS and most programs is that in SPSS a filter is either on or off, whereas in other programs the filter is specifically applied to separate analyses.

Counted Values and Missing Values in Multiple Response Questions

Counted values.

In a single response question it is usually obvious that the correct way to compute the proportions is to compute the number of people that selected a category and divide this by the total number of people that selected at least one category. At an intuitive level it makes sense that percentages of multiple response data would be computed in the same way. However, the way that data is stored prevents it from being quite so simple. Usually, multiple response data is stored so that there is one variable for each brand. However, it is not always clear how to analyze this particular variable.

In some data files the code frame will be set up as:

In such a situation it is usually pretty obvious that the correct way to compute the proportions is to work out the proportion of people with a 1 in their data (it gets more complicated if there is missing data; this is discussed in the next section). Or, phrasing it in a different way, the correct way of computing the proportions is to count the higher value (i.e., the 1).

Similarly, if there is no metadata and the variable only contains 0s and 1s it is still obvious that the 1s should be counted.

However, where it gets complicated is when there are values other than 0 and 1 in the data. For example, sometimes the code frame will be:

To a human being it is obvious at the Yes responses should be counted. However, in one sense, this is the opposite to the previous examples, as now we are counting the lowest of the observed values rather than the highest.

Due to the potential ambiguity, the way that most programs work is that they either force the user to specify a specific value (e.g., SPSS requires the user to specify the Counted value ), or, they give the user the ability to inspect and modify the setting. For example, in Q the user specifies whether the analysis should or should not Count this value and in Displayr the user has the option to Select Categories .

Multiple response data where the variables contain multiple values

Sometimes the variables contain more than two values, so it is not at all obvious which of the values should be counted. There are two very difference instances of this.

Case A: Max-Multi data

If the data is in max-multi format then different options need to be selected at the time of creating the multiple response set .

In SPSS, for example, the data needs to be selected as Categories when defining the multiple response sets, whereas in Q there is a special question type of Pick Any – Compact designed for this type of data.

Case B: Recoding grid questions

Often it is useful to treat some types of grid questions as if they are multiple response questions. Most commonly, with a question that gets people to rate agreement using five points (e.g., Strongly disagree; Somewhat disagree; Neither agree nor disagree; Somewhat Agree; Strongly Agree it is common to turn this into a top 2 box scores (i.e., the NET of Somewhat Agree and Strongly Agree . There are numerous ways of doing this. However, the simplest is to treat the data as being multiple response and count multiple values. For example, count the 4 and 5 values (assuming they correspond to Somewhat Agree and Strongly Agree ). This can be done in most programs by recoding the existing variables so that they have only two values (E.g., 0 and 1) and then treating the data as if from a multiple response question. In the case of Q and Displayr they both permit the specification of multiple counted values (i.e., there is no need to recode the variables in these programs).

The following table shows the data for the first 10 of 498 respondents from the Mobiles Example . Note that, for example, the first respondent has only provided data for brands 1, 2 and 7 (i.e., has missing values for all of the others). This data is from a question where people were presented with a list of brands and asked which they had shown before (i.e., it is an Aided Awareness question). Where respondents have missing values (shown as a .) this is because they had indicated in an earlier Unaided Awareness question that they were aware of the brands.

The only way to compute a valid summary table of this multiple response question is if the data has been set up so that the analysis program knows that the correct interpretation is that a person is aware of a given brand if either they have said Yes , or, they have missing data. By default no analysis program will work this out. For example, the resulting summary tables in SPSS and Q are shown below. Both are incorrect in this instance.

To better understand the data and how to compute valid percentages it is helpful to look at the following table, which indicates the number of respondents to have data of Yes , No or Missing data for each option. Looking at the SPSS table above, the 21.5% shown for Responses AAPT has been computed by dividing the 78 people that said Yes for AAPT by the total number of Yes records for all the brands. Less obviously, the 71.6% shown for Percent of cases has been computed by dividing 78 by 106, where 106 is the number of people to have a Yes response response in the data for at least one of the brands (this number is not shown on the table and cannot be deduced from the table). In the presence of missing data neither of these statistics has an useful meaning (i.e., they are not estimates that relate to the population of phone users).

The table computed by Q (above and to the right) shows 17.6% for AAPT . This has been computed by dividing the 78 by the total number of people to have said either Yes or No for AAPT (i.e., 17.6% = 78/(376 + 78)). This percentage does have a real meaning which is useful in some contexts. The interpreation is that 17.6% of people asked whether they were aware of AAPT said they were aware. However, in this specific example, where we know that everybody with missing data was aware of AAPT, Q’s default calculation is also unhelpful.

With this type of data the correct calculation is to compute the aided awareness as the proportion of people to have said Yes or having missing data and divide this by the total number of people in the study. In the case of AAPT, for example, the correct proportion is 24.5% (i.e., (44 + 78) / 498). The following table shows the correct proportions for all of the brands:

Using software to compute the proportions correctly

The standard way to fix the data is to:

Recode the variables so that the missing values are recoded as having a value of 1.
If it is not already in the data, create a “none of these” alternative (this is necessary because SPSS and some of the older analysis packages require that each respondent has at least one Yes response in order for the percentages to be correctly calculated).

The standard method can be done in Q and Displayr as well, but both of these programs have an easier way of fixing this problem.

Computing the correct proportions in Displayr

Select a variable set which corresponds to a multiple-response question under Data Sets .
On the right, select Properties > DATA VALUES > Missing Data , select Include in Analyses for each of the categories and press OK .
On the right, select Properties > DATA VALUES > Select Categories and ensure that Yes and Missing data are selected and press OK .

Computing the correct proportions in Q

In the Tables tab, right-click on one of the categories of the question and select Values .
Fill in the dialog box as shown below.

This study is from the Australian mobile (cell) phone market.

The questionnaire: Mobiles Questionnaire.pdf

SPSS data file: Mobiles.sav

This study is from the cola market.

The questionnaire: Colas Questionnaire.pdf

SPSS data file: Colas.sav

Need to print it? Download the Guide

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Talking with Your Healthcare Provider
Birth Defects Statistics
Birth Defects Resources
Birth Defects Awareness Month
Living with Down Syndrome
Conversation Tips
Growth Charts for Down Syndrome
Accessing NBDPS and BD-STEPS Data
Birth Defects Awareness Month Social Media Resources
About Alcohol Use During Pregnancy

About Down Syndrome

Down syndrome is a genetic condition where a person is born with an extra chromosome.
This can affect how their brain and body develop.
People diagnosed with Down syndrome can lead healthy lives with supportive care.

Down syndrome is a condition in which a person has an extra copy of chromosome 21. Chromosomes are small "packages" of genes in the body's cells, which determine how the body forms and functions.

When babies are growing, the extra chromosome changes how their body and brain develop. This can cause both physical and mental challenges.

People with Down syndrome often have developmental challenges, such as being slower to learn to speak than other children.

Distinct physical signs of Down syndrome are usually present at birth and become more apparent as the baby grows. They can include facial features, such as:

A flattened face, especially the bridge of the nose
Almond-shaped eyes that slant up
A tongue that tends to stick out of the mouth

Other physical signs can include:

A short neck
Small ears, hands, and feet
A single line across the palm of the hand (palmar crease)
Small pinky fingers
Poor muscle tone or loose joints
Shorter-than-average height

Some people with Down syndrome have other medical problems as well. Common health problems include:

Congenital heart defects
Hearing loss
Obstructive sleep apnea

Down syndrome is the most common chromosomal condition diagnosed in the United States. Each year, about 5,700 babies born in the US have Down syndrome. 1

Collage of photos of people of all races and ages with Down syndrome. Text reads

There are three types of Down syndrome. The physical features and behaviors are similar for all three types.

With Trisomy 21, each cell in the body has three separate copies of chromosome 21. About 95% of people with Down syndrome have Trisomy 21.

