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Reliability and Validity – Definitions, Types & Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On October 26, 2023

A researcher must test the collected data before making any conclusion. Every  research design  needs to be concerned with reliability and validity to measure the quality of the research.

What is Reliability?

Reliability refers to the consistency of the measurement. Reliability shows how trustworthy is the score of the test. If the collected data shows the same results after being tested using various methods and sample groups, the information is reliable. If your method has reliability, the results will be valid.

Example: If you weigh yourself on a weighing scale throughout the day, you’ll get the same results. These are considered reliable results obtained through repeated measures.

Example: If a teacher conducts the same math test of students and repeats it next week with the same questions. If she gets the same score, then the reliability of the test is high.

What is the Validity?

Validity refers to the accuracy of the measurement. Validity shows how a specific test is suitable for a particular situation. If the results are accurate according to the researcher’s situation, explanation, and prediction, then the research is valid. 

If the method of measuring is accurate, then it’ll produce accurate results. If a method is reliable, then it’s valid. In contrast, if a method is not reliable, it’s not valid. 

Example:  Your weighing scale shows different results each time you weigh yourself within a day even after handling it carefully, and weighing before and after meals. Your weighing machine might be malfunctioning. It means your method had low reliability. Hence you are getting inaccurate or inconsistent results that are not valid.

Example:  Suppose a questionnaire is distributed among a group of people to check the quality of a skincare product and repeated the same questionnaire with many groups. If you get the same response from various participants, it means the validity of the questionnaire and product is high as it has high reliability.

Most of the time, validity is difficult to measure even though the process of measurement is reliable. It isn’t easy to interpret the real situation.

Example:  If the weighing scale shows the same result, let’s say 70 kg each time, even if your actual weight is 55 kg, then it means the weighing scale is malfunctioning. However, it was showing consistent results, but it cannot be considered as reliable. It means the method has low reliability.

Internal Vs. External Validity

One of the key features of randomised designs is that they have significantly high internal and external validity.

Internal validity  is the ability to draw a causal link between your treatment and the dependent variable of interest. It means the observed changes should be due to the experiment conducted, and any external factor should not influence the  variables .

Example: age, level, height, and grade.

External validity  is the ability to identify and generalise your study outcomes to the population at large. The relationship between the study’s situation and the situations outside the study is considered external validity.

Also, read about Inductive vs Deductive reasoning in this article.

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Threats to Interval Validity

Threats of external validity, how to assess reliability and validity.

Reliability can be measured by comparing the consistency of the procedure and its results. There are various methods to measure validity and reliability. Reliability can be measured through  various statistical methods  depending on the types of validity, as explained below:

Types of Reliability

Types of validity.

As we discussed above, the reliability of the measurement alone cannot determine its validity. Validity is difficult to be measured even if the method is reliable. The following type of tests is conducted for measuring validity. 

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How to Increase Reliability?

• Use an appropriate questionnaire to measure the competency level.
• Ensure a consistent environment for participants
• Make the participants familiar with the criteria of assessment.
• Train the participants appropriately.
• Analyse the research items regularly to avoid poor performance.

How to Increase Validity?

Ensuring Validity is also not an easy job. A proper functioning method to ensure validity is given below:

• The reactivity should be minimised at the first concern.
• The Hawthorne effect should be reduced.
• The respondents should be motivated.
• The intervals between the pre-test and post-test should not be lengthy.
• Dropout rates should be avoided.
• The inter-rater reliability should be ensured.
• Control and experimental groups should be matched with each other.

How to Implement Reliability and Validity in your Thesis?

According to the experts, it is helpful if to implement the concept of reliability and Validity. Especially, in the thesis and the dissertation, these concepts are adopted much. The method for implementation given below:

Frequently Asked Questions

What is reliability and validity in research.

Reliability in research refers to the consistency and stability of measurements or findings. Validity relates to the accuracy and truthfulness of results, measuring what the study intends to. Both are crucial for trustworthy and credible research outcomes.

What is validity?

Validity in research refers to the extent to which a study accurately measures what it intends to measure. It ensures that the results are truly representative of the phenomena under investigation. Without validity, research findings may be irrelevant, misleading, or incorrect, limiting their applicability and credibility.

What is reliability?

Reliability in research refers to the consistency and stability of measurements over time. If a study is reliable, repeating the experiment or test under the same conditions should produce similar results. Without reliability, findings become unpredictable and lack dependability, potentially undermining the study’s credibility and generalisability.

What is reliability in psychology?

In psychology, reliability refers to the consistency of a measurement tool or test. A reliable psychological assessment produces stable and consistent results across different times, situations, or raters. It ensures that an instrument’s scores are not due to random error, making the findings dependable and reproducible in similar conditions.

What is test retest reliability?

Test-retest reliability assesses the consistency of measurements taken by a test over time. It involves administering the same test to the same participants at two different points in time and comparing the results. A high correlation between the scores indicates that the test produces stable and consistent results over time.

How to improve reliability of an experiment?

• Standardise procedures and instructions.
• Use consistent and precise measurement tools.
• Train observers or raters to reduce subjective judgments.
• Increase sample size to reduce random errors.
• Conduct pilot studies to refine methods.
• Repeat measurements or use multiple methods.
• Address potential sources of variability.

What is the difference between reliability and validity?

Reliability refers to the consistency and repeatability of measurements, ensuring results are stable over time. Validity indicates how well an instrument measures what it’s intended to measure, ensuring accuracy and relevance. While a test can be reliable without being valid, a valid test must inherently be reliable. Both are essential for credible research.

Are interviews reliable and valid?

Interviews can be both reliable and valid, but they are susceptible to biases. The reliability and validity depend on the design, structure, and execution of the interview. Structured interviews with standardised questions improve reliability. Validity is enhanced when questions accurately capture the intended construct and when interviewer biases are minimised.

