chapter 3 methodology sampling technique

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3.4 Sampling Techniques in Quantitative Research

Target population.

The target population includes the people the researcher is interested in conducting the research and generalizing the findings on. 40 For example, if certain researchers are interested in vaccine-preventable diseases in children five years and younger in Australia. The target population will be all children aged 0–5 years residing in Australia. The actual population is a subset of the target population from which the sample is drawn, e.g. children aged 0–5 years living in the capital cities in Australia. The sample is the people chosen for the study from the actual population (Figure 3.9). The sampling process involves choosing people, and it is distinct from the sample. 40 In quantitative research, the sample must accurately reflect the target population, be free from bias in terms of selection, and be large enough to validate or reject the study hypothesis with statistical confidence and minimise random error. 2

Sampling techniques

Sampling in quantitative research is a critical component that involves selecting a representative subset of individuals or cases from a larger population and often employs sampling techniques based on probability theory. 41 The goal of sampling is to obtain a sample that is large enough and representative of the target population. Examples of probability sampling techniques include simple random sampling, stratified random sampling, systematic random sampling and cluster sampling ( shown below ). 2 The key feature of probability techniques is that they involve randomization. There are two main characteristics of probability sampling. All individuals of a population are accessible to the researcher (theoretically), and there is an equal chance that each person in the population will be chosen to be part of the study sample. 41 While quantitative research often uses sampling techniques based on probability theory, some non-probability techniques may occasionally be utilised in healthcare research. 42 Non-probability sampling methods are commonly used in qualitative research. These include purposive, convenience, theoretical and snowballing and have been discussed in detail in chapter 4.

Sample size calculation

In order to enable comparisons with some level of established statistical confidence, quantitative research needs an acceptable sample size. 2 The sample size is the most crucial factor for reliability (reproducibility) in quantitative research. It is important for a study to be powered – the likelihood of identifying a difference if it exists in reality. 2 Small sample-sized studies are more likely to be underpowered, and results from small samples are more likely to be prone to random error. 2 The formula for sample size calculation varies with the study design and the research hypothesis. 2 There are numerous formulae for sample size calculations, but such details are beyond the scope of this book. For further readings, please consult the biostatistics textbook by Hirsch RP, 2021. 43 However, we will introduce a simple formula for calculating sample size for cross-sectional studies with prevalence as the outcome. 2

z   is the statistical confidence; therefore,  z = 1.96 translates to 95% confidence; z = 1.68 translates to 90% confidence

p = Expected prevalence (of health condition of interest)

d = Describes intended precision; d = 0.1 means that the estimate falls +/-10 percentage points of true prevalence with the considered confidence. (e.g. for a prevalence of 40% (0.4), if d=.1, then the estimate will fall between 30% and 50% (0.3 to 0.5).

Example: A district medical officer seeks to estimate the proportion of children in the district receiving appropriate childhood vaccinations. Assuming a simple random sample of a community is to be selected, how many children must be studied if the resulting estimate is to fall within 10% of the true proportion with 95% confidence? It is expected that approximately 50% of the children receive vaccinations

z = 1.96 (95% confidence)

d = 10% = 10/ 100 = 0.1 (estimate to fall within 10%)

p = 50% = 50/ 100 = 0.5

Now we can enter the values into the formula

Given that people cannot be reported in decimal points, it is important to round up to the nearest whole number.

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

3 Chapter 3: Sampling Methods

Case study : rehabilitation versus incarceration of juvenile offenders.

Research Study

Public Preferences for Rehabilitation versus Incarceration of Juvenile Offenders 1

Research Question

Is the public willing to pay more in taxes for rehabilitation or incarceration as a response to serious juvenile crime?

Methodology

Through a process known as random digit dialing, the researchers of this study randomly sampled 29,532 telephone numbers from four states (Illinois, Louisiana, Pennsylvania, and Washington). Random digit dialing is a variation of probability sampling (discussed later in this chapter) where researchers utilize a computer program that randomly dials the last four digits of a phone number in a known area code. With this variation of sampling, all phone numbers within a given area code have an equal chance at selection for the sample.

Following the random number selection process, researchers excluded randomly chosen phone numbers that corresponded to fax machines, businesses, government organizations, nonworking numbers, and so on. After excluding these phone numbers, a total of 7,132 eligible phone numbers remained. Of the remaining eligible phone numbers, 2,282 telephone interviews were eventually completed across the four states for an overall response rate of 32%.