Translocation Down syndrome

In this type, an extra part or a whole extra chromosome 21 is present. However, the extra chromosome is attached or "trans-located" to a different chromosome rather than being a separate chromosome 21. This type accounts for about 3% of people with Down syndrome.

Mosaic Down syndrome

Mosaic means mixture or combination. In this type, some cells have three copies of chromosome 21, but other cells have the typical two copies. People with mosaic Down syndrome may have fewer features of the condition. This type accounts for about 2% of people with Down syndrome.

Risk factors

We don't know for sure why Down syndrome occurs or how many different factors play a role. We do know that some things can affect your risk of having a baby with Down syndrome.

One factor is your age when you get pregnant. The risk of having a baby with Down syndrome increases with age, especially if you are 35 years or older when you get pregnant. 2 3 4

However, the majority of babies with Down syndrome are still born to mothers less than 35 years old. This is because there are many more births among younger women. 5 6

Regardless of age, parents who have one child with Down syndrome are at an increased risk of having another child with Down syndrome. 7

Screening and diagnosis

There are two types of tests available to detect Down syndrome during pregnancy: screening tests and diagnostic tests. A screening test can tell you if your pregnancy has a higher chance of being affected Down syndrome. Screening tests don't provide an absolute diagnosis.

Diagnostic tests can typically detect if a baby will have Down syndrome, but they carry more risk. Neither screening nor diagnostic tests can predict the full impact of Down syndrome on a baby.

The views of these organizations are their own and do not reflect the official position of CDC.

Down Syndrome Resource Foundation (DSRF) : The DSRF supports people living with Down syndrome and their families with individualized and leading-edge educational programs, health services, information resources, and rich social connections so each person can flourish in their own right.

GiGi's Playhouse : GiGi's Playhouse provides free educational, therapeutic-based, and career development programs for individuals with Down syndrome, their families, and the community, through a replicable playhouse model.

Global Down Syndrome Foundation : This foundation is dedicated to significantly improving the lives of people with Down syndrome through research, medical care, education and advocacy.

National Association for Down Syndrome : The National Association for Down Syndrome supports all persons with Down syndrome in achieving their full potential. They seek to help families, educate the public, address social issues and challenges, and facilitate active participation.

National Down Syndrome Society (NDSS) : NDSS seeks to increase awareness and acceptance of those with Down syndrome.

Stallings, E. B., Isenburg, J. L., Rutkowski, R. E., Kirby, R. S., Nembhard, W.N., Sandidge, T., Villavicencio, S., Nguyen, H. H., McMahon, D. M., Nestoridi, E., Pabst, L. J., for the National Birth Defects Prevention Network. National population-based estimates for major birth defects, 2016–2020. Birth Defects Research. 2024 Jan;116(1), e2301.
Allen EG, Freeman SB, Druschel C, et al. Maternal age and risk for trisomy 21 assessed by the origin of chromosome nondisjunction: a report from the Atlanta and National Down Syndrome Projects. Hum Genet. 2009 Feb;125(1):41-52.
Ghosh S, Feingold E, Dey SK. Etiology of Down syndrome: Evidence for consistent association among altered meiotic recombination, nondisjunction, and maternal age across populations. Am J Med Genet A. 2009 Jul;149A(7):1415-20.
Sherman SL, Allen EG, Bean LH, Freeman SB. Epidemiology of Down syndrome. Ment Retard Dev Disabil Res Rev. 2007;13(3):221-7.
Olsen CL, Cross PK, Gensburg LJ, Hughes JP. The effects of prenatal diagnosis, population ageing, and changing fertility rates on the live birth prevalence of Down syndrome in New York State, 1983-1992. Prenat Diagn. 1996 Nov;16(11):991-1002.
Adams MM, Erickson JD, Layde PM, Oakley GP. Down's syndrome. Recent trends in the United States. JAMA. 1981 Aug 14;246(7):758-60.
Morris JK, Mutton DE, Alberman E. Recurrences of free trisomy 21: analysis of data from the National Down Syndrome Cytogenetic Register. Prenatal Diagnosis: Published in Affiliation With the International Society for Prenatal Diagnosis. 2005 Dec 15;25(12):1120-8.

Birth Defects

About one in every 33 babies is born with a birth defect. Although not all birth defects can be prevented, people can increase their chances of having a healthy baby by managing health conditions and adopting healthy behaviors before becoming pregnant.

For Everyone

Health care providers, public health.

Cultural Relativity and Acceptance of Embryonic Stem Cell Research

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Main Article Content

There is a debate about the ethical implications of using human embryos in stem cell research, which can be influenced by cultural, moral, and social values. This paper argues for an adaptable framework to accommodate diverse cultural and religious perspectives. By using an adaptive ethics model, research protections can reflect various populations and foster growth in stem cell research possibilities.

INTRODUCTION

Stem cell research combines biology, medicine, and technology, promising to alter health care and the understanding of human development. Yet, ethical contention exists because of individuals’ perceptions of using human embryos based on their various cultural, moral, and social values. While these disagreements concerning policy, use, and general acceptance have prompted the development of an international ethics policy, such a uniform approach can overlook the nuanced ethical landscapes between cultures. With diverse viewpoints in public health, a single global policy, especially one reflecting Western ethics or the ethics prevalent in high-income countries, is impractical. This paper argues for a culturally sensitive, adaptable framework for the use of embryonic stem cells. Stem cell policy should accommodate varying ethical viewpoints and promote an effective global dialogue. With an extension of an ethics model that can adapt to various cultures, we recommend localized guidelines that reflect the moral views of the people those guidelines serve.

Stem cells, characterized by their unique ability to differentiate into various cell types, enable the repair or replacement of damaged tissues. Two primary types of stem cells are somatic stem cells (adult stem cells) and embryonic stem cells. Adult stem cells exist in developed tissues and maintain the body’s repair processes. [1] Embryonic stem cells (ESC) are remarkably pluripotent or versatile, making them valuable in research. [2] However, the use of ESCs has sparked ethics debates. Considering the potential of embryonic stem cells, research guidelines are essential. The International Society for Stem Cell Research (ISSCR) provides international stem cell research guidelines. They call for “public conversations touching on the scientific significance as well as the societal and ethical issues raised by ESC research.” [3] The ISSCR also publishes updates about culturing human embryos 14 days post fertilization, suggesting local policies and regulations should continue to evolve as ESC research develops. [4] Like the ISSCR, which calls for local law and policy to adapt to developing stem cell research given cultural acceptance, this paper highlights the importance of local social factors such as religion and culture.

I. Global Cultural Perspective of Embryonic Stem Cells

Views on ESCs vary throughout the world. Some countries readily embrace stem cell research and therapies, while others have stricter regulations due to ethical concerns surrounding embryonic stem cells and when an embryo becomes entitled to moral consideration. The philosophical issue of when the “someone” begins to be a human after fertilization, in the morally relevant sense, [5] impacts when an embryo becomes not just worthy of protection but morally entitled to it. The process of creating embryonic stem cell lines involves the destruction of the embryos for research. [6] Consequently, global engagement in ESC research depends on social-cultural acceptability.

a. US and Rights-Based Cultures

In the United States, attitudes toward stem cell therapies are diverse. The ethics and social approaches, which value individualism, [7] trigger debates regarding the destruction of human embryos, creating a complex regulatory environment. For example, the 1996 Dickey-Wicker Amendment prohibited federal funding for the creation of embryos for research and the destruction of embryos for “more than allowed for research on fetuses in utero.” [8] Following suit, in 2001, the Bush Administration heavily restricted stem cell lines for research. However, the Stem Cell Research Enhancement Act of 2005 was proposed to help develop ESC research but was ultimately vetoed. [9] Under the Obama administration, in 2009, an executive order lifted restrictions allowing for more development in this field. [10] The flux of research capacity and funding parallels the different cultural perceptions of human dignity of the embryo and how it is socially presented within the country’s research culture. [11]

b. Ubuntu and Collective Cultures

African bioethics differs from Western individualism because of the different traditions and values. African traditions, as described by individuals from South Africa and supported by some studies in other African countries, including Ghana and Kenya, follow the African moral philosophies of Ubuntu or Botho and Ukama , which “advocates for a form of wholeness that comes through one’s relationship and connectedness with other people in the society,” [12] making autonomy a socially collective concept. In this context, for the community to act autonomously, individuals would come together to decide what is best for the collective. Thus, stem cell research would require examining the value of the research to society as a whole and the use of the embryos as a collective societal resource. If society views the source as part of the collective whole, and opposes using stem cells, compromising the cultural values to pursue research may cause social detachment and stunt research growth. [13] Based on local culture and moral philosophy, the permissibility of stem cell research depends on how embryo, stem cell, and cell line therapies relate to the community as a whole. Ubuntu is the expression of humanness, with the person’s identity drawn from the “’I am because we are’” value. [14] The decision in a collectivistic culture becomes one born of cultural context, and individual decisions give deference to others in the society.