Are IQ tests valid and reliable?

IQ tests are generally considered reliable, producing consistent scores over time. Their validity, however, is a subject of debate. While they effectively measure certain cognitive skills, whether they capture the entirety of “intelligence” or predict success in all life areas is contested. Cultural bias and over-reliance on tests are also concerns.

Are questionnaires reliable and valid?

Questionnaires can be both reliable and valid if well-designed. Reliability is achieved when they produce consistent results over time or across similar populations. Validity is ensured when questions accurately measure the intended construct. However, factors like poorly phrased questions, respondent bias, and lack of standardisation can compromise their reliability and validity.

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Impact AND credibility matter when researchers evaluate research

by Veronique Kiermer, Iain Hrynaszkiewicz, & James Harney.

Today we’ve posted a report , along with accompanying data, on qualitative research we conducted about how researchers assess the credibility and impact of research. This study, which has not yet been peer reviewed, was supported by a grant from the Alfred P. Sloan Foundation and conducted with the assistance of the American Society for Cell Biology . The findings will inform future PLOS activities to support improved research assessment practices — specifically to support efforts to move emphasis towards individual research outputs and away from journal-level metrics. 

As we wrote in October 2020, we are interested in how researchers evaluate research outputs when (1) conducting their own research, and (2) when they take part in committees for hiring or grant review. In particular, we were interested in how researchers make judgments about the credibility and impact of the research outputs — including papers, preprints, research data — that they encounter in these contexts.

We interviewed 52 cell biology researchers.  Our approach focused on the goals they are trying to achieve (e.g.”identify impactful research to read”), rather than the tools they are presently using to carry out these tasks. By focusing on researchers’ goals (the what ) rather than how they are achieving them, we sought to better understand how we might influence those practices. This qualitative research will be followed by survey work to better quantify our findings. This will provide insights into opportunities for better solutions for improved research assessment. In particular, we’ll understand what signals of credibility and impact might provide researchers with more useful ways than journal impact factor or journal prestige to assess the quality and credibility of individual studies and individual researchers.

Our results confirmed our initial hypothesis that the credibility (or trustworthiness) of research outputs is the central concern for researchers when conducting their own research, and that impact was a strong focus when researchers are part of hiring or grant review committees. But we established that researchers also assess attributes of research outputs related to reproducibility, quality, and novelty. 

In addition, we found that researchers said they assessed credibility in committees more frequently than we anticipated, given that impact considerations — including journal impact factor — are prevalent in committee guidance and research assessment objectives (see for example McKiernan et al . (2019), Niles et al. (2020), Alperin et al. (2020), and Sugimoto & Larivière (2018)).

Our interviews confirmed that convenient proxies for credibility and impact, usually those based on journals, are used pervasively and are common in both research discovery and committee activities. 

Our research also indicates that when researchers inspect publications to evaluate credibility they try to minimize the amount of time they spend reading and understanding publications. Their tactics included selective reading of the abstracts, figures, and methods sections. Sometimes they said that they also look for signals such as whether data was available and had been reused, whether peer-reviewed versions of preprints have been published, and whether open peer review reports were available. 

Insights that help us better understand what researchers’ goals are and how they make judgements about credibility when discovering and reading research may offer opportunities to provide more reliable signals that help them with these tasks, yet are better tailored for credibility judgments than journal-level metrics. The stated importance of assessing credibility by researchers who participate in research assessment committees also suggests an opportunity for funders and institutions to better align their guidelines with the practice and motivations of committee members. 

After our follow-up survey work to validate these preliminary findings, we will report back and hope that this research will help others in the understanding and development of better methods of research assessment.

Note: The following announcement was published by the East African Science and Technology Commission (EASTECO). PLOS is a proud partner of EASTECO…

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• Reliability vs Validity in Research | Differences, Types & Examples

Reliability vs Validity in Research | Differences, Types & Examples

Published on 3 May 2022 by Fiona Middleton . Revised on 10 October 2022.

Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method , technique, or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.

It’s important to consider reliability and validity when you are creating your research design , planning your methods, and writing up your results, especially in quantitative research .

Table of contents

Understanding reliability vs validity, how are reliability and validity assessed, how to ensure validity and reliability in your research, where to write about reliability and validity in a thesis.

Reliability and validity are closely related, but they mean different things. A measurement can be reliable without being valid. However, if a measurement is valid, it is usually also reliable.

What is reliability?

Reliability refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable.

What is validity?

Validity refers to how accurately a method measures what it is intended to measure. If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world.

High reliability is one indicator that a measurement is valid. If a method is not reliable, it probably isn’t valid.

However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation.

Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect your data must be valid: the research must be measuring what it claims to measure. This ensures that your discussion of the data and the conclusions you draw are also valid.

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Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory. Methods of estimating reliability and validity are usually split up into different types.

Types of reliability

Different types of reliability can be estimated through various statistical methods.

Types of validity

The validity of a measurement can be estimated based on three main types of evidence. Each type can be evaluated through expert judgement or statistical methods.

To assess the validity of a cause-and-effect relationship, you also need to consider internal validity (the design of the experiment ) and external validity (the generalisability of the results).

The reliability and validity of your results depends on creating a strong research design , choosing appropriate methods and samples, and conducting the research carefully and consistently.

Ensuring validity

If you use scores or ratings to measure variations in something (such as psychological traits, levels of ability, or physical properties), it’s important that your results reflect the real variations as accurately as possible. Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data .

• Choose appropriate methods of measurement

Ensure that your method and measurement technique are of high quality and targeted to measure exactly what you want to know. They should be thoroughly researched and based on existing knowledge.

For example, to collect data on a personality trait, you could use a standardised questionnaire that is considered reliable and valid. If you develop your own questionnaire, it should be based on established theory or the findings of previous studies, and the questions should be carefully and precisely worded.

