conclusion validity research method

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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• Construct Validity | Definition, Types, & Examples

Construct Validity | Definition, Types, & Examples

Published on February 17, 2022 by Pritha Bhandari . Revised on June 22, 2023.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s crucial to establishing the overall validity of a method.

Assessing construct validity is especially important when you’re researching something that can’t be measured or observed directly, such as intelligence, self-confidence, or happiness. You need multiple observable or measurable indicators to measure those constructs or run the risk of introducing research bias into your work.

• Content validity : Is the test fully representative of what it aims to measure?
• Face validity : Does the content of the test appear to be suitable to its aims?
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Table of contents

What is a construct, what is construct validity, types of construct validity, how do you measure construct validity, threats to construct validity, other interesting articles, frequently asked questions about construct validity.

A construct is a theoretical concept, theme, or idea based on empirical observations. It’s a variable that’s usually not directly measurable.

Some common constructs include:

• Self-esteem
• Logical reasoning
• Academic motivation
• Social anxiety

Constructs can range from simple to complex. For example, a concept like hand preference is easily assessed:

• A simple survey question : Ask participants which hand is their dominant hand.
• Observations : Ask participants to perform simple tasks, such as picking up an object or drawing a cat, and observe which hand they use to execute the tasks.

A more complex concept, like social anxiety, requires more nuanced measurements, such as psychometric questionnaires and clinical interviews.

Simple constructs tend to be narrowly defined, while complex constructs are broader and made up of dimensions. Dimensions are different parts of a construct that are coherently linked to make it up as a whole.

As a construct, social anxiety is made up of several dimensions.

• Psychological dimension: Intense fear and anxiety
• Physiological dimension: Physical stress indicators
• Behavioral dimension: Avoidance of social settings

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Construct validity concerns the extent to which your test or measure accurately assesses what it’s supposed to.

In research, it’s important to operationalize constructs into concrete and measurable characteristics based on your idea of the construct and its dimensions.

Be clear on how you define your construct and how the dimensions relate to each other before you collect or analyze data . This helps you ensure that any measurement method you use accurately assesses the specific construct you’re investigating as a whole and helps avoid biases and mistakes like omitted variable bias or information bias .

• How often do you avoid entering a room when everyone else is already seated?
• Do other people tend to describe you as quiet?
• When talking to new acquaintances, how often do you worry about saying something foolish?
• To what extent do you fear giving a talk in front of an audience?
• How often do you avoid making eye contact with other people?
• Do you prefer to have a small number of close friends over a big group of friends?

When designing or evaluating a measure, it’s important to consider whether it really targets the construct of interest or whether it assesses separate but related constructs.

It’s crucial to differentiate your construct from related constructs and make sure that every part of your measurement technique is solely focused on your specific construct.

• Does your questionnaire solely measure social anxiety?
• Are all aspects of social anxiety covered by the questions?
• Do your questions avoid measuring other relevant constructs like shyness or introversion?

There are two main types of construct validity.

• Convergent validity: The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Convergent validity

Convergent validity is the extent to which measures of the same or similar constructs actually correspond to each other.

In research studies, you expect measures of related constructs to correlate with one another. If you have two related scales, people who score highly on one scale tend to score highly on the other as well.

Discriminant validity

Conversely, discriminant validity means that two measures of unrelated constructs that should be unrelated, very weakly related, or negatively related actually are in practice.

You check for discriminant validity the same way as convergent validity: by comparing results for different measures and assessing whether or how they correlate.

How do you select unrelated constructs? It’s good to pick constructs that are theoretically distinct or opposing concepts within the same category.

For example, if your construct of interest is a personality trait (e.g., introversion), it’s appropriate to pick a completely opposing personality trait (e.g., extroversion). You can expect results for your introversion test to be negatively correlated with results for a measure of extroversion.

Alternatively, you can pick non-opposing unrelated concepts and check there are no correlations (or weak correlations) between measures.

You often focus on assessing construct validity after developing a new measure. It’s best to test out a new measure with a pilot study, but there are other options.

• A pilot study is a trial run of your study. You test out your measure with a small sample to check its feasibility, reliability , and validity . This helps you figure out whether you need to tweak or revise your measure to make sure you’re accurately testing your construct.
• Statistical analyses are often applied to test validity with data from your measures. You test convergent and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.
• You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity.

It’s important to recognize and counter threats to construct validity for a robust research design. The most common threats are:

Poor operationalization

Experimenter expectancies, subject bias.

A big threat to construct validity is poor operationalization of the construct.

A good operational definition of a construct helps you measure it accurately and precisely every time. Your measurement protocol is clear and specific, and it can be used under different conditions by other people.

Without a good operational definition, you may have random or systematic error , which compromises your results and can lead to information bias . Your measure may not be able to accurately assess your construct.

Experimenter expectancies about a study can bias your results. It’s best to be aware of this research bias and take steps to avoid it.

To combat this threat, use researcher triangulation and involve people who don’t know the hypothesis in taking measurements in your study. Since they don’t have strong expectations, they are unlikely to bias the results.

When participants hold expectations about the study, their behaviors and responses are sometimes influenced by their own biases. This can threaten your construct validity because you may not be able to accurately measure what you’re interested in.

You can mitigate subject bias by using masking (blinding) to hide the true purpose of the study from participants. By giving them a cover story for your study, you can lower the effect of subject bias on your results, as well as prevent them guessing the point of your research, which can lead to demand characteristics , social desirability bias , and a Hawthorne effect .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

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5.3 Experimentation and Validity

Learning objectives.

• Explain what internal validity is and why experiments are considered to be high in internal validity.
• Explain what external validity is and evaluate studies in terms of their external validity.
• Explain the concepts of construct and statistical validity.

Four Big Validities

When we read about psychology experiments with a critical view, one question to ask is “is this study valid?” However, that question is not as straightforward as it seems because, in psychology, there are many different kinds of validities. Researchers have focused on four validities to help assess whether an experiment is sound (Judd & Kenny, 1981; Morling, 2014) [1] [2] : internal validity, external validity, construct validity, and statistical validity. We will explore each validity in depth.

Internal Validity

Two variables being statistically related does not necessarily mean that one causes the other. “Correlation does not imply causation.” For example, if it were the case that people who exercise regularly are happier than people who do not exercise regularly, this implication would not necessarily mean that exercising increases people’s happiness. It could mean instead that greater happiness causes people to exercise or that something like better physical health causes people to exercise   and  be happier.

The purpose of an experiment, however, is to show that two variables are statistically related and to do so in a way that supports the conclusion that the independent variable caused any observed differences in the dependent variable. The logic is based on this assumption: If the researcher creates two or more highly similar conditions and then manipulates the independent variable to produce just  one  difference between them, then any later difference between the conditions must have been caused by the independent variable. For example, because the only difference between Darley and Latané’s conditions was the number of students that participants believed to be involved in the discussion, this difference in belief must have been responsible for differences in helping between the conditions.

An empirical study is said to be high in  internal validity  if the way it was conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Thus experiments are high in internal validity because the way they are conducted—with the manipulation of the independent variable and the control of extraneous variables—provides strong support for causal conclusions. In contrast, nonexperimental research designs (e.g., correlational designs), in which variables are measured but are not manipulated by an experimenter, are low in internal validity.

External Validity

At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism. Specifically, the need to manipulate the independent variable and control extraneous variables means that experiments are often conducted under conditions that seem artificial (Bauman, McGraw, Bartels, & Warren, 2014) [3] . In many psychology experiments, the participants are all undergraduate students and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. Consider, for example, an experiment in which researcher Barbara Fredrickson and her colleagues had undergraduate students come to a laboratory on campus and complete a math test while wearing a swimsuit (Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998) [4] . At first, this manipulation might seem silly. When will undergraduate students ever have to complete math tests in their swimsuits outside of this experiment?

The issue we are confronting is that of external validity . An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied. As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to and participants encounter every day, often described as mundane realism . Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes. Their study would be high in external validity and have high mundane realism if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store. If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this increase would be true for other shoppers in other stores. Their study would be relatively low in external validity, however, if they studied a sample of undergraduate students in a laboratory at a selective university who merely judged the appeal of various colors presented on a computer screen; however, this study would have high psychological realism where the same mental process is used in both the laboratory and in the real world.  If the students judged purple to be more appealing than yellow, the researchers would not be very confident that this preference is relevant to grocery shoppers’ cereal-buying decisions because of low external validity but they could be confident that the visual processing of colors has high psychological realism.