Consent differs in cultures where thought and moral philosophy are based on a collective paradigm. So, applying Western bioethical concepts is unrealistic. For one, Africa is a diverse continent with many countries with different belief systems, access to health care, and reliance on traditional or Western medicines. Where traditional medicine is the primary treatment, the “’restrictive focus on biomedically-related bioethics’” [is] problematic in African contexts because it neglects bioethical issues raised by traditional systems.” [15] No single approach applies in all areas or contexts. Rather than evaluating the permissibility of ESC research according to Western concepts such as the four principles approach, different ethics approaches should prevail.

Another consideration is the socio-economic standing of countries. In parts of South Africa, researchers have not focused heavily on contributing to the stem cell discourse, either because it is not considered health care or a health science priority or because resources are unavailable. [16] Each country’s priorities differ given different social, political, and economic factors. In South Africa, for instance, areas such as maternal mortality, non-communicable diseases, telemedicine, and the strength of health systems need improvement and require more focus [17] Stem cell research could benefit the population, but it also could divert resources from basic medical care. Researchers in South Africa adhere to the National Health Act and Medicines Control Act in South Africa and international guidelines; however, the Act is not strictly enforced, and there is no clear legislation for research conduct or ethical guidelines. [18]

Some parts of Africa condemn stem cell research. For example, 98.2 percent of the Tunisian population is Muslim. [19] Tunisia does not permit stem cell research because of moral conflict with a Fatwa. Religion heavily saturates the regulation and direction of research. [20] Stem cell use became permissible for reproductive purposes only recently, with tight restrictions preventing cells from being used in any research other than procedures concerning ART/IVF. Their use is conditioned on consent, and available only to married couples. [21] The community's receptiveness to stem cell research depends on including communitarian African ethics.

c. Asia

Some Asian countries also have a collective model of ethics and decision making. [22] In China, the ethics model promotes a sincere respect for life or human dignity, [23] based on protective medicine. This model, influenced by Traditional Chinese Medicine (TCM), [24] recognizes Qi as the vital energy delivered via the meridians of the body; it connects illness to body systems, the body’s entire constitution, and the universe for a holistic bond of nature, health, and quality of life. [25] Following a protective ethics model, and traditional customs of wholeness, investment in stem cell research is heavily desired for its applications in regenerative therapies, disease modeling, and protective medicines. In a survey of medical students and healthcare practitioners, 30.8 percent considered stem cell research morally unacceptable while 63.5 percent accepted medical research using human embryonic stem cells. Of these individuals, 89.9 percent supported increased funding for stem cell research. [26] The scientific community might not reflect the overall population. From 1997 to 2019, China spent a total of $576 million (USD) on stem cell research at 8,050 stem cell programs, increased published presence from 0.6 percent to 14.01 percent of total global stem cell publications as of 2014, and made significant strides in cell-based therapies for various medical conditions. [27] However, while China has made substantial investments in stem cell research and achieved notable progress in clinical applications, concerns linger regarding ethical oversight and transparency. [28] For example, the China Biosecurity Law, promoted by the National Health Commission and China Hospital Association, attempted to mitigate risks by introducing an institutional review board (IRB) in the regulatory bodies. 5800 IRBs registered with the Chinese Clinical Trial Registry since 2021. [29] However, issues still need to be addressed in implementing effective IRB review and approval procedures.

The substantial government funding and focus on scientific advancement have sometimes overshadowed considerations of regional cultures, ethnic minorities, and individual perspectives, particularly evident during the one-child policy era. As government policy adapts to promote public stability, such as the change from the one-child to the two-child policy, [30] research ethics should also adapt to ensure respect for the values of its represented peoples.

Japan is also relatively supportive of stem cell research and therapies. Japan has a more transparent regulatory framework, allowing for faster approval of regenerative medicine products, which has led to several advanced clinical trials and therapies. [31] South Korea is also actively engaged in stem cell research and has a history of breakthroughs in cloning and embryonic stem cells. [32] However, the field is controversial, and there are issues of scientific integrity. For example, the Korean FDA fast-tracked products for approval, [33] and in another instance, the oocyte source was unclear and possibly violated ethical standards. [34] Trust is important in research, as it builds collaborative foundations between colleagues, trial participant comfort, open-mindedness for complicated and sensitive discussions, and supports regulatory procedures for stakeholders. There is a need to respect the culture’s interest, engagement, and for research and clinical trials to be transparent and have ethical oversight to promote global research discourse and trust.

d. Middle East

Countries in the Middle East have varying degrees of acceptance of or restrictions to policies related to using embryonic stem cells due to cultural and religious influences. Saudi Arabia has made significant contributions to stem cell research, and conducts research based on international guidelines for ethical conduct and under strict adherence to guidelines in accordance with Islamic principles. Specifically, the Saudi government and people require ESC research to adhere to Sharia law. In addition to umbilical and placental stem cells, [35] Saudi Arabia permits the use of embryonic stem cells as long as they come from miscarriages, therapeutic abortions permissible by Sharia law, or are left over from in vitro fertilization and donated to research. [36] Laws and ethical guidelines for stem cell research allow the development of research institutions such as the King Abdullah International Medical Research Center, which has a cord blood bank and a stem cell registry with nearly 10,000 donors. [37] Such volume and acceptance are due to the ethical ‘permissibility’ of the donor sources, which do not conflict with religious pillars. However, some researchers err on the side of caution, choosing not to use embryos or fetal tissue as they feel it is unethical to do so. [38]

Jordan has a positive research ethics culture. [39] However, there is a significant issue of lack of trust in researchers, with 45.23 percent (38.66 percent agreeing and 6.57 percent strongly agreeing) of Jordanians holding a low level of trust in researchers, compared to 81.34 percent of Jordanians agreeing that they feel safe to participate in a research trial. [40] Safety testifies to the feeling of confidence that adequate measures are in place to protect participants from harm, whereas trust in researchers could represent the confidence in researchers to act in the participants’ best interests, adhere to ethical guidelines, provide accurate information, and respect participants’ rights and dignity. One method to improve trust would be to address communication issues relevant to ESC. Legislation surrounding stem cell research has adopted specific language, especially concerning clarification “between ‘stem cells’ and ‘embryonic stem cells’” in translation. [41] Furthermore, legislation “mandates the creation of a national committee… laying out specific regulations for stem-cell banking in accordance with international standards.” [42] This broad regulation opens the door for future global engagement and maintains transparency. However, these regulations may also constrain the influence of research direction, pace, and accessibility of research outcomes.

e. Europe

In the European Union (EU), ethics is also principle-based, but the principles of autonomy, dignity, integrity, and vulnerability are interconnected. [43] As such, the opportunity for cohesion and concessions between individuals’ thoughts and ideals allows for a more adaptable ethics model due to the flexible principles that relate to the human experience The EU has put forth a framework in its Convention for the Protection of Human Rights and Dignity of the Human Being allowing member states to take different approaches. Each European state applies these principles to its specific conventions, leading to or reflecting different acceptance levels of stem cell research. [44]