• Use appropriate sampling methods to select your subjects

To produce valid generalisable results, clearly define the population you are researching (e.g., people from a specific age range, geographical location, or profession). Ensure that you have enough participants and that they are representative of the population.

Ensuring reliability

Reliability should be considered throughout the data collection process. When you use a tool or technique to collect data, it’s important that the results are precise, stable, and reproducible.

• Apply your methods consistently

Plan your method carefully to make sure you carry out the same steps in the same way for each measurement. This is especially important if multiple researchers are involved.

For example, if you are conducting interviews or observations, clearly define how specific behaviours or responses will be counted, and make sure questions are phrased the same way each time.

• Standardise the conditions of your research

When you collect your data, keep the circumstances as consistent as possible to reduce the influence of external factors that might create variation in the results.

For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions.

It’s appropriate to discuss reliability and validity in various sections of your thesis or dissertation or research paper. Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy.

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Validity & Reliability In Research

A Plain-Language Explanation (With Examples)

By: Derek Jansen (MBA) | Expert Reviewer: Kerryn Warren (PhD) | September 2023

Validity and reliability are two related but distinctly different concepts within research. Understanding what they are and how to achieve them is critically important to any research project. In this post, we’ll unpack these two concepts as simply as possible.

This post is based on our popular online course, Research Methodology Bootcamp . In the course, we unpack the basics of methodology  using straightfoward language and loads of examples. If you’re new to academic research, you definitely want to use this link to get 50% off the course (limited-time offer).

Overview: Validity & Reliability

• The big picture
• Validity 101
• Reliability 101 
• Key takeaways

First, The Basics…

First, let’s start with a big-picture view and then we can zoom in to the finer details.

Validity and reliability are two incredibly important concepts in research, especially within the social sciences. Both validity and reliability have to do with the measurement of variables and/or constructs – for example, job satisfaction, intelligence, productivity, etc. When undertaking research, you’ll often want to measure these types of constructs and variables and, at the simplest level, validity and reliability are about ensuring the quality and accuracy of those measurements .

As you can probably imagine, if your measurements aren’t accurate or there are quality issues at play when you’re collecting your data, your entire study will be at risk. Therefore, validity and reliability are very important concepts to understand (and to get right). So, let’s unpack each of them.

What Is Validity?

In simple terms, validity (also called “construct validity”) is all about whether a research instrument accurately measures what it’s supposed to measure .

For example, let’s say you have a set of Likert scales that are supposed to quantify someone’s level of overall job satisfaction. If this set of scales focused purely on only one dimension of job satisfaction, say pay satisfaction, this would not be a valid measurement, as it only captures one aspect of the multidimensional construct. In other words, pay satisfaction alone is only one contributing factor toward overall job satisfaction, and therefore it’s not a valid way to measure someone’s job satisfaction.

Oftentimes in quantitative studies, the way in which the researcher or survey designer interprets a question or statement can differ from how the study participants interpret it . Given that respondents don’t have the opportunity to ask clarifying questions when taking a survey, it’s easy for these sorts of misunderstandings to crop up. Naturally, if the respondents are interpreting the question in the wrong way, the data they provide will be pretty useless . Therefore, ensuring that a study’s measurement instruments are valid – in other words, that they are measuring what they intend to measure – is incredibly important.

There are various types of validity and we’re not going to go down that rabbit hole in this post, but it’s worth quickly highlighting the importance of making sure that your research instrument is tightly aligned with the theoretical construct you’re trying to measure .  In other words, you need to pay careful attention to how the key theories within your study define the thing you’re trying to measure – and then make sure that your survey presents it in the same way.

For example, sticking with the “job satisfaction” construct we looked at earlier, you’d need to clearly define what you mean by job satisfaction within your study (and this definition would of course need to be underpinned by the relevant theory). You’d then need to make sure that your chosen definition is reflected in the types of questions or scales you’re using in your survey . Simply put, you need to make sure that your survey respondents are perceiving your key constructs in the same way you are. Or, even if they’re not, that your measurement instrument is capturing the necessary information that reflects your definition of the construct at hand.

If all of this talk about constructs sounds a bit fluffy, be sure to check out Research Methodology Bootcamp , which will provide you with a rock-solid foundational understanding of all things methodology-related. Remember, you can take advantage of our 60% discount offer using this link.

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What Is Reliability?

As with validity, reliability is an attribute of a measurement instrument – for example, a survey, a weight scale or even a blood pressure monitor. But while validity is concerned with whether the instrument is measuring the “thing” it’s supposed to be measuring, reliability is concerned with consistency and stability . In other words, reliability reflects the degree to which a measurement instrument produces consistent results when applied repeatedly to the same phenomenon , under the same conditions .

As you can probably imagine, a measurement instrument that achieves a high level of consistency is naturally more dependable (or reliable) than one that doesn’t – in other words, it can be trusted to provide consistent measurements . And that, of course, is what you want when undertaking empirical research. If you think about it within a more domestic context, just imagine if you found that your bathroom scale gave you a different number every time you hopped on and off of it – you wouldn’t feel too confident in its ability to measure the variable that is your body weight 🙂

It’s worth mentioning that reliability also extends to the person using the measurement instrument . For example, if two researchers use the same instrument (let’s say a measuring tape) and they get different measurements, there’s likely an issue in terms of how one (or both) of them are using the measuring tape. So, when you think about reliability, consider both the instrument and the researcher as part of the equation.

As with validity, there are various types of reliability and various tests that can be used to assess the reliability of an instrument. A popular one that you’ll likely come across for survey instruments is Cronbach’s alpha , which is a statistical measure that quantifies the degree to which items within an instrument (for example, a set of Likert scales) measure the same underlying construct . In other words, Cronbach’s alpha indicates how closely related the items are and whether they consistently capture the same concept . 