The survey instrument administered to respondents inquired about rehabilitation versus incarceration for serious juvenile offenders, as related to an increase in household taxes. Of those respondents who agreed to participate in the telephone survey, one-half were randomly assigned the rehabilitation scenario and one-half were randomly assigned to receive the incarceration scenario. The hypothetical rehabilitation versus incarceration scenarios are as follows:

(Rehabilitation Scenario) �Currently in [state] juvenile offenders who commit serious crimes such as robbery are put in jail for about one year. Suppose [state] citizens were asked to approve the addition of a rehabilitation program to the sentence for these sorts of crimes. Similar programs have reduced youth crime by 30%. Youths in these programs are also more likely to graduate from high school and get jobs. If the change is approved, this new law would cost your household an additional $100 per year in taxes.�

(Incarceration Scenario) �Currently in [state] juvenile offenders who commit serious crimes such as robbery are put in jail for about one year. Suppose [state] citizens were asked to vote on a change in the law that would increase the sentence for these sorts of crimes by one additional year, making the average length of jail time two years. The additional year will not only impose more punishment but also reduce youth crime by about 30% by keeping juvenile offenders off the street for another year. If the change is approved, this new law would cost your household an additional $100 per year in taxes.�

After receiving one of the initial scenarios, respondents were asked: �Would you be willing to pay the additional $100 in taxes for this change in the law?� Those who answered �yes� were then asked if they would be willing to pay $200 for the same change. Those respondents who originally answered �no� to the $100 increase were asked if they would be willing to pay $50 for the same change in the law. Based on the survey, four potential outcomes were measured among respondents: 1) those who said no to $100 and no to $50, 2) those who said yes to $50 but no to $100, 3) those who said yes to $100 but no to $200, and 4) those who said yes to $100 and yes to $200.

Across four states and 2,282 completed telephone interviews, the results of the survey revealed respondents were willing to pay (WTP) more for rehabilitation than incarceration for serious juvenile offenders. Among respondents who were randomly chosen to receive the rehabilitation scenario, 28.5% were not willing to pay any additional taxes. Conversely, roughly 70% were willing to pay at least $50, with nearly 65% willing to pay $100�$200. Among respondents randomly chosen to receive the incarceration scenario, 39% were not willing to pay any additional taxes. Roughly 60% of respondents who received the incarceration scenario were willing to pay at least $50. In short, more respondents were willing to pay (and pay more) for rehabilitation than incarceration for serious juvenile offenders.

Limitations with the Study Procedure

Specific to sampling, perhaps the greatest limitation with this study lies in the relatively high nonresponse rate. In this study, the overall response rate was 32%. This means that nearly 70% of all eligible phone numbers, and hence the perspectives of roughly 70% of randomly sampled persons associated with those phone numbers, were not able to be assessed in the current study. High rates of nonresponse effectively reduce sample size, increase sampling error (the difference in results expected between surveying a sample versus the whole population), and call into question the generalizability of findings since the random sample becomes less representative of the larger population. One relevant question to be considered with high nonresponse is whether those individuals who were eligible to participate in the survey but did not, differ from those who were eligible and did participate. It is possible, for example, that those who were randomly chosen to participate in the telephone interview but did not may hold significantly different attitudes towards rehabilitation versus incarceration compared to those who ultimately participated in the survey. And, if those who refused participation in the survey hold widely different opinions of rehabilitation versus incarceration relative to increasing taxes, the sample (and their results) cannot be said to be representative of the larger population. In sum, such a high rate of nonresponse affects both the representativeness of the sample and hence the ability to generalize the results to the larger population of interest. As discussed in detail in the chapter, a primary advantage of a representative sample is the ability to generalize or apply the results from the sample to the larger population. If in fact large numbers of the sample refuse to participate, this may inhibit the ability of researchers to generalize sample findings to the larger population and may call into question results of a study based on a low response rate.

Beyond response rates and sample sizes, it is important to note other general limitations with survey research such as that highlighted in the current study. As acknowledged by the study authors, it is possible that the hypothetical scenario failed to elicit �real� responses and feelings on rehabilitation versus incarceration from respondents because the scenario was hypothetical, and not a real or genuine proposed change in the law in the states examined. Moreover, surveys in general are replete with potential limitations. While these issues are covered in detail in Chapter 4, it is important to note that surveys may fail to elicit �considered� answers to questions. This potential is particularly relevant in telephone interviews, which are rarely planned ahead of time and therefore respondents might be contacted during a time where they are rushed or otherwise unable or unwilling to provide completely considered answers. More broadly, respondents may not have completely understood the questions being asked. If there was any confusion on the part of the respondents, this could have affected their responses to the questions and ultimately the outcome of the study. Again, these are general limitations of surveys and not necessarily specific to this study. However, such potentials should be considered when interpreting survey results.

Although no research study is perfect, to become an informed consumer of research it is important to be aware of potential limitations not only in sampling, but in response rates and general research methodology. Although this chapter focuses on sampling, knowledge of additional areas often associated with sampling, such as survey limitations, nonresponse or nonparticipation by respondents, and concerns about representativeness and generalizability, allows a clearer picture of the entire research process of which sampling is only one part. In large part, being an informed consumer of research requires more than an understanding of research results; it also requires knowledge of how the results were produced in the first place.