For example, in Germany, Lebenzusammenhang , or the coherence of life, references integrity in the unity of human culture. Namely, the personal sphere “should not be subject to external intervention.” [45] Stem cell interventions could affect this concept of bodily completeness, leading to heavy restrictions. Under the Grundgesetz, human dignity and the right to life with physical integrity are paramount. [46] The Embryo Protection Act of 1991 made producing cell lines illegal. Cell lines can be imported if approved by the Central Ethics Commission for Stem Cell Research only if they were derived before May 2007. [47] Stem cell research respects the integrity of life for the embryo with heavy specifications and intense oversight. This is vastly different in Finland, where the regulatory bodies find research more permissible in IVF excess, but only up to 14 days after fertilization. [48] Spain’s approach differs still, with a comprehensive regulatory framework. [49] Thus, research regulation can be culture-specific due to variations in applied principles. Diverse cultures call for various approaches to ethical permissibility. [50] Only an adaptive-deliberative model can address the cultural constructions of self and achieve positive, culturally sensitive stem cell research practices. [51]

II. Religious Perspectives on ESC

Embryonic stem cell sources are the main consideration within religious contexts. While individuals may not regard their own religious texts as authoritative or factual, religion can shape their foundations or perspectives.

The Qur'an states:

“And indeed We created man from a quintessence of clay. Then We placed within him a small quantity of nutfa (sperm to fertilize) in a safe place. Then We have fashioned the nutfa into an ‘alaqa (clinging clot or cell cluster), then We developed the ‘alaqa into mudgha (a lump of flesh), and We made mudgha into bones, and clothed the bones with flesh, then We brought it into being as a new creation. So Blessed is Allah, the Best of Creators.” [52]

Many scholars of Islam estimate the time of soul installment, marked by the angel breathing in the soul to bring the individual into creation, as 120 days from conception. [53] Personhood begins at this point, and the value of life would prohibit research or experimentation that could harm the individual. If the fetus is more than 120 days old, the time ensoulment is interpreted to occur according to Islamic law, abortion is no longer permissible. [54] There are a few opposing opinions about early embryos in Islamic traditions. According to some Islamic theologians, there is no ensoulment of the early embryo, which is the source of stem cells for ESC research. [55]

In Buddhism, the stance on stem cell research is not settled. The main tenets, the prohibition against harming or destroying others (ahimsa) and the pursuit of knowledge (prajña) and compassion (karuna), leave Buddhist scholars and communities divided. [56] Some scholars argue stem cell research is in accordance with the Buddhist tenet of seeking knowledge and ending human suffering. Others feel it violates the principle of not harming others. Finding the balance between these two points relies on the karmic burden of Buddhist morality. In trying to prevent ahimsa towards the embryo, Buddhist scholars suggest that to comply with Buddhist tenets, research cannot be done as the embryo has personhood at the moment of conception and would reincarnate immediately, harming the individual's ability to build their karmic burden. [57] On the other hand, the Bodhisattvas, those considered to be on the path to enlightenment or Nirvana, have given organs and flesh to others to help alleviate grieving and to benefit all. [58] Acceptance varies on applied beliefs and interpretations.

Catholicism does not support embryonic stem cell research, as it entails creation or destruction of human embryos. This destruction conflicts with the belief in the sanctity of life. For example, in the Old Testament, Genesis describes humanity as being created in God’s image and multiplying on the Earth, referencing the sacred rights to human conception and the purpose of development and life. In the Ten Commandments, the tenet that one should not kill has numerous interpretations where killing could mean murder or shedding of the sanctity of life, demonstrating the high value of human personhood. In other books, the theological conception of when life begins is interpreted as in utero, [59] highlighting the inviolability of life and its formation in vivo to make a religious point for accepting such research as relatively limited, if at all. [60] The Vatican has released ethical directives to help apply a theological basis to modern-day conflicts. The Magisterium of the Church states that “unless there is a moral certainty of not causing harm,” experimentation on fetuses, fertilized cells, stem cells, or embryos constitutes a crime. [61] Such procedures would not respect the human person who exists at these stages, according to Catholicism. Damages to the embryo are considered gravely immoral and illicit. [62] Although the Catholic Church officially opposes abortion, surveys demonstrate that many Catholic people hold pro-choice views, whether due to the context of conception, stage of pregnancy, threat to the mother’s life, or for other reasons, demonstrating that practicing members can also accept some but not all tenets. [63]

Some major Jewish denominations, such as the Reform, Conservative, and Reconstructionist movements, are open to supporting ESC use or research as long as it is for saving a life. [64] Within Judaism, the Talmud, or study, gives personhood to the child at birth and emphasizes that life does not begin at conception: [65]

“If she is found pregnant, until the fortieth day it is mere fluid,” [66]

Whereas most religions prioritize the status of human embryos, the Halakah (Jewish religious law) states that to save one life, most other religious laws can be ignored because it is in pursuit of preservation. [67] Stem cell research is accepted due to application of these religious laws.

We recognize that all religions contain subsets and sects. The variety of environmental and cultural differences within religious groups requires further analysis to respect the flexibility of religious thoughts and practices. We make no presumptions that all cultures require notions of autonomy or morality as under the common morality theory , which asserts a set of universal moral norms that all individuals share provides moral reasoning and guides ethical decisions. [68] We only wish to show that the interaction with morality varies between cultures and countries.

III. A Flexible Ethical Approach

The plurality of different moral approaches described above demonstrates that there can be no universally acceptable uniform law for ESC on a global scale. Instead of developing one standard, flexible ethical applications must be continued. We recommend local guidelines that incorporate important cultural and ethical priorities.

While the Declaration of Helsinki is more relevant to people in clinical trials receiving ESC products, in keeping with the tradition of protections for research subjects, consent of the donor is an ethical requirement for ESC donation in many jurisdictions including the US, Canada, and Europe. [69] The Declaration of Helsinki provides a reference point for regulatory standards and could potentially be used as a universal baseline for obtaining consent prior to gamete or embryo donation.

For instance, in Columbia University’s egg donor program for stem cell research, donors followed standard screening protocols and “underwent counseling sessions that included information as to the purpose of oocyte donation for research, what the oocytes would be used for, the risks and benefits of donation, and process of oocyte stimulation” to ensure transparency for consent. [70] The program helped advance stem cell research and provided clear and safe research methods with paid participants. Though paid participation or covering costs of incidental expenses may not be socially acceptable in every culture or context, [71] and creating embryos for ESC research is illegal in many jurisdictions, Columbia’s program was effective because of the clear and honest communications with donors, IRBs, and related stakeholders. This example demonstrates that cultural acceptance of scientific research and of the idea that an egg or embryo does not have personhood is likely behind societal acceptance of donating eggs for ESC research. As noted, many countries do not permit the creation of embryos for research.

Proper communication and education regarding the process and purpose of stem cell research may bolster comprehension and garner more acceptance. “Given the sensitive subject material, a complete consent process can support voluntary participation through trust, understanding, and ethical norms from the cultures and morals participants value. This can be hard for researchers entering countries of different socioeconomic stability, with different languages and different societal values. [72]

An adequate moral foundation in medical ethics is derived from the cultural and religious basis that informs knowledge and actions. [73] Understanding local cultural and religious values and their impact on research could help researchers develop humility and promote inclusion.

IV. Concerns

Some may argue that if researchers all adhere to one ethics standard, protection will be satisfied across all borders, and the global public will trust researchers. However, defining what needs to be protected and how to define such research standards is very specific to the people to which standards are applied. We suggest that applying one uniform guide cannot accurately protect each individual because we all possess our own perceptions and interpretations of social values. [74] Therefore, the issue of not adjusting to the moral pluralism between peoples in applying one standard of ethics can be resolved by building out ethics models that can be adapted to different cultures and religions.