Recap: Key Takeaways

Alright, let’s quickly recap to cement your understanding of validity and reliability:

• Validity is concerned with whether an instrument (e.g., a set of Likert scales) is measuring what it’s supposed to measure
• Reliability is concerned with whether that measurement is consistent and stable when measuring the same phenomenon under the same conditions.

In short, validity and reliability are both essential to ensuring that your data collection efforts deliver high-quality, accurate data that help you answer your research questions . So, be sure to always pay careful attention to the validity and reliability of your measurement instruments when collecting and analysing data. As the adage goes, “rubbish in, rubbish out” – make sure that your data inputs are rock-solid.

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• Volume 18, Issue 2
• Issues of validity and reliability in qualitative research
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• Helen Noble 1 ,
• Joanna Smith 2
• 1 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
• 2 School of Human and Health Sciences, University of Huddersfield , Huddersfield , UK
• Correspondence to Dr Helen Noble School of Nursing and Midwifery, Queens's University Belfast, Medical Biology Centre, 97 Lisburn Rd, Belfast BT9 7BL, UK; helen.noble{at}qub.ac.uk

https://doi.org/10.1136/eb-2015-102054

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Evaluating the quality of research is essential if findings are to be utilised in practice and incorporated into care delivery. In a previous article we explored ‘bias’ across research designs and outlined strategies to minimise bias. 1 The aim of this article is to further outline rigour, or the integrity in which a study is conducted, and ensure the credibility of findings in relation to qualitative research. Concepts such as reliability, validity and generalisability typically associated with quantitative research and alternative terminology will be compared in relation to their application to qualitative research. In addition, some of the strategies adopted by qualitative researchers to enhance the credibility of their research are outlined.

Are the terms reliability and validity relevant to ensuring credibility in qualitative research?

Although the tests and measures used to establish the validity and reliability of quantitative research cannot be applied to qualitative research, there are ongoing debates about whether terms such as validity, reliability and generalisability are appropriate to evaluate qualitative research. 2–4 In the broadest context these terms are applicable, with validity referring to the integrity and application of the methods undertaken and the precision in which the findings accurately reflect the data, while reliability describes consistency within the employed analytical procedures. 4 However, if qualitative methods are inherently different from quantitative methods in terms of philosophical positions and purpose, then alterative frameworks for establishing rigour are appropriate. 3 Lincoln and Guba 5 offer alternative criteria for demonstrating rigour within qualitative research namely truth value, consistency and neutrality and applicability. Table 1 outlines the differences in terminology and criteria used to evaluate qualitative research.

• View inline

Terminology and criteria used to evaluate the credibility of research findings

What strategies can qualitative researchers adopt to ensure the credibility of the study findings?

Unlike quantitative researchers, who apply statistical methods for establishing validity and reliability of research findings, qualitative researchers aim to design and incorporate methodological strategies to ensure the ‘trustworthiness’ of the findings. Such strategies include:

Accounting for personal biases which may have influenced findings; 6

Acknowledging biases in sampling and ongoing critical reflection of methods to ensure sufficient depth and relevance of data collection and analysis; 3

Meticulous record keeping, demonstrating a clear decision trail and ensuring interpretations of data are consistent and transparent; 3 , 4

Establishing a comparison case/seeking out similarities and differences across accounts to ensure different perspectives are represented; 6 , 7

Including rich and thick verbatim descriptions of participants’ accounts to support findings; 7

Demonstrating clarity in terms of thought processes during data analysis and subsequent interpretations 3 ;

Engaging with other researchers to reduce research bias; 3

Respondent validation: includes inviting participants to comment on the interview transcript and whether the final themes and concepts created adequately reflect the phenomena being investigated; 4

Data triangulation, 3 , 4 whereby different methods and perspectives help produce a more comprehensive set of findings. 8 , 9

Table 2 provides some specific examples of how some of these strategies were utilised to ensure rigour in a study that explored the impact of being a family carer to patients with stage 5 chronic kidney disease managed without dialysis. 10

Strategies for enhancing the credibility of qualitative research

In summary, it is imperative that all qualitative researchers incorporate strategies to enhance the credibility of a study during research design and implementation. Although there is no universally accepted terminology and criteria used to evaluate qualitative research, we have briefly outlined some of the strategies that can enhance the credibility of study findings.

• Sandelowski M
• Lincoln YS ,
• Barrett M ,
• Mayan M , et al
• Greenhalgh T
• Lingard L ,

Twitter Follow Joanna Smith at @josmith175 and Helen Noble at @helnoble

Competing interests None.

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Research Reliability

Reliability refers to whether or not you get the same answer by using an instrument to measure something more than once. In simple terms, research reliability is the degree to which research method produces stable and consistent results.

A specific measure is considered to be reliable if its application on the same object of measurement number of times produces the same results.

Research reliability can be divided into three categories:

1. Test-retest reliability relates to the measure of reliability that has been obtained by conducting the same test more than one time over period of time with the participation of the same sample group.

Example: Employees of ABC Company may be asked to complete the same questionnaire about   employee job satisfaction two times with an interval of one week, so that test results can be compared to assess stability of scores.

2. Parallel forms reliability relates to a measure that is obtained by conducting assessment of the same phenomena with the participation of the same sample group via more than one assessment method.

Example: The levels of employee satisfaction of ABC Company may be assessed with questionnaires, in-depth interviews and focus groups and results can be compared.

3. Inter-rater reliability as the name indicates relates to the measure of sets of results obtained by different assessors using same methods. Benefits and importance of assessing inter-rater reliability can be explained by referring to subjectivity of assessments.

Example: Levels of employee motivation at ABC Company can be assessed using observation method by two different assessors, and inter-rater reliability relates to the extent of difference between the two assessments.