We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Consider that Darley and Latané’s experiment provided a reasonably good simulation of a real emergency situation. Or consider field experiments  that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy (Cialdini, 2005) [5] . These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels. The result was that guests who received the message that most hotel guests choose to reuse their towels, reused their own towels substantially more often than guests receiving either of the other two messages. Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels.

A second reason not to draw the blanket conclusion that experiments are low in external validity is that they are often conducted to learn about psychological processes  that are likely to operate in a variety of people and situations. Let us return to the experiment by Fredrickson and colleagues. They found that the women in their study, but not the men, performed worse on the math test when they were wearing swimsuits. They argued that this gender difference was due to women’s greater tendency to objectify themselves—to think about themselves from the perspective of an outside observer—which diverts their attention away from other tasks. They argued, furthermore, that this process of self-objectification and its effect on attention is likely to operate in a variety of women and situations—even if none of them ever finds herself taking a math test in her swimsuit.

Construct Validity

In addition to the generalizability of the results of an experiment, another element to scrutinize in a study is the quality of the experiment’s manipulations or the construct validity . The research question that Darley and Latané started with is “does helping behavior become diffused?” They hypothesized that participants in a lab would be less likely to help when they believed there were more potential helpers besides themselves. This conversion from research question to experiment design is called operationalization (see Chapter 4 for more information about the operational definition). Darley and Latané operationalized the independent variable of diffusion of responsibility by increasing the number of potential helpers. In evaluating this design, we would say that the construct validity was very high because the experiment’s manipulations very clearly speak to the research question; there was a crisis, a way for the participant to help, and increasing the number of other students involved in the discussion, they provided a way to test diffusion.

What if the number of conditions in Darley and Latané’s study changed? Consider if there were only two conditions: one student involved in the discussion or two. Even though we may see a decrease in helping by adding another person, it may not be a clear demonstration of diffusion of responsibility, just merely the presence of others. We might think it was a form of Bandura’s social inhibition. The construct validity would be lower. However, had there been five conditions, perhaps we would see the decrease continue with more people in the discussion or perhaps it would plateau after a certain number of people. In that situation, we may not necessarily be learning more about diffusion of responsibility or it may become a different phenomenon. By adding more conditions, the construct validity may not get higher. When designing your own experiment, consider how well the research question is operationalized your study.

Statistical Validity

Statistical validity concerns the proper statistical treatment of data and the soundness of the researchers’ statistical conclusions. There are many different types of inferential statistics tests (e.g.,  t- tests, ANOVA, regression, correlation) and statistical validity concerns the use of the proper type of test to analyze the data. When considering the proper type of test, researchers must consider the scale of measure their dependent variable was measured on and the design of their study. Further, many of inferential statistics tests carry certain assumptions (e.g., the data are normally distributed) and statistical validity is threatened when these assumptions are not met but the statistics are used nonetheless.

One common critique of experiments is that a study did not have enough participants. The main reason for this criticism is that it is difficult to generalize about a population from a small sample. At the outset, it seems as though this critique is about external validity but there are studies where small sample sizes are not a problem (subsequent chapters will discuss how small samples, even of only 1 person, are still very illuminating for psychology research). Therefore, small sample sizes are actually a critique of statistical validity . The statistical validity speaks to whether the statistics conducted in the study are sound and support the conclusions that are made.

The proper statistical analysis should be conducted on the data to determine whether the difference or relationship that was predicted was found. The number of conditions and the total number of participants will determine the overall size of the effect. With this information, a power analysis can be conducted to ascertain whether you are likely to find a real difference. When designing a study, it is best to think about the power analysis so that the appropriate number of participants can be recruited and tested. To design a statistically valid experiment, thinking about the statistical tests at the beginning of the design will help ensure the results can be believed.

Prioritizing Validities

These four big validities–internal, external, construct, and statistical–are useful to keep in mind when both reading about other experiments and designing your own. However, researchers must prioritize and often it is not possible to have high validity in all four areas. In Cialdini’s study on towel usage in hotels, the external validity was high but the statistical validity was more modest. This discrepancy does not invalidate the study but it shows where there may be room for improvement for future follow-up studies (Goldstein, Cialdini, & Griskevicius, 2008) [6] . Morling (2014) points out that most psychology studies have high internal and construct validity but sometimes sacrifice external validity.

Key Takeaways

• Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.
• Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.
• Judd, C.M. & Kenny, D.A. (1981). Estimating the effects of social interventions . Cambridge, MA: Cambridge University Press. ↵
• Morling, B. (2014, April). Teach your students to be better consumers. APS Observer . Retrieved from http://www.psychologicalscience.org/index.php/publications/observer/2014/april-14/teach-your-students-to-be-better-consumers.html ↵
• Bauman, C.W., McGraw, A.P., Bartels, D.M., & Warren, C. (2014). Revisiting external validity: Concerns about trolley problems and other sacrificial dilemmas in moral psychology. Social and Personality Psychology Compass, 8/9 , 536-554. ↵
• Fredrickson, B. L., Roberts, T.-A., Noll, S. M., Quinn, D. M., & Twenge, J. M. (1998). The swimsuit becomes you: Sex differences in self-objectification, restrained eating, and math performance. Journal of Personality and Social Psychology, 75 , 269–284. ↵
• Cialdini, R. (2005, April). Don’t throw in the towel: Use social influence research. APS Observer . Retrieved from http://www.psychologicalscience.org/index.php/publications/observer/2005/april-05/dont-throw-in-the-towel-use-social-influence-research.html ↵
• Goldstein, N. J., Cialdini, R. B., & Griskevicius, V. (2008). A room with a viewpoint: Using social norms to motivate environmental conservation in hotels. Journal of Consumer Research, 35 , 472–482. ↵

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The Significance of Validity and Reliability in Quantitative Research

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Key Takeaways:

• Types of validity to consider during quantitative research include internal, external, construct, and statistical
• Types of reliability that apply to quantitative research include test re-test, inter-rater, internal consistency, and parallel forms
• There are numerous challenges to achieving validity and reliability in quantitative research, but the right techniques can help overcome them

Quantitative research is used to investigate and analyze data to draw meaningful conclusions. Validity and reliability are two critical concepts in quantitative analysis that ensure the accuracy and consistency of the research results. Validity refers to the extent to which the research measures what it intends to measure, while reliability refers to the consistency and reproducibility of the research results over time. Ensuring validity and reliability is crucial in conducting high-quality research, as it increases confidence in the findings and conclusions drawn from the data.

This article aims to provide an in-depth analysis of the significance of validity and reliability in quantitative research. It will explore the different types of validity and reliability, their interrelationships, and the associated challenges and limitations.

In this Article:

The role of validity in quantitative research, the role of reliability in quantitative research, validity and reliability: how they differ and interrelate, challenges and limitations of ensuring validity and reliability, overcoming challenges and limitations to achieve validity and reliability, explore trusted quantitative solutions.

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Validity is crucial in maintaining the credibility and reliability of quantitative research outcomes. Therefore, it is critical to establish that the variables being measured in a study align with the research objectives and accurately reflect the phenomenon being investigated.

Several types of validity apply to various study designs; let’s take a deeper look at each one below:

Internal validity is concerned with the extent to which a study establishes a causal relationship between the independent and dependent variables. In other words, internal validity determines whether the changes observed in the conditional variable result from changes in the independent variable or some other factor.

External validity refers to the degree to which the findings of a study can be generalized to other populations and contexts. External validity helps ensure the results of a study are not limited to the specific people or context in which the study was conducted.

Construct validity refers to the degree to which a research study accurately measures the theoretical construct it intends to measure. Construct validity helps provide alignment between the study’s measures and the theoretical concept it aims to investigate.

Finally, statistical validity refers to the accuracy of the statistical tests used to analyze the data. Establishing statistical validity provides confidence that the conclusions drawn from the data are reliable and accurate.

To safeguard the validity of a study, researchers must carefully design their research methodology, select appropriate measures, and control for extraneous variables that may impact the results. Validity is especially crucial in fields such as medicine, where inaccurate research findings can have severe consequences for patients and healthcare practices.

Ensuring the consistency and reproducibility of research outcomes over time is crucial in quantitative research, and this is where the concept of reliability comes into play. Reliability is vital to building trust in the research findings and their ability to be replicated in diverse contexts.

Similar to validity, multiple types of reliability are pertinent to different research designs. Let’s take a closer look at each of these types of reliability below:

Test-retest reliability refers to the consistency of the results obtained when the same test is administered to the same group of participants at different times. This type of reliability is essential when researchers need to administer the same test multiple times to assess changes in behavior or attitudes over time.