Other concerns include medical tourism, which may promote health inequities. [75] Some countries may develop and approve products derived from ESC research before others, compromising research ethics or drug approval processes. There are also concerns about the sale of unauthorized stem cell treatments, for example, those without FDA approval in the United States. Countries with robust research infrastructures may be tempted to attract medical tourists, and some customers will have false hopes based on aggressive publicity of unproven treatments. [76]

For example, in China, stem cell clinics can market to foreign clients who are not protected under the regulatory regimes. Companies employ a marketing strategy of “ethically friendly” therapies. Specifically, in the case of Beike, China’s leading stem cell tourism company and sprouting network, ethical oversight of administrators or health bureaus at one site has “the unintended consequence of shifting questionable activities to another node in Beike's diffuse network.” [77] In contrast, Jordan is aware of stem cell research’s potential abuse and its own status as a “health-care hub.” Jordan’s expanded regulations include preserving the interests of individuals in clinical trials and banning private companies from ESC research to preserve transparency and the integrity of research practices. [78]

The social priorities of the community are also a concern. The ISSCR explicitly states that guidelines “should be periodically revised to accommodate scientific advances, new challenges, and evolving social priorities.” [79] The adaptable ethics model extends this consideration further by addressing whether research is warranted given the varying degrees of socioeconomic conditions, political stability, and healthcare accessibilities and limitations. An ethical approach would require discussion about resource allocation and appropriate distribution of funds. [80]

While some religions emphasize the sanctity of life from conception, which may lead to public opposition to ESC research, others encourage ESC research due to its potential for healing and alleviating human pain. Many countries have special regulations that balance local views on embryonic personhood, the benefits of research as individual or societal goods, and the protection of human research subjects. To foster understanding and constructive dialogue, global policy frameworks should prioritize the protection of universal human rights, transparency, and informed consent. In addition to these foundational global policies, we recommend tailoring local guidelines to reflect the diverse cultural and religious perspectives of the populations they govern. Ethics models should be adapted to local populations to effectively establish research protections, growth, and possibilities of stem cell research.

For example, in countries with strong beliefs in the moral sanctity of embryos or heavy religious restrictions, an adaptive model can allow for discussion instead of immediate rejection. In countries with limited individual rights and voice in science policy, an adaptive model ensures cultural, moral, and religious views are taken into consideration, thereby building social inclusion. While this ethical consideration by the government may not give a complete voice to every individual, it will help balance policies and maintain the diverse perspectives of those it affects. Embracing an adaptive ethics model of ESC research promotes open-minded dialogue and respect for the importance of human belief and tradition. By actively engaging with cultural and religious values, researchers can better handle disagreements and promote ethical research practices that benefit each society.

This brief exploration of the religious and cultural differences that impact ESC research reveals the nuances of relative ethics and highlights a need for local policymakers to apply a more intense adaptive model.

[1] Poliwoda, S., Noor, N., Downs, E., Schaaf, A., Cantwell, A., Ganti, L., Kaye, A. D., Mosel, L. I., Carroll, C. B., Viswanath, O., & Urits, I. (2022). Stem cells: a comprehensive review of origins and emerging clinical roles in medical practice. Orthopedic reviews , 14 (3), 37498. https://doi.org/10.52965/001c.37498

[2] Poliwoda, S., Noor, N., Downs, E., Schaaf, A., Cantwell, A., Ganti, L., Kaye, A. D., Mosel, L. I., Carroll, C. B., Viswanath, O., & Urits, I. (2022). Stem cells: a comprehensive review of origins and emerging clinical roles in medical practice. Orthopedic reviews , 14 (3), 37498. https://doi.org/10.52965/001c.37498

[3] International Society for Stem Cell Research. (2023). Laboratory-based human embryonic stem cell research, embryo research, and related research activities . International Society for Stem Cell Research. https://www.isscr.org/guidelines/blog-post-title-one-ed2td-6fcdk ; Kimmelman, J., Hyun, I., Benvenisty, N. et al. Policy: Global standards for stem-cell research. Nature 533 , 311–313 (2016). https://doi.org/10.1038/533311a

[4] International Society for Stem Cell Research. (2023). Laboratory-based human embryonic stem cell research, embryo research, and related research activities . International Society for Stem Cell Research. https://www.isscr.org/guidelines/blog-post-title-one-ed2td-6fcdk

[5] Concerning the moral philosophies of stem cell research, our paper does not posit a personal moral stance nor delve into the “when” of human life begins. To read further about the philosophical debate, consider the following sources:

Sandel M. J. (2004). Embryo ethics--the moral logic of stem-cell research. The New England journal of medicine , 351 (3), 207–209. https://doi.org/10.1056/NEJMp048145 ; George, R. P., & Lee, P. (2020, September 26). Acorns and Embryos . The New Atlantis. https://www.thenewatlantis.com/publications/acorns-and-embryos ; Sagan, A., & Singer, P. (2007). The moral status of stem cells. Metaphilosophy , 38 (2/3), 264–284. http://www.jstor.org/stable/24439776 ; McHugh P. R. (2004). Zygote and "clonote"--the ethical use of embryonic stem cells. The New England journal of medicine , 351 (3), 209–211. https://doi.org/10.1056/NEJMp048147 ; Kurjak, A., & Tripalo, A. (2004). The facts and doubts about beginning of the human life and personality. Bosnian journal of basic medical sciences , 4 (1), 5–14. https://doi.org/10.17305/bjbms.2004.3453

[6] Vazin, T., & Freed, W. J. (2010). Human embryonic stem cells: derivation, culture, and differentiation: a review. Restorative neurology and neuroscience , 28 (4), 589–603. https://doi.org/10.3233/RNN-2010-0543

[7] Socially, at its core, the Western approach to ethics is widely principle-based, autonomy being one of the key factors to ensure a fundamental respect for persons within research. For information regarding autonomy in research, see: Department of Health, Education, and Welfare, & National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (1978). The Belmont Report. Ethical principles and guidelines for the protection of human subjects of research.; For a more in-depth review of autonomy within the US, see: Beauchamp, T. L., & Childress, J. F. (1994). Principles of Biomedical Ethics . Oxford University Press.

[8] Sherley v. Sebelius , 644 F.3d 388 (D.C. Cir. 2011), citing 45 C.F.R. 46.204(b) and [42 U.S.C. § 289g(b)]. https://www.cadc.uscourts.gov/internet/opinions.nsf/6c690438a9b43dd685257a64004ebf99/$file/11-5241-1391178.pdf

[9] Stem Cell Research Enhancement Act of 2005, H. R. 810, 109 th Cong. (2001). https://www.govtrack.us/congress/bills/109/hr810/text ; Bush, G. W. (2006, July 19). Message to the House of Representatives . National Archives and Records Administration. https://georgewbush-whitehouse.archives.gov/news/releases/2006/07/20060719-5.html

[10] National Archives and Records Administration. (2009, March 9). Executive order 13505 -- removing barriers to responsible scientific research involving human stem cells . National Archives and Records Administration. https://obamawhitehouse.archives.gov/the-press-office/removing-barriers-responsible-scientific-research-involving-human-stem-cells

[11] Hurlbut, W. B. (2006). Science, Religion, and the Politics of Stem Cells. Social Research , 73 (3), 819–834. http://www.jstor.org/stable/40971854

[12] Akpa-Inyang, Francis & Chima, Sylvester. (2021). South African traditional values and beliefs regarding informed consent and limitations of the principle of respect for autonomy in African communities: a cross-cultural qualitative study. BMC Medical Ethics . 22. 10.1186/s12910-021-00678-4.