4. Internal consistency reliability is applied to assess the extent of differences within the test items that explore the same construct produce similar results. It can be represented in two main formats.

a) average inter-item correlation is a specific form of internal consistency that is obtained by applying the same construct on each item of the test

b) split-half reliability as another type of internal consistency reliability involves all items of a test to be ‘spitted in half’.

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• 29 April 2021

Good research begins long before papers get written

You have full access to this article via your institution.

Many researchers are keen to improve transparency and reproducibility in science. Publishers have designed a new reporting framework to help. Getty

In 2013, Nature began asking the authors of life-sciences papers to provide extra information in a bid to tackle the pressing problem of poor reproducibility in research. According to one survey of Nature authors conducted in 2016–17, 86% of respondents considered poor reproducibility to be a growing challenge in the life sciences.

Researchers in these fields are now asked to use a structured reporting summary for their manuscript submissions. Among other things, the checklist requires authors to state whether their experimental findings have been replicated; how they determined an appropriate sample size; whether they randomized samples; and whether data have been assessed by researchers who did not know which group they were assessing.

Such a checklist, which is provided to peer reviewers and published with each life-sciences paper, has helped to improve transparency in the reporting of research 1 , 2 . But editors from many journals and researchers recognize that there is still work to be done.

Checklists work to improve science

In 2017, a group met to discuss how such a systematic approach to transparency and reproducibility could be improved and adopted across more journals. The result is the MDAR (Materials Design Analysis Reporting) Framework, which has just been published 3 .

The MDAR initiative is the result of an effort by editors at Science , Cell Press, the Public Library of Science, eLife , Wiley and in the Nature Portfolio, working with experts in reproducibility and research improvement. The objective is to encourage more-detailed disclosures in four areas of life-sciences manuscripts: materials (such as reagents, laboratory animals and model organisms); data; analysis (including code and statistics); and reporting (adhering to discipline-specific guidelines). Nature ’s standards cover most of the MDAR initiative’s objectives, but there are plans for further alignment. At the same time, the group is encouraging other journals beyond the founding members to sign up.

Publishers are not the only important players in this arena, however. A key test will be the extent to which funders and universities also support the new framework. Any initiative that improves transparency and reproducibility should be welcomed. But MDAR comes at a time when some of Europe’s largest funders have announced plans to reduce what they regard as burdens and bureaucracy in research. The European Commission, for example, is undertaking a review of its pharmaceuticals legislation, partly in an effort to reduce red tape. And the UK government has appointed Adam Tickell, vice-chancellor of the University of Sussex in Brighton, to lead a review with the explicit aim of reducing red tape for researchers.

For these funders, such measures are designed, in part, to remove perceived obstacles to innovation and competitiveness in science. But if the result is reduced funding for research management and administrative support — which are essential to the success of implementing quality measures — that will have an impact on efforts to improve transparency and reproducibility.

All of those involved — funders, publishers and research managers and administrators — need to be on the same page in this respect. Europe’s national and regional funders, in particular, must not forget that efforts to enhance transparency and reproducibility are fundamental to the scientific process — and to scientific integrity — and are far from being red tape.

Equality and diversity efforts do not ‘burden’ research

Fortunately, many researchers appreciate this. In a pilot study in 2019, the MDAR checklist was tested by 33 journal editors and 211 authors working on 289 manuscripts. Most respondents from both groups said they found the expanded checklist helpful. And in response to Nature ’s 2016–17 survey, some three-quarters of respondents said that they would use the journal’s checklist to some extent, whether or not they were planning to submit their draft to a Nature journal.

In a parallel and welcome development, researchers and publishers, including the Nature journals, are embracing a format called Registered Reports in which scientists submit a detailed plan for a research project, including the question or questions being asked, study design and methodology. If editors approve it for peer review, and reviewers think the proposal is sufficiently robust, the journal commits to publishing the work, regardless of the outcome.

All participants involved in the research process know that good research starts long before papers get written. Progress in science comes not with the sparkle of glitter or the crash of cymbals, but in carefully crafted prose after years of deliberations, experimental testing and continuous refinement. The MDAR Framework is one such achievement. The time has come for science institutions to catch up with the growing desire among researchers for greater transparency and reproducibility. MDAR won’t solve everything, but, if it can make research more reliable, then it will go some way towards achieving its promise.

Nature 593 , 8 (2021)

doi: https://doi.org/10.1038/d41586-021-01167-9

Hair, K. et al. Res. Integr. Peer Rev. 4 , 12 (2019).

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Macleod, M. et al. Proc. Natl Acad. Sci. USA 118 , e2103238118 (2021).

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Critically Thinking About Qualitative Versus Quantitative Research

What should we do regarding our research questions and methodology.

Posted January 26, 2022 | Reviewed by Davia Sills

• Neither a quantitative nor a qualitative methodology is the right way to approach every scientific question.
• Rather, the nature of the question determines which methodology is best suited to address it.
• Often, researchers benefit from a mixed approach that incorporates both quantitative and qualitative methodologies.

As a researcher who has used a wide variety of methodologies, I understand the importance of acknowledging that we, as researchers, do not pick the methodology; rather, the research question dictates it. So, you can only imagine how annoyed I get when I hear of undergraduates designing their research projects based on preconceived notions, like "quantitative is more straightforward," or "qualitative is easier." Apart from the fact that neither of these assertions is actually the case, these young researchers are blatantly missing one of the foundational steps of good research: If you are interested in researching a particular area, you must get to know the area (i.e., through reading) and then develop a question based on that reading.

The nature of the question will dictate the most appropriate methodological approach.

I’ve debated with researchers in the past who are "exclusively" qualitative or "exclusively" quantitative. Depending on the rationale for their exclusivity, I might question a little deeper, learn something, and move on, or I might debate further. Sometimes, I throw some contentious statements out to see what the responses are like. For example, "Qualitative research, in isolation, is nothing but glorified journalism . " This one might not be new to you. Yes, qualitative is flawed, but so, too, is quantitative.