Inter-rater reliability refers to the results’ consistency when different raters or observers monitor the same behavior or phenomenon. This type of reliability is vital when researchers are required to rely on different individuals to rate or observe the same behavior or phenomenon.

Internal consistency reliability refers to the degree to which the items or questions in a test or questionnaire measure the same construct. This type of reliability is important in studies where researchers use multiple items or questions to assess a particular construct, such as knowledge or quality of life.

Lastly, parallel forms reliability refers to the consistency of the results obtained when two different versions of the same test are administered to the same group of participants. This type of reliability is important when researchers administer different versions of the same test to assess the consistency of the results.

Reliability in research is like the accuracy and consistency of a medical test. Just as a reliable medical test produces consistent and accurate results that physicians can trust to make informed decisions about patient care, a highly reliable study produces consistent and precise findings that researchers can trust to make knowledgeable conclusions about a particular phenomenon. To ensure reliability in a study, researchers must carefully select appropriate measures and establish protocols for administering the measures consistently. They must also take steps to control for extraneous variables that may impact the results.

Validity and reliability are two critical concepts in quantitative research that significantly determine the quality of research studies. While both terms are often used interchangeably, they refer to different aspects of research. Validity is the extent to which a research study measures what it claims to measure without being affected by extraneous factors or bias. In contrast, reliability is the degree to which the research results are consistent and stable over time and across different samples , methods, and evaluators.

Designing a research study that is both valid and reliable is essential for producing high-quality and trustworthy research findings. Finding this balance requires significant expertise, skill, and attention to detail. Ultimately, the goal is to produce research findings that are valid and reliable but also impactful and influential for the organization requesting them. Achieving this level of excellence requires a deep understanding of the nuances and complexities of research methodology and a commitment to excellence and rigor in all aspects of the research process.

Ensuring validity and reliability in quantitative research is not without its challenges. Some of the factors to consider include:

1. Measuring Complex Constructs or Variables One of the main challenges is the difficulty in accurately measuring complex constructs or variables. For instance, measuring constructs such as intelligence or personality can be complicated due to their multi-dimensional nature, and it can be challenging to capture all aspects accurately.

2. Limitations of Data Collection Instruments In addition, the measures or instruments used to collect data can also be limited in their sensitivity or specificity. This can impact the study’s validity and reliability, as accurate and precise measures can lead to incorrect conclusions and unreliable results. For example, a scale that measures depression but does not include all relevant symptoms may not accurately capture the construct being studied.

3. Sources of Error and Bias in Data Collection The data collection process itself can introduce sources of error or bias, which can impact the validity and reliability of the study. For instance, measurement errors can occur due to the limitations of the measuring instrument or human error during data collection. In addition, response bias can arise when participants provide socially desirable answers, while sampling bias can occur when the sample is not representative of the studied population.

4. The Complexity of Achieving Meaningful and Accurate Research Findings There are also some limitations to validity and reliability in research studies. For example, achieving internal validity by controlling for extraneous variables may only sometimes ensure external validity or the ability to generalize findings to other populations or settings. This can be a limitation for researchers who wish to apply their findings to a larger population or different contexts.

Additionally, while reliability is essential for producing consistent and reproducible results, it does not guarantee the accuracy or truth of the findings. This means that even if a study has reliable results, it may still need to be revised in terms of accuracy. These limitations remind us that research is a complex process, and achieving validity and reliability is just one part of the giant puzzle of producing accurate and meaningful research.

Researchers can adopt various measures and techniques to overcome the challenges and limitations in ensuring validity and reliability in research studies.

One such approach is to use multiple measures or instruments to assess the same construct. In addition, various steps can help identify commonalities and differences across measures, thereby providing a more comprehensive understanding of the construct being studied.

Inter-rater reliability checks can also be conducted to ensure different raters or observers consistently interpret and rate the same data. This can reduce measurement errors and improve the reliability of the results. Additionally, data-cleaning techniques can be used to identify and remove any outliers or errors in the data.

Finally, researchers can use appropriate statistical methods to assess the validity and reliability of their measures. For example, factor analysis identifies the underlying factors contributing to the construct being studied, while test-retest reliability helps evaluate the consistency of results over time. By adopting these measures and techniques, researchers can crease t their findings’ overall quality and usefulness.

The backbone of any quantitative research lies in the validity and reliability of the data collected. These factors ensure the data accurately reflects the intended research objectives and is consistent and reproducible. By carefully balancing the interrelationship between validity and reliability and using appropriate techniques to overcome challenges, researchers protect the credibility and impact of their work. This is essential in producing high-quality research that can withstand scrutiny and drive progress.

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A threat to conclusion validity is a factor that can lead you to reach an incorrect conclusion about a relationship in your observations. You can essentially make two kinds of errors about relationships:

• Conclude that there is no relationship when in fact there is (you missed the relationship or didn’t see it)
• Conclude that there is a relationship when in fact there is not (you’re seeing things that aren’t there!)

Most threats to conclusion validity have to do with the first problem. Why? Maybe it’s because it’s so hard in most research to find relationships in our data at all that it’s not as big or frequent a problem — we tend to have more problems finding the needle in the haystack than seeing things that aren’t there! So, I’ll divide the threats by the type of error they are associated with.

Finding no relationship when there is one (or, “missing the needle in the haystack”)

When you’re looking for the needle in the haystack you essentially have two basic problems: the tiny needle and too much hay. You can view this as a signal-to-noise ratio problem.The “signal” is the needle — the relationship you are trying to see. The “noise” consists of all of the factors that make it hard to see the relationship. There are several important sources of noise, each of which is a threat to conclusion validity. One important threat is low reliability of measures (see reliability ). This can be due to many factors including poor question wording, bad instrument design or layout, illegibility of field notes, and so on. In studies where you are evaluating a program you can introduce noise through poor reliability of treatment implementation . If the program doesn’t follow the prescribed procedures or is inconsistently carried out, it will be harder to see relationships between the program and other factors like the outcomes. Noise that is caused by random irrelevancies in the setting can also obscure your ability to see a relationship. In a classroom context, the traffic outside the room, disturbances in the hallway, and countless other irrelevant events can distract the researcher or the participants. The types of people you have in your study can also make it harder to see relationships. The threat here is due to random heterogeneity of respondents . If you have a very diverse group of respondents, they are likely to vary more widely on your measures or observations. Some of their variety may be related to the phenomenon you are looking at, but at least part of it is likely to just constitute individual differences that are irrelevant to the relationship being observed.

All of these threats add variability into the research context and contribute to the “noise” relative to the signal of the relationship you are looking for. But noise is only one part of the problem. We also have to consider the issue of the signal — the true strength of the relationship. There is one broad threat to conclusion validity that tends to subsume or encompass all of the noise-producing factors above and also takes into account the strength of the signal, the amount of information you collect, and the amount of risk you’re willing to take in making a decision about a whether a relationship exists. This threat is called low statistical power . Because this idea is so important in understanding how we make decisions about relationships, we have a separate discussion of statistical power .

Finding a relationship when there is not one (or “seeing things that aren’t there”)

In anything but the most trivial research study, the researcher will spend a considerable amount of time analyzing the data for relationships. Of course, it’s important to conduct a thorough analysis, but most people are well aware of the fact that if you play with the data long enough, you can often “turn up” results that support or corroborate your hypotheses. In more everyday terms, you are “fishing” for a specific result by analyzing the data repeatedly under slightly differing conditions or assumptions.

In statistical analysis, we attempt to determine the probability that the finding we get is a “real” one or could have been a “chance” finding. In fact, we often use this probability to decide whether to accept the statistical result as evidence that there is a relationship. In the social sciences, researchers often use the rather arbitrary value known as the 0.05 level of significance to decide whether their result is credible or could be considered a “fluke.” Essentially, the value 0.05 means that the result you got could be expected to occur by chance at least 5 times out of every 100 times you run the statistical analysis. The probability assumption that underlies most statistical analyses assumes that each analysis is “independent” of the other. But that may not be true when you conduct multiple analyses of the same data. For instance, let’s say you conduct 20 statistical tests and for each one you use the 0.05 level criterion for deciding whether you are observing a relationship. For each test, the odds are 5 out of 100 that you will see a relationship even if there is not one there (that’s what it means to say that the result could be “due to chance”). Odds of 5 out of 100 are equal to the fraction 5/100 which is also equal to 1 out of 20. Now, in this example, you conduct 20 separate analyses. Let’s say that you find that of the twenty results, only one is statistically significant at the 0.05 level. Does that mean you have found a statistically significant relationship? If you had only done the one analysis, you might conclude that you’ve found a relationship in that result. But if you did 20 analyses, you would expect to find one of them significant by chance alone, even if there is no real relationship in the data. We call this threat to conclusion validity fishing and the error rate problem . The basic problem is that you were “fishing” by conducting multiple analyses and treating each one as though it was independent. Instead, when you conduct multiple analyses, you should adjust the error rate (i.e. significance level) to reflect the number of analyses you are doing. The bottom line here is that you are more likely to see a relationship when there isn’t one when you keep reanalyzing your data and don’t take that fishing into account when drawing your conclusions.