[13] Source for further reading: Tangwa G. B. (2007). Moral status of embryonic stem cells: perspective of an African villager. Bioethics , 21(8), 449–457. https://doi.org/10.1111/j.1467-8519.2007.00582.x , see also Mnisi, F. M. (2020). An African analysis based on ethics of Ubuntu - are human embryonic stem cell patents morally justifiable? African Insight , 49 (4).

[14] Jecker, N. S., & Atuire, C. (2021). Bioethics in Africa: A contextually enlightened analysis of three cases. Developing World Bioethics , 22 (2), 112–122. https://doi.org/10.1111/dewb.12324

[15] Jecker, N. S., & Atuire, C. (2021). Bioethics in Africa: A contextually enlightened analysis of three cases. Developing World Bioethics, 22(2), 112–122. https://doi.org/10.1111/dewb.12324

[16] Jackson, C.S., Pepper, M.S. Opportunities and barriers to establishing a cell therapy programme in South Africa. Stem Cell Res Ther 4 , 54 (2013). https://doi.org/10.1186/scrt204 ; Pew Research Center. (2014, May 1). Public health a major priority in African nations . Pew Research Center’s Global Attitudes Project. https://www.pewresearch.org/global/2014/05/01/public-health-a-major-priority-in-african-nations/

[17] Department of Health Republic of South Africa. (2021). Health Research Priorities (revised) for South Africa 2021-2024 . National Health Research Strategy. https://www.health.gov.za/wp-content/uploads/2022/05/National-Health-Research-Priorities-2021-2024.pdf

[18] Oosthuizen, H. (2013). Legal and Ethical Issues in Stem Cell Research in South Africa. In: Beran, R. (eds) Legal and Forensic Medicine. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32338-6_80 , see also: Gaobotse G (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[19] United States Bureau of Citizenship and Immigration Services. (1998). Tunisia: Information on the status of Christian conversions in Tunisia . UNHCR Web Archive. https://webarchive.archive.unhcr.org/20230522142618/https://www.refworld.org/docid/3df0be9a2.html

[20] Gaobotse, G. (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[21] Kooli, C. Review of assisted reproduction techniques, laws, and regulations in Muslim countries. Middle East Fertil Soc J 24 , 8 (2020). https://doi.org/10.1186/s43043-019-0011-0 ; Gaobotse, G. (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[22] Pang M. C. (1999). Protective truthfulness: the Chinese way of safeguarding patients in informed treatment decisions. Journal of medical ethics , 25(3), 247–253. https://doi.org/10.1136/jme.25.3.247

[23] Wang, L., Wang, F., & Zhang, W. (2021). Bioethics in China’s biosecurity law: Forms, effects, and unsettled issues. Journal of law and the biosciences , 8(1). https://doi.org/10.1093/jlb/lsab019 https://academic.oup.com/jlb/article/8/1/lsab019/6299199

[24] Wang, Y., Xue, Y., & Guo, H. D. (2022). Intervention effects of traditional Chinese medicine on stem cell therapy of myocardial infarction. Frontiers in pharmacology , 13 , 1013740. https://doi.org/10.3389/fphar.2022.1013740

[25] Li, X.-T., & Zhao, J. (2012). Chapter 4: An Approach to the Nature of Qi in TCM- Qi and Bioenergy. In Recent Advances in Theories and Practice of Chinese Medicine (p. 79). InTech.

[26] Luo, D., Xu, Z., Wang, Z., & Ran, W. (2021). China's Stem Cell Research and Knowledge Levels of Medical Practitioners and Students. Stem cells international , 2021 , 6667743. https://doi.org/10.1155/2021/6667743

[27] Luo, D., Xu, Z., Wang, Z., & Ran, W. (2021). China's Stem Cell Research and Knowledge Levels of Medical Practitioners and Students. Stem cells international , 2021 , 6667743. https://doi.org/10.1155/2021/6667743

[28] Zhang, J. Y. (2017). Lost in translation? accountability and governance of Clinical Stem Cell Research in China. Regenerative Medicine , 12 (6), 647–656. https://doi.org/10.2217/rme-2017-0035

[29] Wang, L., Wang, F., & Zhang, W. (2021). Bioethics in China’s biosecurity law: Forms, effects, and unsettled issues. Journal of law and the biosciences , 8(1). https://doi.org/10.1093/jlb/lsab019 https://academic.oup.com/jlb/article/8/1/lsab019/6299199

[30] Chen, H., Wei, T., Wang, H. et al. Association of China’s two-child policy with changes in number of births and birth defects rate, 2008–2017. BMC Public Health 22 , 434 (2022). https://doi.org/10.1186/s12889-022-12839-0

[31] Azuma, K. Regulatory Landscape of Regenerative Medicine in Japan. Curr Stem Cell Rep 1 , 118–128 (2015). https://doi.org/10.1007/s40778-015-0012-6

[32] Harris, R. (2005, May 19). Researchers Report Advance in Stem Cell Production . NPR. https://www.npr.org/2005/05/19/4658967/researchers-report-advance-in-stem-cell-production

[33] Park, S. (2012). South Korea steps up stem-cell work. Nature . https://doi.org/10.1038/nature.2012.10565

[34] Resnik, D. B., Shamoo, A. E., & Krimsky, S. (2006). Fraudulent human embryonic stem cell research in South Korea: lessons learned. Accountability in research , 13 (1), 101–109. https://doi.org/10.1080/08989620600634193 .

[35] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

[36] Association for the Advancement of Blood and Biotherapies. https://www.aabb.org/regulatory-and-advocacy/regulatory-affairs/regulatory-for-cellular-therapies/international-competent-authorities/saudi-arabia

[37] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia. BMC medical ethics , 21 (1), 35. https://doi.org/10.1186/s12910-020-00482-6

[38] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia. BMC medical ethics , 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

Culturally, autonomy practices follow a relational autonomy approach based on a paternalistic deontological health care model. The adherence to strict international research policies and religious pillars within the regulatory environment is a great foundation for research ethics. However, there is a need to develop locally targeted ethics approaches for research (as called for in Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6), this decision-making approach may help advise a research decision model. For more on the clinical cultural autonomy approaches, see: Alabdullah, Y. Y., Alzaid, E., Alsaad, S., Alamri, T., Alolayan, S. W., Bah, S., & Aljoudi, A. S. (2022). Autonomy and paternalism in Shared decision‐making in a Saudi Arabian tertiary hospital: A cross‐sectional study. Developing World Bioethics , 23 (3), 260–268. https://doi.org/10.1111/dewb.12355 ; Bukhari, A. A. (2017). Universal Principles of Bioethics and Patient Rights in Saudi Arabia (Doctoral dissertation, Duquesne University). https://dsc.duq.edu/etd/124; Ladha, S., Nakshawani, S. A., Alzaidy, A., & Tarab, B. (2023, October 26). Islam and Bioethics: What We All Need to Know . Columbia University School of Professional Studies. https://sps.columbia.edu/events/islam-and-bioethics-what-we-all-need-know

[39] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics. Research Ethics , 17 (2), 228-241. https://doi.org/10.1177/1747016120966779

[40] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics. Research Ethics , 17 (2), 228-241. https://doi.org/10.1177/1747016120966779

[41] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East. Nature 510, 189. https://doi.org/10.1038/510189a

[42] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East. Nature 510, 189. https://doi.org/10.1038/510189a

[43] The EU’s definition of autonomy relates to the capacity for creating ideas, moral insight, decisions, and actions without constraint, personal responsibility, and informed consent. However, the EU views autonomy as not completely able to protect individuals and depends on other principles, such as dignity, which “expresses the intrinsic worth and fundamental equality of all human beings.” Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[44] Council of Europe. Convention for the protection of Human Rights and Dignity of the Human Being with regard to the Application of Biology and Medicine: Convention on Human Rights and Biomedicine (ETS No. 164) https://www.coe.int/en/web/conventions/full-list?module=treaty-detail&treatynum=164 (forbidding the creation of embryos for research purposes only, and suggests embryos in vitro have protections.); Also see Drabiak-Syed B. K. (2013). New President, New Human Embryonic Stem Cell Research Policy: Comparative International Perspectives and Embryonic Stem Cell Research Laws in France. Biotechnology Law Report , 32 (6), 349–356. https://doi.org/10.1089/blr.2013.9865

[45] Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[46] Tomuschat, C., Currie, D. P., Kommers, D. P., & Kerr, R. (Trans.). (1949, May 23). Basic law for the Federal Republic of Germany. https://www.btg-bestellservice.de/pdf/80201000.pdf

[47] Regulation of Stem Cell Research in Germany . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-germany

[48] Regulation of Stem Cell Research in Finland . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-finland

[49] Regulation of Stem Cell Research in Spain . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-spain

[50] Some sources to consider regarding ethics models or regulatory oversights of other cultures not covered:

Kara MA. Applicability of the principle of respect for autonomy: the perspective of Turkey. J Med Ethics. 2007 Nov;33(11):627-30. doi: 10.1136/jme.2006.017400. PMID: 17971462; PMCID: PMC2598110.