Let's try this one: "Numbers don’t lie, just the researchers who interpret them." If researchers are going to have a pop at qual for subjectivity, why don’t they recognize the same issues in quant? The numbers in a results section may be objectively correct, but their meaningfulness is only made clear through the interpretation of the human reporting them. This is not a criticism but is an important observation for those who believe in the absolute objectivity of quantitative reporting. The subjectivity associated with this interpretation may miss something crucial in the interpretation of the numbers because, hey, we’re only human.

With that, I love quantitative research, but I’m not unreasonable about it. Let’s say we’ve evaluated a three-arm RCT—the new therapeutic intervention is significantly efficacious, with a large effect, for enhancing "x" in people living with "y." One might conclude that this intervention works and that we must conduct further research on it to further support its efficacy—this is, of course, a fine suggestion, consistent with good research practice and epistemological understanding.

However, blindly recommending the intervention based on the interpretation of numbers alone might be suspect—think of all the variables that could be involved in a 4-, 8-, 12-, or 52-week intervention with human participants. It would be foolish to believe that all variables were considered—so, here is a fantastic example of where a qualitative methodology might be useful. At the end of the intervention, a researcher might decide to interview a random 20 percent of the cohort who participated in the intervention group about their experience and the program’s strengths and weaknesses. The findings from this qualitative element might help further explain the effects, aid the initial interpretation, and bring to life new ideas and concepts that had been missing from the initial interpretation. In this respect, infusing a qualitative approach at the end of quantitative analysis has shown its benefits—a mixed approach to intervention evaluation is very useful.

What about before that? Well, let’s say I want to develop another intervention to enhance "z," but there’s little research on it, and that which has been conducted isn’t of the highest quality; furthermore, we don’t know about people’s experiences with "z" or even other variables associated with it.

To design an intervention around "z" would be ‘jumping the gun’ at best (and a waste of funds). It seems that an exploration of some sort is necessary. This is where qualitative again shines—giving us an opportunity to explore what "z" is from the perspective of a relevant cohort(s).

Of course, we cannot generalize the findings; we cannot draw a definitive conclusion as to what "z" is. But what the findings facilitate is providing a foundation from which to work; for example, we still cannot say that "z" is this, that, or the other, but it appears that it might be associated with "a," "b" and "c." Thus, future research should investigate the nature of "z" as a particular concept, in relation to "a," "b" and "c." Again, a qualitative methodology shows its worth. In the previous examples, a qualitative method was used because the research questions warranted it.

Through considering the potentially controversial statements about qual and quant above, we are pushed into examining the strengths and weaknesses of research methodologies (regardless of our exclusivity with a particular approach). This is useful if we’re going to think critically about finding answers to our research questions. But simply considering these does not let poor research practice off the hook.

For example, credible qualitative researchers acknowledge that generalizability is not the point of their research; however, that doesn’t stop some less-than-credible researchers from presenting their "findings" as generalizable as possible, without actually using the word. Such practices should be frowned upon—so should making a career out of strictly using qualitative methodology in an attempt to find answers core to the human condition. All these researchers are really doing is spending a career exploring, yet never really finding anything (despite arguing to the contrary, albeit avoiding the word "generalize").

The solution to this problem, again, is to truly listen to what your research question is telling you. Eventually, it’s going to recommend a quantitative approach. Likewise, a "numbers person" will be recommended a qualitative approach from time to time—flip around the example above, and there’s a similar criticism. Again, embrace a mixed approach.

What's the point of this argument?

I conduct both research methodologies. Which do I prefer? Simple—whichever one helps me most appropriately answer my research question.

Do I have problems with qualitative methodologies? Absolutely—but I have issues with quantitative methods as well. Having these issues is good—it means that you recognize the limitations of your tools, which increases the chances of you "fixing," "sharpening" or "changing out" your tools when necessary.

So, the next time someone speaks with you about labeling researchers as one type or another, ask them why they think that way, ask them which they think you are, and then reflect on the responses alongside your own views of methodology and epistemology. It might just help you become a better researcher.

Christopher Dwyer, Ph.D., is a lecturer at the Technological University of the Shannon in Athlone, Ireland.

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24 Most Reliable Used Cars You Can Buy Today

Posted: May 3, 2024 | Last updated: May 3, 2024

Buying a used car can be nerve-racking and confusing, even for the most experienced gearhead. If you’re parting with a serious amount of money, you’ll want to do some research first.

To help make your next car purchase easier, we’ve started the research to find out which are the most reliable vehicles on the used car market today. We’ve dug through JD Power’s vast database of automotive reliability ratings and picked some of the most highly rated models to ensure your next car won’t be a lemon.

No matter what car you’re buying, you should always check if it’s been in an accident, that previous owners stayed on top of its maintenance, and if there are any recalls. Here are 24 of the most reliable cars on the used car market today.

2011 Cadillac DTS — JD Power Reliability Score: 93/100

If you’re looking for space and comfort, you could do much worse than the Cadillac DTS. The entire family can travel comfortably, and the large trunk swallows plenty of cargo.

The V8 engine produces 275 horsepower unless you opt for the Platinum trim, which has 292 horses. It’s connected to a four-speed automatic, which isn’t very exciting, but it gets the job done. Of course, being an older car, you won’t find all the modern tech and gadgets here.

2020 Chevrolet Sonic — JD Power Reliability Score: 93/100

Chevrolet axed the Sonic from the US market after the 2020 model year, but the good news is that they had ironed out any issues by then.

Buying a four-year-old car means you’ll get most of the modern amenities for much less than you’d pay for a brand-new model. With its sub-140 horsepower, the Chevrolet Sonic isn’t the most exciting used car, but its excellent reliability score makes it a solid choice.