Problems that can lead to either conclusion error

Every analysis is based on a variety of assumptions about the nature of the data, the procedures you use to conduct the analysis, and the match between these two. If you are not sensitive to the assumptions behind your analysis you are likely to draw erroneous conclusions about relationships. In quantitative research we refer to this threat as the violated assumptions of statistical tests . For instance, many statistical analyses assume that the data are distributed normally — that the population from which they are drawn would be distributed according to a “normal” or “bell-shaped” curve. If that assumption is not true for your data and you use that statistical test, you are likely to get an incorrect estimate of the true relationship. And, it’s not always possible to predict what type of error you might make — seeing a relationship that isn’t there or missing one that is.

I believe that the same problem can occur in qualitative research as well. There are assumptions, some of which we may not even realize, behind our qualitative methods. For instance, in interview situations we may assume that the respondent is free to say anything s/he wishes. If that is not true — if the respondent is under covert pressure from supervisors to respond in a certain way — you may erroneously see relationships in the responses that aren’t real and/or miss ones that are.

The threats listed above illustrate some of the major difficulties and traps that are involved in one of the most basic of research tasks — deciding whether there is a relationship in your data or observations. So, how do we attempt to deal with these threats? The researcher has a number of strategies for improving conclusion validity through minimizing or eliminating the threats described above.

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• Open access
• Published: 09 August 2024

Exploring Agrobacterium -mediated genetic transformation methods and its applications in Lilium

• Xinyue Fan 1 &
• Hongmei Sun 1 , 2  

Plant Methods volume  20 , Article number:  120 ( 2024 ) Cite this article

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As a typical bulb flower, lily is widely cultivated worldwide because of its high ornamental, medicinal and edible value. Although breeding efforts evolved over the last 10000 years, there are still many problems in the face of increasing consumer demand. The approach of biotechnological methods would help to solve this problem and incorporate traits impossible by conventional breeding. Target traits are dormancy, development, color, floral fragrance and resistances against various biotic and abiotic stresses, so as to improve the quality of bulbs and cut flowers in planting, cultivation, postharvest, plant protection and marketing. Genetic transformation technology is an important method for varietal improvement and has become the foundation and core of plant functional genomics research, greatly assisting various plant improvement programs. However, achieving stable and efficient genetic transformation of lily has been difficult worldwide. Many gene function verification studies depend on the use of model plants, which greatly limits the pace of directed breeding and germplasm improvement in lily. Although significant progress has been made in the development and optimization of genetic transformation systems, shortcomings remain. Agrobacterium -mediated genetic transformation has been widely used in lily. However, severe genotypic dependence is the main bottleneck limiting the genetic transformation of lily. This review will summarizes the research progress in the genetic transformation of lily over the past 30 years to generate the material including a section how genome engineering using stable genetic transformation system, and give an overview about recent and future applications of lily transformation. The information provided in this paper includes ideas for optimizing and improving the efficiency of existing genetic transformation methods and for innovation, provides technical support for mining and identifying regulatory genes for key traits, and lays a foundation for genetic improvement and innovative germplasm development in lily.

Flowers are not only a globally important agricultural industry with great economic benefits but also necessary agents for mental health in people's daily lives. Many countries have given intensive attention to the development of the flower industry. At present, the world's flower cultivation area is 22.3 hm 2 , and the international trade volume of flowers is expanding (AIPH, https://www.floraldaily.com ). Lily is a typical perennial herbaceous bulb plant with more than 100 wild species and more than 9,000 varieties worldwide; additionally, this plant has high ornamental value, and the share of lily as a cut flower in the global flower market is increasing annually [ 1 ]. Some lily varieties are edible and have high medicinal value, and their extracts are rich in antioxidant and anti-inflammatory components, resulting in widespread use in medicine, functional food and cosmetics [ 2 ]. It is therefore unsurprising that lilies are the focus of much bulbous flower research. Improving the quality of seed balls and cut flowers has always been a key goal in the global lily industry [ 3 , 4 ]. The target traits are dormancy, development, colour, floral fragrance and resistance to various biological and abiotic stresses to improve the quality of bulbs and cut flowers in planting, cultivation, postharvest, plant protection and marketing. Crossbreeding can quickly fuse good traits and easily produce heterosis. However, lily has a complex genetic background, high heterozygosity, and it is extremely difficult to carry out genome processing, because it is one of the plants with the largest genome, with nearly 30 Gb of genetic information. The long cycle of cross-breeding requires a lot of manpower and material resources. In many cases, sexual incompatibility is also an obstacle in the crossbreeding of lily. Likewise, physical and chemical mutagenesis methods are also highly uncertain. With the rapid development of molecular biology technology, genetic engineering has received increasing amounts of attention. Using molecular methods to improve germplasms can not only lead to the creation of new traits but also increase the efficiency and accuracy of breeding [ 5 , 6 , 7 ] (Fig.  1 ).

Strategies for developing new varieties of lily using different plant breeding tools. A Physical mutagenesis (in which mutants are created by exposing seeds or bulblets to radiation). B Chemical mutagenesis (treatment of different explants with chemical agents to obtain mutants, such as EMS mutagenesis). C Traditional crossbreeding has led to the cultivation of new lily varieties. D Transgenic breeding

Genetic transformation is an important part of genetic engineering technology, and the main goals of flower genetic transformation are as follows: (1) genetic transformation for basic research on a single gene, gene family or gene regulatory network and (2) the application of basic research results as the theoretical basis for improving flower traits and creating new varieties. Plant genetic transformation includes target gene selection, delivery, integration into plant cells, and expression and, ultimately, the production of a complete plant after numerous processes [ 8 ]. Although genetically modified plants were obtained in 1983, the genetic transformation of bulb flowers such as lily has long been considered difficult or impossible [ 9 ]. With progress in the field of lily research, an increasing number of genes, including genes related to major factors involved in regulating various life activities, responding to various biological and abiotic stresses, and responding to various environmental signals, have been identified. However, many gene function studies still depend on the heterologous transformation of model plants such as Arabidopsis and Nicotiana benthamiana (Fig. S1) . In 1992, Cohen conducted the first genetic transformation experiment in lily through Agrobacterium -mediated transformation and detected foreign genes in the calli [ 10 ]. However, the low efficiency, difficulty of regeneration, and difficulty of integration into the lily genome remain obstacles to overcome. With increasing basic research on lily plants, instantaneous transformation based on virus induction has gradually become the first choice of many researchers because this method is simple and fast, and the research cycle is only a few hours or days. However, because these methods cannot enable integration into the genome and are sometimes limited to a single tissue, it is difficult to provide sufficient evidence for gene function, and the research results are uninformative and unfavourable for further application in breeding work. Agrobacterium -mediated transformation, particle bombardment, PEG and electric shock are common methods of plant genetic transformation at present (Fig.  2 ). Compared with other plant transgenic methods, Agrobacterium -mediated plant genetic transformation remains the most common and widespread lily transgenic strategy because of its advantages of high transformation efficiency, few transgenic copies, and stable transfer of integrated genes into offspring after continuous optimization and updating [ 11 ].