Ugarte, O. N., & Acioly, M. A. (2014). The principle of autonomy in Brazil: one needs to discuss it ... Revista do Colegio Brasileiro de Cirurgioes , 41 (5), 374–377. https://doi.org/10.1590/0100-69912014005013

Bharadwaj, A., & Glasner, P. E. (2012). Local cells, global science: The rise of embryonic stem cell research in India . Routledge.

For further research on specific European countries regarding ethical and regulatory framework, we recommend this database: Regulation of Stem Cell Research in Europe . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-europe

[51] Klitzman, R. (2006). Complications of culture in obtaining informed consent. The American Journal of Bioethics, 6(1), 20–21. https://doi.org/10.1080/15265160500394671 see also: Ekmekci, P. E., & Arda, B. (2017). Interculturalism and Informed Consent: Respecting Cultural Differences without Breaching Human Rights. Cultura (Iasi, Romania) , 14 (2), 159–172.; For why trust is important in research, see also: Gray, B., Hilder, J., Macdonald, L., Tester, R., Dowell, A., & Stubbe, M. (2017). Are research ethics guidelines culturally competent? Research Ethics , 13 (1), 23-41. https://doi.org/10.1177/1747016116650235

[52] The Qur'an (M. Khattab, Trans.). (1965). Al-Mu’minun, 23: 12-14. https://quran.com/23

[53] Lenfest, Y. (2017, December 8). Islam and the beginning of human life . Bill of Health. https://blog.petrieflom.law.harvard.edu/2017/12/08/islam-and-the-beginning-of-human-life/

[54] Aksoy, S. (2005). Making regulations and drawing up legislation in Islamic countries under conditions of uncertainty, with special reference to embryonic stem cell research. Journal of Medical Ethics , 31: 399-403.; see also: Mahmoud, Azza. "Islamic Bioethics: National Regulations and Guidelines of Human Stem Cell Research in the Muslim World." Master's thesis, Chapman University, 2022. https://doi.org/10.36837/ chapman.000386

[55] Rashid, R. (2022). When does Ensoulment occur in the Human Foetus. Journal of the British Islamic Medical Association , 12 (4). ISSN 2634 8071. https://www.jbima.com/wp-content/uploads/2023/01/2-Ethics-3_-Ensoulment_Rafaqat.pdf.

[56] Sivaraman, M. & Noor, S. (2017). Ethics of embryonic stem cell research according to Buddhist, Hindu, Catholic, and Islamic religions: perspective from Malaysia. Asian Biomedicine,8(1) 43-52. https://doi.org/10.5372/1905-7415.0801.260

[57] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.), Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues (pp. 79-94). Berkeley: University of California Press. https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[58] Lecso, P. A. (1991). The Bodhisattva Ideal and Organ Transplantation. Journal of Religion and Health , 30 (1), 35–41. http://www.jstor.org/stable/27510629 ; Bodhisattva, S. (n.d.). The Key of Becoming a Bodhisattva . A Guide to the Bodhisattva Way of Life. http://www.buddhism.org/Sutras/2/BodhisattvaWay.htm

[59] There is no explicit religious reference to when life begins or how to conduct research that interacts with the concept of life. However, these are relevant verses pertaining to how the fetus is viewed. (( King James Bible . (1999). Oxford University Press. (original work published 1769))

Jerimiah 1: 5 “Before I formed thee in the belly I knew thee; and before thou camest forth out of the womb I sanctified thee…”

In prophet Jerimiah’s insight, God set him apart as a person known before childbirth, a theme carried within the Psalm of David.

Psalm 139: 13-14 “…Thou hast covered me in my mother's womb. I will praise thee; for I am fearfully and wonderfully made…”

These verses demonstrate David’s respect for God as an entity that would know of all man’s thoughts and doings even before birth.

[60] It should be noted that abortion is not supported as well.

[61] The Vatican. (1987, February 22). Instruction on Respect for Human Life in Its Origin and on the Dignity of Procreation Replies to Certain Questions of the Day . Congregation For the Doctrine of the Faith. https://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_con_cfaith_doc_19870222_respect-for-human-life_en.html

[62] The Vatican. (2000, August 25). Declaration On the Production and the Scientific and Therapeutic Use of Human Embryonic Stem Cells . Pontifical Academy for Life. https://www.vatican.va/roman_curia/pontifical_academies/acdlife/documents/rc_pa_acdlife_doc_20000824_cellule-staminali_en.html ; Ohara, N. (2003). Ethical Consideration of Experimentation Using Living Human Embryos: The Catholic Church’s Position on Human Embryonic Stem Cell Research and Human Cloning. Department of Obstetrics and Gynecology . Retrieved from https://article.imrpress.com/journal/CEOG/30/2-3/pii/2003018/77-81.pdf.

[63] Smith, G. A. (2022, May 23). Like Americans overall, Catholics vary in their abortion views, with regular mass attenders most opposed . Pew Research Center. https://www.pewresearch.org/short-reads/2022/05/23/like-americans-overall-catholics-vary-in-their-abortion-views-with-regular-mass-attenders-most-opposed/

[64] Rosner, F., & Reichman, E. (2002). Embryonic stem cell research in Jewish law. Journal of halacha and contemporary society , (43), 49–68.; Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.), Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues (pp. 79-94). Berkeley: University of California Press. https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[65] Schenker J. G. (2008). The beginning of human life: status of embryo. Perspectives in Halakha (Jewish Religious Law). Journal of assisted reproduction and genetics , 25 (6), 271–276. https://doi.org/10.1007/s10815-008-9221-6

[66] Ruttenberg, D. (2020, May 5). The Torah of Abortion Justice (annotated source sheet) . Sefaria. https://www.sefaria.org/sheets/234926.7?lang=bi&with=all&lang2=en

[67] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.), Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues (pp. 79-94). Berkeley: University of California Press. https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[68] Gert, B. (2007). Common morality: Deciding what to do . Oxford Univ. Press.

[69] World Medical Association (2013). World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA , 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053 Declaration of Helsinki – WMA – The World Medical Association .; see also: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research . U.S. Department of Health and Human Services. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html

[70] Zakarin Safier, L., Gumer, A., Kline, M., Egli, D., & Sauer, M. V. (2018). Compensating human subjects providing oocytes for stem cell research: 9-year experience and outcomes. Journal of assisted reproduction and genetics , 35 (7), 1219–1225. https://doi.org/10.1007/s10815-018-1171-z https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6063839/ see also: Riordan, N. H., & Paz Rodríguez, J. (2021). Addressing concerns regarding associated costs, transparency, and integrity of research in recent stem cell trial. Stem Cells Translational Medicine , 10 (12), 1715–1716. https://doi.org/10.1002/sctm.21-0234

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Olivia Bowers

MS Bioethics Columbia University (Disclosure: affiliated with Voices in Bioethics)

Mifrah Hayath

SM Candidate Harvard Medical School, MS Biotechnology Johns Hopkins University

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Burden of Mental Disorders and Suicide Attributable to Childhood Maltreatment

1 The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, New South Wales, Australia
2 Department of Clinical, Educational, and Health Psychology, Division of Psychology and Language Sciences, University College London, London, United Kingdom
3 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York

Question What proportion of mental health conditions and burden in Australia is attributable to childhood maltreatment?