2021 Nissan Maxima — JD Power Reliability Score: 93/100

If you’re in the market for a mildly exciting sedan, the 2021 Nissan Maxima is worth considering. Again, being an almost-new car, it offers all the creature comforts and modern tech. It also boasts an excellent 93/100 reliability score on JD Power .

Interior-wise, the front seats offer excellent support and are comfortable for long periods of time, and thanks to a 300-horsepower V6 engine, the Maxima is more than capable of long trips. If there’s one drawback, the rear seats are a bit cramped for the average-sized adult.

2021 Chrysler 300 — JD Power Reliability Score: 92/100

The 2021 Chrysler 300 looks mean, packs plenty of power, and it’s very reliable. What more could we possibly ask for? Chrysler’s splendid Uconnect infotainment system is easy to use, too, which reduces the risk of getting distracted.

Under the hood, there’s a V8 engine that sends 363 horsepower to the rear wheels. Floor the throttle and hear it roar as it pushes into the seat.

2021 Genesis G90 — JD Power Reliability Score: 91/100

The Genesis G90 is another vehicle with plenty of power—between 365 and 420 horses, to be exact. Unfortunately, these horses are also very thirsty, so fuel economy suffers . Even the most “frugal” version only manages 24 mpg on the highway.

The good news is that the G90 offers excellent ride quality and driving dynamics. The interior has lots of room and is a comfortable place to spend time.

2013 Buick Verano — JD Power Reliability Score: 91/100

Buying an 11-year-old car can be risky, but as long as previous owners took proper care of it, the 2013 Buick Verano should be a safe bet. It’s a compact sedan, but it still manages to swallow a lot of stuff thanks to its 14.3 cu-ft of cargo space.

Compared to others on this list, the Verano may seem a little underpowered, but both four-cylinder engine options offer some excitement , thanks to 180 horsepower or 250 turbocharged horses. It should be noted that the car’s driving dynamics are tuned more for comfort than fun.

2008 Lincoln MKZ — JD Power Reliability Score: 91/100

Buying a 16-year-old car will never be 100% trouble-free, but the unassuming Lincoln MKZ does offer exceptional reliability. Make sure you check if the recalls have been dealt with and that it has a solid maintenance record.

If you want a solid, V6-powered, midsize luxury sedan but are on a strict budget, the 2008 Lincoln MKZ could be what you’re looking for. However, there’s absolutely nothing exciting about it, so you’ll want to look elsewhere if you want a car that’ll get your heart racing.

2020 Nissan Armada — JD Power Reliability Score: 91/100

If you want luxury and are not on a budget, Nissan’s flagship SUV is one of the most impressive vehicles in its class. It has a very luxurious and spacious cabin, and even the passengers in the rear seat rows won’t complain about being cramped.

With a 390-horsepower V8 engine, the Nissan Armada offers plenty of oomph for a large SUV as well. The downside is that its weight combined with a powerful engine means the fuel economy suffers.

2008 Acura RL — JD Power Reliability Score: 90/100

Back in 2008, the Acura RL was the biggest sedan offered by Honda’s luxury division. For the time, it was technologically advanced and regarded as a very safe vehicle, but automotive tech and safety have come a long way since then.

That being said, the Acura RL is still a reliable vehicle, and thanks to its V6 engine producing 290 horsepower, it also offers some excitement. Ensure it’s maintained properly and that all airbag-related recalls are handled.

2020 BMW X6 — JD Power Reliability Score: 90/100

BMW’s reputation for making solid cars has suffered in recent years. However, when correctly cared for, the Bavarian machines will provide years of service, as the 2020 BMW X6 proves.

This model has earned a great reliability score, and thanks to the generous standard equipment level and powerful engine options, it’s also a fun and exciting SUV.

2020 Kia Forte — JD Power Reliability Score: 90/100

If you need a powerful vehicle, we’ll inform you upfront that the Kia Forte will leave you disappointed. Only two engine options are available, producing 147 or 201 horsepower. If you opt for the more powerful version, you can choose between a six-speed manual or a seven-speed automatic.

The Kia Forte’s real forte is its solid reputation for reliability, along with its spacious interior. All things considered, it’s the perfect choice if you just want a no-frills daily driver that delivers no-nonsense dependability.

2021 Hyundai Accent — JD Power Reliability Score: 90/100

The Hyundai Accent has been around forever, and it’s never been an exciting car to own or drive. That hasn’t changed for the 2021-year model. However, no one would buy a Hyundai Accent if they wanted a sports car; they buy it because it offers years of trouble-free motoring.

Low fuel consumption, excellent reliability, and a spacious interior make this the perfect daily driver. To sweeten the deal even further, you can find it on the used market for as little as $12-15,000, which makes it a bargain.

2019 Nissan Titan — JD Power Reliability Score: 89/100

The 2019 Nissan Titan is the first vehicle on this list with a reliability score that dips into the eighties. However, it’s still much more reliable than the average vehicle. Americans often overlook the Titan in favor of domestic brands, but the Nissan has proven itself to be just as good in many ways.

Reliability-wise, the 2019 Titan scores better than most trucks in its segment, and thanks to its various trim levels, you can find them specced with plenty of tech and luxuries. As spacious and comfortable as the Titan may be, it does leave a bit to be desired compared to its American rivals.

2010 Audi A6 — JD Power Reliability Score: 89/100

2010 Audi A6

The 2010 Audi A6 offers European luxury for under $10,000, and it’s surprisingly reliable as well. You’ll get everything European sports sedans are known for: excellent handling, safety equipment, a premium interior, and plenty of power. The downside is that repairs and maintenance tend to be more expensive than Japanese and American cars.