Common methods for the genetic transformation of lily. A Agrobacterium -mediated stable genetic transformation B Virus-induced transient gene silencing (VIGS). C Particle bombardment. D Pollen magnetic effect method

In the past 30 years, many researchers have attempted to improve and create technology by adjusting or changing various parameters and operating methods and have accumulated considerable valuable experience (Fig.  3 ). Agrobacterium is a gram-negative bacterial genus that is widely distributed in soil. At the beginning of the twentieth century, the principle that natural pathogens can infect plants through wounds was gradually elucidated. The main mechanism is the delivery of tumorigenic DNA molecules (transfer DNA or T-DNA) into plant cells through wounds in infected plants; these molecules are eventually integrated into the host genome and stably transmitted to the next generation of the plant through meiosis [ 12 , 13 ]. The ability of Agrobacterium to integrate its own DNA into the host genome is primarily determined by the Ti plasmid [ 14 ], which can be modified by the insertion of target genes into the T-DNA region. With the help of the transferability of this region, genes can be introduced into plants by Agrobacterium infection and incorporated into plant genome, after which transgenic plants can be generated by cell and tissue culture technology [ 13 ]. Like most plant genetic transformation methods mediated by Agrobacterium, the genetic transformation procedures for lily mainly include vector construction, selection and culture of explants, preculture, Agrobacterium infection, coculture, resistance selection and transgenic plant regeneration (Fig.  2 ). At present, in addition to calli induced by roots, petals and leaves, scales and embryonic calli are common explants used for genetic transformation in lily [ 15 , 16 , 17 ]. With the continuous updating and optimization of genetic transformation technology for lily, functional verification of several genes by heterologous transformation of model plants has gradually increased, and many genes that regulate desirable traits in lily have been identified [ 3 , 17 , 18 , 19 , 20 ]. Nevertheless, stable and efficient transformation of target genes has been achieved in only a few lily varieties, and the success rate of genetic transformation of some lily varieties is still low.

Timeline of several major discoveries, applications and breakthroughs in the history of lily genetic transformation

In this paper, the development of genetic transformation technology for lily plants over the past 30 years is reviewed, the factors and key technical points restricting the efficiency of genetic transformation in lily are described, the problems and limitations associated with the genetic transformation of lily are summarized, and the prospects for application and improvement are discussed. The purpose of this paper is to provide a technical reference for establishing a stable and efficient genetic transformation system for lily and to lay a foundation for directional breeding and genetic improvement of key characteristics.

Factors affecting the genetic transformation of lily

Many factors affect lily regeneration and transformation. Genotyping is one of the key problems affecting the success of transformation [ 16 ]. Although genetic transformation systems have been established for different lily varieties, major differences exist between different genotypes [ 21 ]. Under the same conditions during the genetic transformation process, the genotype determines the difficulty of using Agrobacterium to successfully infect lily. At present, stable and efficient genetic transformation has been successfully achieved for few lily varieties (Table  1 ). Since the use of Agrobacterium -mediated genetic transformation of lily has been reported, several studies have aimed to optimize the transformation system or establish methods suitable for different lily species, including Lilium formolongi [ 22 , 23 ], Lilium longiflorum [ 24 , 25 , 26 ], Lilium pumilum DC.Fisch. [ 26 ] and the Oriental hybrid Lily [ 15 , 16 , 27 , 28 ]. Due to the strong genotypic dependence and difficult regeneration of explant materials after transformation, most related research results are restricted to certain genotypes [ 16 , 25 , 27 , 28 ]. Yan et al. [ 26 ] established a stable and efficient transformation system through somatic embryogenesis and adventitious bud regeneration in Lilium pumilum DC. Fisch. and Lilium longiflorum . After method optimization, the transformation efficiency reached 29.17% and 4%, respectively. Although the transformation efficiency in 'White Heaven' is still low, it is relatively stable and can be regenerated within 1 month. Song et al. [ 17 ] improved the original transformation system by adjusting the pH and CaCl 2 concentration of the medium; the number of resistant plants increased by 2.7–6.4 times, the number of positive lines increased by 3–6 times transformation, and the genetic transformation efficiency increased by 5.7–13.0%. In the latest study, the genetic transformation system of Oriental hybrid lily was further optimized, and the efficiency was increased to 60% by screening for the lethal concentration of antibiotics, the concentration of the bacterial solution and the duration of infection.

Good explant material is the basis of plant genetic transformation. The success of transformation depends on the selection and totipotency of explants [ 32 ]. Researchers have tested different explants for genetic transformation of target genes based on the Agrobacterium system (Table  1 ). Calli generated from floral organs, scales, leaves, or seeds have been used for genetic transformation in most lily hybrids [ 27 , 33 , 34 ]. Related studies have shown that filamentous calli have a faster growth rate and may be more susceptible to Agrobacterium infection [ 35 ]. In the Oriental hybrid lily ( Lilium cv. Acapulco), an Agrobacterium -mediated lily transformation system was successfully established by using filament-induced filiform calli as explants. Although transient expression of the GUS reporter gene could be detected by root–, leaf–, stalk–, ovary– and anther-induced callus infection, no positive transgenic tissues or plants were obtained [ 27 ]. In recent years, several other explants have been developed and offer additional possibilities for improving transformation efficiency. Liu et al. [ 24 ] discussed the effect of the direct regeneration pathway and the callus regeneration pathway on the transformation efficiency in Agrobacterium -based genetic transformation experiments using stem segments induced by lily scales as explants. Notably, when stem segments were used as explants, adventitious buds obtained via the direct regeneration pathway after coculture significantly increased the regeneration rate of resistant plants and decreased the gene escape rate [ 24 ]. Another study showed that the direct use of scales as transformation explants did not significantly improve the transformation rate but did greatly shorten the genetic transformation cycle [ 26 ]. Different explant materials have their own advantages. Cohen and Meredith [ 10 ] used a particle bombardment approach to carry out lily genetic transformation and reported that the ability of embryonic calli to accept foreign genes was 50–70 times greater than that of ordinary calli. Embryogenic calli are composed of many embryogenic cells, and each cell has the potential to develop into adult somatic embryos. Therefore, using embryogenic calli as explant materials can result in a more stable transformation population with a lower chimaerism rate, which is very important for the research and development of plant genetic transformation [ 36 , 37 , 38 , 39 ]. Mercuri et al. [ 25 ] induced embryonic calli using the styles and peduncles of Lilium longiflorum ‘Snow Queen’, and they were found to be highly competent for transformation. Recently, two studies reported an efficient protocol with high transformation efficiency for Lilium pumilum DC.Fisch. using embryonic calli. Despite their long transformation cycle, embryogenic calli are the most common explant material for the genetic transformation of lily due to their high cell proliferation rate and genetic stability [ 17 , 26 ].

Strains of agrobacterium

To date, many successful cases of stable genetic transformation of plants mediated by Agrobacterium have been reported [ 40 , 41 , 42 , 43 , 44 ]. The strain of Agrobacterium can also considerably influence the transformation frequency. With the continuous updating and optimization of plant genetic transformation technology, several researchers have attempted to improve the efficiency of plant transformation by changing various parameters, including Agrobacterium strains [ 45 ] (Table  1 ). Different plants have different preferences for the routinely used Agrobacterium strains EHA105, EHA101, LBA4404, GV3101, AGL1 and C58 [ 8 , 27 , 29 , 46 ]. In alfalfa [ 47 ], tomato [ 48 ], grasspea [ 49 ], and pigeon pea [ 50 ], LB4404 and LBA4404 were found to be more virulent and highly effective, offering higher transformation efficiency. However, the LBA4404 strain has been less frequently reported in lilies. Mercuri et al. [ 25 ] reported that LBA4404 effectively promoted the infection of embryogenic calli from Lilium longiflorum 'Snow Queen'. According to another study of genetic transformation in lily, the use of the EHA105 strain to infuse embryonic calli seemed to be more beneficial for improving transformation efficiency [ 26 ]. In recent years, the strains EHA101 and EHA105 have been more widely used in lily transformation and are considered to result in greater transformation frequency [ 16 , 18 ].

Selection of markers and reporter genes

The selection of marker genes also determines the efficiency of plant genetic transformation. They are usually delivered along with the target gene, conferring resistance to toxic compounds in plants and facilitating the growth of transformed cells in the presence of such unfavourable conditions. Normally, the marker gene and the target gene are connected to the same plasmid and delivered to the plant somatic cells via the Agrobacterium -mediated method. Suitable marker genes can help to quickly and efficiently screen many transformed materials and remove untransformed cells [ 8 ]. Conventional marker genes include the hpt gene, which encodes hygromycin phosphotransferase and confers resistance to hygromycin; the npt-II gene, which encodes neomycin phosphotransferase II and confers resistance to kanamycin, neomycin and geneticin; and the bar gene, which encodes phosphinothricin acetyltransferase and confers resistance to the herbicide phosphinothricin [ 23 , 51 , 52 ]. The type of resistance marker gene is determined in the vector, and hpt and npt-II are commonly used as marker genes for lily transformation [ 3 , 17 , 18 , 24 ]. Linking the GUS gene to a transformation vector for double or even triple marker gene screening combined with GUS histochemical staining is also an effective strategy for reducing the false positive rate of resistant plants [ 16 , 22 , 26 ] (Table  1 ).