Findings This meta-analysis found, after controlling for genetic and environmental confounding, that childhood maltreatment accounts for 21% to 41% of common mental health conditions in Australia, with the highest attributable proportion for suicide attempts and self-harm. More than 1.8 million cases of depressive, anxiety, and substance use disorders, 66 143 years of life lost, and 184 636 disability-adjusted life-years could be prevented if childhood maltreatment was eradicated in Australia.

Meaning Efforts to prevent child maltreatment exposure have the potential to improve mental health at a population level in Australia.

Importance The proportion of mental disorders and burden causally attributable to childhood maltreatment is unknown.

Objective To determine the contribution of child maltreatment to mental health conditions in Australia, accounting for genetic and environmental confounding.

Design, Setting, and Participants This meta-analysis involved an epidemiological assessment accounting for genetic and environmental confounding between maltreatment and mental health and 3 cross-sectional national surveys: the Australian Child Maltreatment Study (ACMS) 2023, National Study of Mental Health and Well-being 2020-2022, and Australian Burden of Disease Study 2023. Causal estimates were derived on the association between childhood maltreatment and mental health conditions from a meta-analysis of quasi-experimental studies. This was combined with the prevalence of maltreatment from the ACMS to calculate the population attributable fraction (PAF). The PAF was applied to the number and burden of mental health conditions in Australia, sourced from 2 population-based, nationally representative surveys of Australians aged 16 to 85 years, to generate the number and associated burden of mental disorders attributable to child maltreatment.

Exposure Physical abuse, sexual abuse, emotional abuse, or neglect prior to age 18 years.

Main Outcomes and Measures Proportion and number of cases, years of life lost, years lived with disability, and disability-adjusted life-years of mental health conditions (anxiety, depression, harmful alcohol and drug use, self-harm, and suicide attempt) attributable to childhood maltreatment.

Results Meta-analytic estimates were generated from 34 studies and 54 646 participants and applied to prevalence estimates of childhood maltreatment generated from 8503 Australians. Childhood maltreatment accounted for a substantial proportion of mental health conditions, ranging from 21% (95% CI, 13%-28%) for depression to 41% (95% CI, 27%-54%) of suicide attempts. More than 1.8 million cases of depressive, anxiety, and substance use disorders could be prevented if childhood maltreatment was eradicated. Maltreatment accounted for 66 143 years of life lost (95% CI, 43 313-87 314), primarily through suicide, and 184 636 disability-adjusted life-years (95% CI, 109 321-252 887).

Conclusions and Relevance This study provides the first estimates of the causal contribution of child maltreatment to mental health in Australia. Results highlight the urgency of preventing child maltreatment to reduce the population prevalence and burden of mental disorders.

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Experimental R&D Value Added Statistics for the U.S. and States Now Available

Research and development activity accounted for 2.3 percent of the U.S. economy in 2021, according to new experimental statistics released today by the Bureau of Economic Analysis. R&D as a share of each state’s gross domestic product, or GDP, ranged from 0.3 percent in Louisiana and Wyoming to 6.3 percent in New Mexico, home to federally funded Los Alamos National Laboratory and Sandia National Laboratories.

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These statistics are part of a new Research and Development Satellite Account BEA is developing in partnership with the National Center for Science and Engineering Statistics of the National Science Foundation . The statistics complement BEA’s national data on R&D investment and provide BEA’s first state-by-state numbers on R&D.

The new statistics, covering 2017 to 2021, provide information on the contribution of R&D to GDP (known as R&D value added), compensation, and employment for the nation, all 50 states, and the District of Columbia. In the state statistics, R&D is attributed to the state where the R&D is performed.

Some highlights from the newly released statistics:

R&D activity is highly concentrated in the United States. The top ten R&D-producing states account for 70 percent of U.S. R&D value added. California alone accounts for almost a third of U.S. R&D. Other top R&D-producing states include Washington, Massachusetts, Texas, and New York.

chart-RD-state-ranking-value-added-vertical

Treating R&D as a sector allows for comparisons with other industries and sectors of the U.S. economy. For instance, R&D’s share of U.S. value added in 2021 is similar to hospitals (2.4 percent) and food services and drinking places (2.2 percent).

Comparison of R and D with Other Sectors

Eighty-five percent of R&D value added is generated by the business sector, followed by government, and nonprofit institutions serving households.

Within the business sector, the professional, scientific, and technical services industry accounts for 40 percent of business R&D value added. Information (15 percent), chemical manufacturing (12 percent), and computer and electronic product manufacturing (11 percent) also account for sizable shares.

chart-RD-industry-and-biz-sector-comparison

Visit the R&D Satellite Account on BEA’s website for the full set of experimental statistics and accompanying information. To help refine the methodology and presentation of these statistics, BEA is seeking your feedback. Please submit comments to [email protected] .

Food insecurity is associated with poor hypertension management in the Eastern Caribbean

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Background Limited evidence exists on the association between food insecurity (FI) and blood pressure control in the Caribbean despite the high burden of both. The objective of this study is to examine the relationship between FI and hypertension prevalence, awareness, and control in the Eastern Caribbean. Methods and Findings We conducted a cross-sectional analysis of baseline data (2013-2018) from the Eastern Caribbean Health Outcomes Research Network Cohort Study (n=2961). Food insecurity was measured using the Latin American and Caribbean Food Security Scale (ELCSA) and classified as 0=no FI, 1-6 mild/moderate FI, and 7-9 severe FI. Hypertension was defined by the Seventh Report of the Joint National Committee on Prevention. Logistic regression modeling was conducted to examine the relationship between FI and hypertension prevalence, awareness, and control, adjusting for age, sex, educational attainment, site, and usual source of care. Prevalence of FI was 28 percent among participants and was higher in Puerto Rico and Trinidad and Tobago compared to other sites. Seventeen percent of the participants experienced low, 6 percent moderate, and 4 percent experienced severe FI. Aggregate model results showed no association between FI and hypertension outcomes. Sex-stratified results showed moderate (OR=2.65, CI=1.25-5.65) and severe FI (OR=3.69, CI=1.20-11.31) were positively associated with lack of control among women. Limitations of this study include the cross-sectional design, small sample size, and the average age of our cohort. Cross-sectional design precluded the ability to make inferences about temporality between FI and HTN prevalence and awareness. Small sample size precluded the ability to detect statistically significant differences despite strong odds ratios for model results like lack of control. Conclusions Findings align with prior evidence of greater FI prevalence among women and negative health impact. Nutrition policies are needed to reduce the overall FI burden in the Caribbean and increase access to affordable, nutritious foods.

Competing Interest Statement

The authors have declared no competing interest.

Clinical Trial

Funding statement.

The study sponsor has no role in the study design, collection, analysis, and interpretation of the data. To the best of our knowledge, there are no relevant conflicts of interest, financial or otherwise, relevant to this study and the publication thereof to declare.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The ECS study was approved by the Yale University Human Subjects Investigation Committee, the Institutional Review Boards of the University of Puerto Rico Medical Sciences Campus, the University of the Virgin Islands, and the University of the West Indies Cave Hill Campus, as well as by the Ministry of Health of Trinidad and Tobago. The current analysis was approved by the Data Access and Scientific Review Committee of the ECS. This study was reported according to STROBE guidelines.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

The corresponding author had full access to all the data in the study and takes responsibility for its integrity and the data analysis. Data described in the manuscript will be made available upon reasonable request.

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