With 255 to 265 horsepower, depending on the year, the Audi A6 is a very capable car, even more so if it has the Quattro AWD system.

2021 Nissan Murano — JD Power Reliability Score: 89/100

The Nissan Murano has soldiered on for quite some time, and by 2021, it was getting a bit long in the tooth. However, Nissan sorted that out by adding some safety features and interior updates. Just like that, the Murano had discovered the fountain of youth and remained among its segment’s top sellers.

As you’d expect from a vehicle that’s only three years old, it has lots of modern tech and gadgetry. If there’s anything to point a finger at, it’s the cargo capacity, which doesn’t live up to the standard set by the competition.

2020 Cadillac CT5 — JD Power Reliability Score: 89/100

The Cadillac CT5 first saw the light of day in 2019 as a 2020 model-year car. It was another hit for the American luxury brand, largely thanks to its great reliability score and the smooth ride quality Cadillac has become known for.

It offers plenty of features and creature comforts, but it’s not perfect. Compared to its European rivals, the interior does leave something to be desired, and the cargo capacity is sub-par.

2020 Chevrolet Malibu — JD Power Reliability Score: 89/100

The first Malibu arrived in 1964, and it’s still around today after Chevy brought the nameplate back in 1997 after a hiatus since the early 1980s. It’s a much-loved model thanks to its excellent reliability ratings, and buying a four-year-old car means you can enjoy years of trouble-free car ownership.

Like most reliable cars, it’s not the most exciting model money can buy, but depending on the trim, it’ll deliver between 163 and 250 all-American stallions. Inside, base models are, as expected, very basic, and higher trims offer more luxuries and gadgets.

2020 Kia Soul — JD Power Reliability Score: 89/100

What’s not to love about the 2020 Kia Soul? It’s one of the most reliable models on the used market, and it looks cute yet a little aggressive. Sure, it’s not fast, as it’s only available with two four-cylinder engines: one that produces 147 horses, and a turbocharged version with 201 horsepower.

Other than its fun exterior, the Kia Soul offers more comfort than most vehicles in its segment. It has plenty of tech and safety features, and thanks to its large glass areas, outward visibility is far better than that of the competition.

2021 Chevrolet Corvette — JD Power Reliability Score: 89/100

Who says sports cars aren’t reliable? The C8 Chevy Corvette is as reliable as any other American-made car, and thanks to a 490+ horsepower mid-mounted engine, it packs performance that’ll rival a supercar.

Inside, it doesn’t quite rival Europe’s finest, but everything is hyper-modern and as luxurious as one would expect from an American sports car in this price class.

2021 Dodge Charger — JD Power Reliability Score: 89/100

If you want American V8 performance but need something practical, the Dodge Charger is one of the best options. This is proper American muscle with more heritage than you can shake a stick at.

As an added bonus, it also looks mean and intimidating, so even if you don’t go for one of the powerful V8s, it’ll still draw some attention. Thanks to the Charger’s long lifespan, Dodge has had plenty of time to iron out any issues.

2021 BMW X4 — JD Power Reliability Score: 89/100

In 2021, BMW added more tech and safety features to the standard BMW X4. With its solid reputation for reliability, luxurious interior, and driving dynamics on par with what we expect from Bavaria’s finest, the X4 is a very attractive option.

Power-wise, the 2021 BMW X4 delivers between 248 and 503 thoroughbred horses, depending on spec. There are some drawbacks, however. It’s not as spacious as other models in its segments, and tall adults won’t be comfortable in the rear seat.

2015 Infiniti Q40 — JD Power Reliability Score: 89/100

You don’t come across an Infiniti Q40 very often, and that’s because it was only made in 2015. Infiniti renamed its G37 sedan Q40 for its final production year, so it’s possible to find earlier cars for even less money as long as you know about the name change.

It’s built on the same platform as the Nissan 370Z, which means it’s an exciting car to drive, even if it’s getting a bit old now. It has a 328-horsepower V6 engine, and all that power is sent to the rear wheels through a seven-speed automatic transmission.

2020 Toyota Tundra — JD Power Reliability Score: 88/100

Knowing that Toyota makes some of the world’s most reliable vehicles, we’re as surprised as you to discover that they’re not at the top of this list. Still, the Toyota Tundra has always offered legendary reliability, which also goes for the 2020 model year.

The V8 engine produces 381 horsepower, and it’s practically bulletproof. However, it should be noted that it’s a heavy drinker. The 2020 Tundra is also a bit dated, and it’s not as refined or comfortable as some of the competitors.

2012 Mercedes-Benz E-Class — JD Power Reliability Score: 88/100

We’ve already had some BMWs and Audis on here, and Mercedes just managed to squeeze onto the list as well. The E-Class is pretty much the definition of a midsize German luxury sedan, and even a 12-year-old model will draw some stares.

Its design has aged well, it offers some very decent and potent engines, and the interior is spacious and luxurious. Tech and safety feature-wise, it’s outdated compared to a brand-new model, but on the other hand, you’ll save thousands compared to buying a new car. Regarding reliability, there’s a reason why Mercedes-Benz is the preferred brand among European taxi drivers.

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PJSC Moscow city telephone network  ·  spd-mgts.ru

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AS25513 – PJSC Moscow city telephone network

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There are 7,157 domain names hosted across 2,230 IP addresses on this ASN.

There are 20 peers for this ASN.

There is 1 peer for this ASN.

There are 16 downstreams for this ASN.

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What is an asn.

Autonomous System Numbers (ASNs) are assigned to entities such as Internet Service Providers and other large organizations that control blocks of IP addresses. This network page, and the organization field that's shown on the main IP address information page and also returned in the geolocation API are based on the ASN.

The ASN details will often correspond to the IP address owner, but for smaller organizations it may be that organization's parent, or their ISP. Find out more about AS25513 at robtex .

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