Key parameters in the genetic transformation program

Ph of the medium.

The expression of the Agrobacterium virulence gene vir is the basis for the transformation of plant cells and the key to ensuring infection efficiency, which is strongly dependent on the pH of the medium [ 17 , 22 , 53 ]. Previous studies have shown that an acidic pH is more conducive to the expression of vir , and as the pH of the preculture or coculture medium decreases, the expression of vir is significantly upregulated [ 54 , 55 ]. Maintaining the pH at 5.2 effectively increased the genetic transformation efficiency of tomato cotyledons [ 56 ]. The virA and virG genes are switches that activate the expression of the vir gene [ 57 ]. Agrobacterium has a chemotactic system different from that of E. coli and is attracted to chemical inducers such as carbohydrates, amino acids and phenolic compounds [ 58 ]. High concentrations of chemical inducers bind to virA to induce the expression of the vir gene and trigger T-DNA transfer [ 59 ]. Acetylsyringone (AS) is a common class of natural phenolic compounds that can promote the direct entry of microorganisms into plant cells through wounds and achieve T-DNA transfer by activating the expression of the vir gene [ 58 , 60 , 61 , 62 ]. AS has been widely used in the genetic transformation of lily [ 23 , 27 ]. Although pH 7.0 is the most suitable environment for the growth of Agrobacterium , vir is more easily expressed under acidic conditions after the addition of AS, while vir expression is hardly induced under neutral pH conditions [ 54 , 63 ]. In a Lilium pumilum DC.Fisch. genetic transformation experiment, a stable pH of 5.8 in suspension and coculture media resulted in a somatic embryo transformation efficiency of 29.17% [ 26 ]. When the pH was adjusted to 5.0, the number of resistant calli increased significantly, and the transformation efficiency increased by 5.7–13% [ 17 ]. Ogaki et al. [ 23 ] reported that exogenous MES could effectively control the pH of the medium. This study further investigated the effect of adding different concentrations of MES (0, 10, 20, 50 and 100 mM) on the transformation efficiency of lily. The results showed that transient expression of the GUS gene could be observed only in coculture medium containing MES, and larger numbers of transgenic calli could be obtained by the addition of 10 mM MES buffer [ 30 ]. The above conclusions indicate that maintaining pH in the range of 5–6 values according to different varieties in the preculture and coculture stages is important for improving the efficiency of genetic transformation (Table  2 ).

Culture medium supplements

The composition of the medium is another rate-limiting factor affecting the genetic transformation efficiency of lily, and the process involves the preculture, coculture and regeneration of resistant plants. Many studies have shown that the addition or removal of certain compounds can significantly improve the efficiency of lily transformation (Table  2 ). MS medium, which contains 20.6 mM NH 4 NO 3 , is widely used in the tissue culture and genetic transformation of lily. However, the presence of NH 4 NO 3 limits the efficiency of genetic transformation in lily [ 23 , 26 , 64 ]. Previous studies have shown that virG transcription can be activated by low concentrations of phosphate [ 53 , 58 ]. When a low concentration of KH 2 PO 4 was used as a salt source instead of NH 4 NO 3 , there was no significant change in the number of regenerated resistant calli. In contrast, the complete removal of KH 2 PO 4 had a positive effect on lily transformation [ 22 ]. In a transformation study of the lily ‘Sorbonne’, it was found that the removal of KH 2 PO 4 , NH 4 NO 3 , KNO 3 or macroelements in the medium could significantly improve the transformation efficiency [ 28 ]. Montoro et al. [ 65 ] reported that Ga 2+ -free media significantly increased GUS activity in Brazilian rubber trees. However, another study concluded that the effect of GaCl 2 on plant transformation efficiency appears to be strongly dependent on genotype. Ga 2+ is considered one of the key factors involved in improving the efficiency of genetic transformation in improved lily genetic transformation systems. Increasing the GaCl 2 concentration from 0.44 g/L to 1.32 g/L significantly increased the germination coefficient of Lilium -resistant somatic embryos [ 17 ]. AS is an indispensable compound in the genetic transformation of lily, and its concentration is also a key factor; an AS concentration that is too high adversely affects T-DNA transfer [ 17 ]. Notably, researchers have found other compounds that can replace AS, and they can provide higher transformation efficiency. Chloroxynil (CX) is a class of phenolic compounds with a mode of action similar to that of AS that also improves the efficiency of treatment by activating the expression of vir. In the genetic transformation of lotus seeds, the transformation efficiency of explants treated with CX was 6 times greater than that of explants treated with AS [ 66 ]. Wei et al. [ 28 ] further confirmed the effect of CX in lily. When 4 μM CX was used, the transformation efficiency reached 11.1%, while 100 μM AS achieved only 6.6%, indicating that CX can replace AS in lily genetic transformation. Early studies showed that the promoting effect of carbohydrate substances other than glucose and xylose on vir gene activity was consistent with that of AS [ 67 , 68 ]. Further studies by Azadi et al. [ 22 ] showed that MS media supplemented with monosaccharides significantly inhibited the expression of the GUS gene, and no hygromycin-resistant lily calli were obtained. In contrast, adding sucrose significantly improved the efficiency of genetic transformation. In summary, for most series lilies, removing NH 4 NO 3 and adding an appropriate amount of AS has a positive effect on improving the genetic transformation efficiency. CX may be an excellent compound to replace AS, and it is worth further attempts in the future.

Bacterial concentration and infection time

The Agrobacterium concentration and infection time play pivotal roles in the transformation of lily. A low bacterial concentration and short infection duration will result in the failure of Agrobacterium to fully adhere to explant tissues, resulting in the inability to achieve effective transformation [ 69 ]. However, a high concentration of bacteria or long infection duration may also lead to rapid bacterial growth, which can cause severe damage to the recipient material [ 17 ]. Cell resistance varies greatly among different plant explants, and plant tolerance to different agrobacterium concentrations also differs. When the OD 600 was 0.8, the highest GUS expression rate was detected in embryogenic calli, but the percentage of resistant calli significantly decreased compared with that when the OD 600 was 0.6. For scales, the GUS expression rate and adventitious bud regeneration rate peaked when the OD 600 was 0.6. Infection time is also the key to determining transformation efficiency, and research shows that for embryonic calli and scales of Lilium pumilum, DC. Fisch, 15 min is the optimal time for Agrobacterium infection [ 26 ]. However, for embryogenic calli of the Oriental hybrid lily ‘Siberia’, an OD 600 of 0.4 was more beneficial for improving the transformation efficiency [ 18 ]. In addition, it has been reported that in the coculture process, the proliferation of Agrobacterium on the surface and around the callus increases with the removal of some elements, indicating that these elements have an inhibitory effect on Agrobacterium . Negative effects of bacterial overgrowth were observed when 10 mM MES was added to coculture media of sensitive varieties such as ‘Red Ruby’ and ‘Casa Blanca’. Therefore, screening different varieties of MES can effectively reduce bacterial growth and improve the transformation efficiency of lily [ 22 ].

Antibiotic selection

In the process of plant genetic transformation, an appropriate concentration of antibiotics can effectively inhibit the growth of non-transformed Cefatothin, and this is also a crucial step in determining the success of genetic transformation. Kanamycin, hygromycin and glyphosate have been used extensively for lily transformation due to their high availability and low toxicity, despite the occurrence of false positives in the screening of resistant plants [ 70 , 71 ] (Table  2 ). Lily explants of different genotypes have different antibiotic concentration requirements. Even within the same variety, different explant types have great differences in antibiotic tolerance [ 18 ]. Studies have shown that embryonic calli of Lilium pumilum DC. Fisch. The plants almost stopped growing and died after treatment with hygromycin supplemented at 40 mg·L −1 , resulting in extremely low growth and induction rates. However, a few scales of ‘White Heaven’ still formed complete buds under these conditions. Adjusting the concentration of hygromycin to 30 mg·L −1 reduced the browning rate of embryogenic calli by approximately 20% and significantly increased the growth and transformation rate. Therefore, 30 mg·L −1 and 40 mg·L −1 were the best hygromycin concentrations suitable for embryogenic calli and scales of Lilium pumilum DC. Fisch., respectively [ 26 ]. Another necessary antibiotic is a bacteriostatic antibiotic, which is mainly used to prevent the transformation of material from dying or difficult regeneration due to excessive Agrobacterium contamination. Cef is a common bacteriostatic agent used in plant transformation that has extensive resistance and inhibits the growth of Agrobacterium [ 72 , 73 ]. However, high concentrations of Cef can inhibit the growth of explant cells. Based on the results of studies on different lily varieties and explants, we believe that 300–400 mg·L −1 Cef may have a broad-spectrum effect [ 18 , 26 ]. The concentration of Agrobacterium may be a prerequisite for screening Cef concentrations. Even 400 mg·L −1 Cef had no bacteriostatic effect when the concentration of the bacterial solution was too high. When the OD600 of the bacterial solution is maintained within 0.2–0.4, 300 mg·L −1 Cef can have good antibacterial efficacy [ 18 ]. Therefore, it is necessary to combine the concentration of the bacterial solution with the concentration of the bacteriostatic agent during screening.

Preculture, infection and coculture procedures

Preculture, infection and coculture are the key steps in determining the success of plant genetic transformation. Previous studies have generally been conducted under the belief that the preculture of explants before transformation can effectively promote cell division so that they can maintain the best life state during infection and integrate foreign genes more easily [ 74 , 75 ]. The timing of preculture depends on the type and quality of the explants. Yan et al. [ 26 ] discussed the influence of preculture time on the transformation efficiency of L. pumilum and ‘White Heaven’. The results showed that the expression rate of GUS was lower in uncultured calli or scale explants. Similarly, compared with those of the control group, the proliferation and survival rates of the explant-treated group were significantly lower. For embryogenic calli, GUS expression and the proliferation rate were the highest in resistant calli after 10 days of preculture (66.67% and 63.33%, respectively). After 4 days of preculture, GUS expression and bud resistance began to decrease for the traumatized scales. Although the percentage of resistant buds was the highest after 2 days of preculture, a higher GUS expression rate appeared after 3 days (Table  2 ).

In lily, wounded explant materials are often more conducive to the transfer and integration of T-DNA, which can greatly improve the efficiency of genetic transformation [ 27 , 76 ]. Wei et al. [ 28 ] further confirmed this view. According to the results of Agrobacterium -mediated ‘Sobone’ genetic transformation, ultrasound treatment for 20 s can produce thousands of microwounds in explants, promote the penetration of Agrobacterium into the internal tissues of plants, and effectively improve the efficiency of transformation. The combination of heat shock and ultrasound had no significant effect. Coculture is an essential stage during which T-DNA is transferred into plant cells [ 77 ]. Therefore, coculture time is also an external factor that has been widely examined [ 75 ]. The time required for Agrobacterium -mediated gene transfer and integration into the plant genome varies widely depending on the genotype and explant type, usually ranging from a few hours to a few days [ 78 , 79 , 80 , 81 ]. Wu et al. [ 82 ] compared the transformation efficiency of bulb sections of Gladiolus under coculture for 3 days and 12 days, and the results showed that the transformation rate of coculture for 12 days was more than twice that for 3 days, indicating that a longer coculture time may benefit Agrobacterium infection and transformation. However, in Lilium pumilum DC. After more than 5 days of coculture, Fisch, which is also a typical bulbous flower, will cause severe browning and death of embryogenic calli and scales [ 26 ]. However, for the calli of the other two kinds of lilies, coculture for 7 days still maintained a high transformation efficiency, indicating that the tolerance of lily to Agrobacterium may depend on the genotype [ 15 , 27 ]. Drying plant tissue or cells before coculture can also promote T-DNA transfer [ 83 ]. The growth state and speed of calli in dry coculture were better than those in traditional media [ 84 ]. During subsequent resistance screening, only a few tissues were contaminated by the bacterial solution under dry conditions, and the regeneration rate of resistant calli increased significantly [ 34 ]. An appropriate low temperature during coculture also had a positive effect on T-DNA transfer [ 85 , 86 ]. The transformation efficiency of Boehmeria nivea (L.) Gaud. was significantly improved by coculture at 20 °C compared with 15 °C, 25 °C and 28 °C [ 87 ]. In the genetic transformation system of Gossypium hirsutum , 19 °C can significantly increase the regeneration rate of resistant calli and completely inhibit the proliferation of Agrobacterium . However, the effect of temperature on the genetic transformation efficiency of lily has not been clearly reported. In future studies, we can try to optimize the genetic transformation system for lily by adjusting the ambient temperature at each link.

Application of genetic transformation technology

Generation of the crispr/cas9 system.

With the continuous development of gene function research technology, gene modification has been widely used in basic plant research and molecular breeding [ 88 ]. Since CRISPR/Cas9 gene editing technology was successfully applied in Lotus japonicus , because of its simple design and limited operation, this technique has been successfully used in the study of flower anatomy and morphology, flower colour, flowering time, fragrance and stress resistance in various ornamental plants [ 89 , 90 , 91 , 92 ]. Yan et al. [ 26 ] established a stable and efficient genetic transformation system for two lily genotypes using somatic embryos and scales as explants and generated completely albino, light yellow and albino green chimeric mutants via directional knockout of the PDS gene; these authors successfully applied CRISPR/Cas9 technology to lily for the first time. The CRISPR/Cas9 system also validated the feasibility and efficiency of the two genetic transformation systems.

Application for improvement of plant morphogenesis

Morphogenetic genes are key factors that control plant organogenesis and somatic embryogenesis and determine the location of target cells to produce different structures or whole plants [ 8 ]. The functions of many morphogenetic genes have been identified in model plants and important cash crops and have been applied in scientific practice to increase the efficiency of regeneration and genetic transformation [ 93 , 94 ]. In lily, the somatic embryo has always been a good explant for genetic transformation. Plant somatic cells dedifferentiate into embryogenic stem cells under the action of external/internal genetic factors and then divide into somatic embryos. This process is the most critical stage for plant cells to become totipotent [ 95 ]. The most widely used method for somatic embryogenesis (SE) in various plants is the use of exogenous plant growth regulators, especially auxin [ 96 ]. Song et al. [ 17 ] reported that overexpression of LpABCB21 in lily could shorten the time required for SE without changing the exogenous PIC (Picloram). In contrast, the LpABCB21 mutant lines delayed somatic embryo generation by 1–3 days, but the induction rate of adventitious buds was significantly greater than that in the LpABCB21 -overexpressing lines. The study also indicated that the PILS (PIN-LIKES) family member LpPILS7 may participate in auxin regulation through the same mechanism as LpABCB21 , and the somatic embryo induction efficiency of the pils7 mutant was significantly reduced by approximately 10–60%. The importance of miRNAs in SE processes has also been validated in many dicot species and crops [ 82 , 97 ]. In Agrobacterium- mediated Lilium embryo transformation experiments, silencing lpu-miR171a and lpu-miR171b promoted starch accumulation and the expression of key cell cycle genes in calli, significantly accelerated the SE process in Lilium , and resulted in the same phenotype as overexpressing LpSCL6-II and LpSCL6-I . WUSCHEL is a typical gene family involved in the regulation of plant morphogenesis, and its expression is upregulated in many plant SE processes [ 98 , 99 ]. LlWOX9 and LlWOX11 reportedly play a positive regulatory role in the formation of bulbils by influencing cytokinin signalling [ 100 ]. However, the function of WUSCHEL members in Lilium embryogenesis remains to be further verified. It seems that changing the expression level of morphogenetic genes can be an effective means to improve the genetic transformation efficiency of lily, and this topic is worthy of further exploration in the future.

Genetic modification for agronomically important traits

At present, few studies have investigated genetic modification in lily, and most studies have validated the function of target genes only through transient gene transformation (Table  3 ). With the continuous improvement of the genetic transformation system for lily, a few key genes regulating important traits have been identified. In addition to influencing SE processes, many morphogenetic genes are involved in regulating plant organ formation or quality maintenance [ 101 , 102 , 103 ]. LaKNOX1 , a member of the homeobox gene family involved in regulating plant organogenesis, was also further validated in 'Siberia' and 'Sorbonne' [ 18 ]. A recent study revealed that a key gene, LdXERICO , is involved in the regulation of dormancy in Lilium davidii var. unicolour , which indicated that the maintenance of dormancy depends on the ABA-related pathway and that the transcription of LdXERICO is inhibited by the temperature response factor LdICE1 during low-temperature storage, which eventually leads to lily sprouting [ 3 ]. Recently, the LoNFYA7-LoVIL1 module has also been shown to play a key role in orchestrating the phase transition from slow to fast growth in lily bulbs [ 104 ]. Biological and abiotic stresses have a great impact on plant growth and development, and these stresses usually disrupt cellular mechanisms by inducing changes at the physiological, biochemical, and molecular levels in plants [ 105 ]. The identification of key genes involved in the regulation of the stress response in lily was aimed at improving plant resistance to biotic and abiotic stresses. Low temperature, drought, salt stress and abscisic acid treatment can significantly upregulate the expression of LlNAC2 , a member of the NAC transcription factor family. Overexpression of the LlNAC2 gene in tobacco significantly enhances the tolerance of transgenic plants to various abiotic stresses [ 106 ]. Chen et al. [ 18 ] used the genetic transformation system of lily to transform LlNAC2 and successfully generated a transgenic line, which provided favourable support for further clarifying the function of LlNAC2 in coping with abiotic stress in lily species. Typical biological stresses, including bacteria, fungi, viruses, insects and other diseases and pests, seriously negatively affect the quality of ornamental plants [ 89 ]. Several researchers have attempted to increase the resistance of lily plants to pathogens or pests by increasing or decreasing the expression of certain genes. Pratylenchus penetrans (RLN) is one of the main pests and diseases encountered in lily production. The overexpression of the rice cystatin (Oc-IΔD86 ) gene in Lilium longiflorum cv. 'Nellie White' showed that the resistance of transgenic lily to RLN infection was significantly enhanced, and the total nematode population decreased by 75 ± 5%. Compared with wild-type plants, OcIΔD86 -overexpressing plants also exhibited improved growth and development [ 107 ]. Plant resistance to viruses is usually established by transferring the coat protein-encoding gene of the virus into the plant [ 89 ]. Azadi et al. [ 22 ] introduced a cucumber mosaic virus (CMV) replicase defective gene ( CMV2-GDD ) into lily using an Agrobacterium -mediated genetic transformation system and identified two transgenic strains that showed stronger resistance to CMV. In Lilium oriental cv. 'Star Gazer', overexpression of the rice chitinase 10 ( RCH10 ) gene enhanced the resistance of lily to Botrytis elliptica [ 19 ]. Du et al. [ 108 ] identified a gene named LhSorPR4-2 , which encodes a disease course-related protein involved in fighting Botrytis elliptica infection in lily, and the overexpression of LhSorPR4-2 significantly enhanced the resistance of lily to Botrytis . This study also revealed that the function of LhSorPR4-2 was closely related to its chitinase activity. Another study showed that the transcription level of the resistance gene LrPR10-5 was significantly increased in transgenic ‘Siberia’ plants that overexpressed LrWRKY1 , which subsequently promoted resistance to F. oxysporum [ 109 ].

Conclusions

Since the first successful transformation event in lily, remarkable progress has been made; a variety of lily genetic transformation systems have been gradually established, and many excellent new germplasms have been obtained. However, the genetic transformation of lily still faces great challenges due to its strong genotypic dependence. Most related studies have focused on optimizing existing systems, and applicable genetic transformation systems have not yet been established for most lily strains with high market value. For a long time, how to stably and efficiently deliver recombinant gene vectors into plant cells has been the focus of most scholars. At present, the most common delivery method is Agrobacterium -mediated transformation. In addition to the cumbersome tissue culture process, the transformation efficiency also depends greatly on the genotype. The choice of explants for DNA, strains and vectors; culture conditions; and effective selection markers are all major factors that play pivotal roles in successful transformation. At present, most transgenic work in lily is limited by laboratory-scale gene function verification, and even after successful transformation, it is not easy to obtain stable transgenic plants. In recent years, various types of Rhizobium , including Ensifer adhaerens , Ochrobactrum haywardense and Sinorhizobium meliloti , have shown great potential in the transformation of nonagricultural bacterial systems. Some studies have revealed an invisible mechanism for delivering DNA into plant cells, where Sinorhizobium meliloti can infect both monocotyledonous and dicotyledonous plants [ 164 ]. Another way to improve the traditional transformation model is to coexpress developmental regulatory factors or morphogenetic genes during transformation. The overexpression of the developmental regulatory factors GROWTH-REGULATING factor (GRF) and Boby room (Bbm) in maize and sorghum, for which it is difficult to achieve genetic transformation, can significantly improve transformation efficiency [ 101 , 165 ]. Pollen tube transformation is a transformation system that does not require tissue culture, but this method is suitable only for model plants such as Arabidopsis and a few closely related plants. The pollen magnetic transfection-mediated transformation method can be applied to lily, but it may be species- or varietal specific. Zhang et al. [ 166 ] optimized pollen culture conditions, established a new method for the transient transformation of pollen magnetic beads, and concluded that the transformation efficiency was positively correlated with the transverse diameter of pollen and negatively correlated with the ratio of longitudinal diameter to transverse diameter. This study also evaluated the transformation efficiency of Lilium regale L. ‘Sweet Surrender’ and Lilium leucanthum ; L. ‘Sweet Surrender’ and Lilium leucanthum reached 85.80% and 54.47%, respectively, but successful transformation was not achieved in Lilium davidii var. unicolour . Particle bombardment and the electrical shock method are also common methods used in plant genetic transformation. At present, the main explants of the electroshock method are plant protoplasts, but because of their high cost, abundance of chimaeras after transformation and limited stable expression in offspring, these methods cannot be applied to large-scale lily plants. Therefore, exploring genetic transformation systems based on non-tissue culture methods is expected to alleviate the pressure of genetic transformation of lily in the future. Cao et al. [ 167 ] reported a cut-dip-bud (CDB) delivery system. Briefly, the CDB delivery system consists of cutting the junction of plant roots under nonsterile conditions, infecting the upper end with Agrobacterium , taking positive new roots after culture, cutting them into segments and culturing them again to obtain regenerated and transformed plants. Researchers have studied the effects on rubber grass ( Taraxacum koko-saghyz Rodin , TKS), Ipomoea batatas [L.] Lam.), Ailanthus altissima (Mill) Swingle, and Aralia elata (Miq.) The CDB system has been tested in several difficult-to-transform plants, including three woody plants, one of which is Clerodendrum chinense Mabb. The results showed that the CDB delivery system has wide applicability in plant genetic transformation. Furthermore, there is a great need for the validation of promoters other than CaMV35S to achieve optimal expression of transforming genes [ 8 ].

Notably, various plant genetic transformation systems have been further applied to establish RNA interference (RNAi) and gene editing technology systems. To date, there have been few reports on the application of RNAi and CRISPR/Cas9-based gene editing techniques in lilies. In 2019, Yan established a stable and efficient genetic transformation system for somatic embryo regeneration for the first time and successfully conducted targeted gene editing based on CRISPR/Cas9 [ 20 , 26 ]. As one of the sharpest tools in genetic technology, CRISPR/Cas9 “gene scissors” have set off a research boom in basic plant research and directed breeding work. It has great application potential for improving yield, quality, herbicide resistance, abiotic stress resistance and disease resistance. However, there are few successful cases of gene editing using CRISPR/Cas9 technology in lily, which may be related to its high heterozygosity. The stable genetic transformation system has not been widely used, which leads to many difficulties in the study of gene function. With the continuous improvement of the technical system of lily genetic transformation and the emergence of new delivery methods, the combination of multiple transformations may be the only way to develop functional lily genomics in the future, and major breakthroughs in genetic engineering applications in lily breeding are expected not to occur.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. No datasets were generated or analysed during the current study.

Abbreviations

Embryogenesis

Acetosyringone

REGULATING factor

Cut-dip-bud

RNA interference

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This work was financed by the National Natural Science Foundation of China (grant number 32302589), the Postdoctoral Science Foundation of China (2023MD744227), Shenyang Innovation Program of Seed Industry (21-110-3-12), Liaoning Province Germplasm Innovation Grain Storage Technology Special Plan (2023JH1/10200010), and the earmarked fund for CARS (CARS-23).

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Additional Files 1. Fig. S1: Literature keywords related to the field of lily research that appear together in the map. Keywords that appear more than 30 times are displayed, and different colours represent different cluster.

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Fan, X., Sun, H. Exploring Agrobacterium -mediated genetic transformation methods and its applications in Lilium . Plant Methods 20 , 120 (2024). https://doi.org/10.1186/s13007-024-01246-8

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DOI : https://doi.org/10.1186/s13007-024-01246-8

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• Agrobacterium tumefaciens
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• Transgenic plants
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