Impact on Criminal Justice

There are many ways in which the highlighted study is important to criminal justice. In one way, this study is important because it was a partial replication of a previous study. 2 Although certain aspects of the current study were modified, the researchers utilized an identical survey instrument. Fully or partially replicating previous research by using the same survey instrument can allow researchers to verify the results of previous studies and have more confidence that the findings are indeed �true� or valid, and not some aberration due to problems in sampling or otherwise.

This study is also important from a policy standpoint. Public opinion often finds its way into policy discussions regarding the will of the public toward any number of pressing criminal justice issues, such as the juvenile death penalty, life without parole for juveniles, and in the current research, whether the public is willing to pay more in taxes for rehabilitation versus incarceration. The current research has the potential to inform the public policy process regarding the desired treatment of serious juvenile offenders and whether or not the public supports funding such treatment with additional taxes.

From a methodological point of view, random digit dialing is an interesting sampling variation as used in conjunction with survey research. Random digit dialing has the potential to reach individuals that may be unknown in more traditional population lists or sampling frames, such as phone books, voter registration records, and others. Inasmuch as a phone number selected via random digit dialing serves as a proxy for a person, random digit dialing remedies the problem of unlisted phone numbers and has the ability to capture those individuals who do not have �land� lines but only cell phones, and therefore are not listed in a phone book. As a result, random digit dialing is a viable sampling variation to identify the largest possible number of individuals in the population to be sampled when telephone surveys are utilized.

In This Chapter You Will Learn

About the process of sampling and why it is important to the research process

The difference between a sample and a population

That there are two general types of samples�probability and non-probability samples

About the difference between probability and non-probability samples

About important concepts related to sampling, such as representativeness and generalizability

About basic procedures in drawing a sample

That random selection is a key component in probability samples

That the type of sampling required in a research study is highly related to the research question of interest

Introduction

The case study highlighted above provides one example of how sampling can be utilized in a research study. While not all studies require sampling, in those that do, sampling is a critical consideration in evaluating the results of the study. And when the sampling process breaks down in some way, it can seriously impact the results. It is therefore critical that research consumers have specific knowledge about sampling, including but not limited to the different types of sampling and problems that may directly or indirectly be associated with sampling. The goal of this chapter is to provide that critical insight.

Chapter 3 begins by examining several areas relevant to sampling. This section includes a focus on what sampling is and why researchers typically utilize a sample instead of an entire population. It then discusses the importance of randomness to the sampling process. Although random sampling is not always desired or needed in every research study that must utilize a form of sampling, it is a critical part of many research studies. This section then examines two additional areas relevant to sampling. These areas include the key concepts of representativeness and generalizability, and a brief discussion about sample size and sampling error.

The second section of this chapter examines different types of sampling methods known as probability sampling methods. Although probability sampling methods each have their own distinct features, the consistent link between all of them is that each member of a particular population has an equal chance at being selected for the sample. When researchers are interested in generalizing or applying the research results obtained from the sample to the larger population from which it was drawn (such as in the highlighted study beginning this chapter), probability samples are superior.

Chapter 3 then explores non-probability sampling methods. As opposed to probability sampling methods, non-probability sampling methods do not ensure that every member of a particular population has an equal chance at being selected for the sample. There are various situations in which a non-probability sample may be utilized and be appropriate for a particular research study. Although the various non-probability samples are unique and useful in their own way, the consistent theme among non-probability samples is that the results produced from studies utilizing this form of sampling do not generalize to a larger population. This is because not everyone in the larger population had an equal chance at being selected for the research study.

CLASSICS IN CJ RESEARCH

A Snowball�s Chance in Hell: Doing Fieldwork with Active Residential Burglars 3

The general methodology for this study was to interview active residential burglars about their criminal careers (e.g., number of burglaries, age at first burglary). A main goal of this study was also to shed light on the process of researching active criminals�locating active offenders, obtaining their cooperation, and maintaining an ongoing relationship throughout the study period.

Perhaps the most interesting part of the study was the process of locating active offenders to interview. Unlike prison inmates or police officers or other known populations, there is no list of active offenders, replete with phone numbers and addresses. For this study, the researchers located their sample members by utilizing a form of non-probability sampling normally employed to contact research participants who are not readily known or otherwise absent from a convenient sampling frame. This type of non-probability sampling is called snowball sampling. Procedurally, to facilitate the sample of active residential burglars, the researchers first hired an ex-offender with ties to the criminal world. The ex-offender first approached known criminal associates. The ex-offender then contacted several law-abiding but street-smart friends, explaining that the research was confidential and no police involvement would occur. The ex-offender also explained to the contacts that individuals who took part in the study would be paid a small sum of money.

Over time, the criminal (e.g., low level fence; small time criminal, crack addict) and noncriminal contacts (e.g., youth worker) recruited by the ex-offender were able to identify and make contact with several active residential burglars. Upon their participation, these burglars also referred other residential burglars. In essence, the sample of active residential burglars snowballed through this sort of referral process that started with one ex-offender. All in all, 105 active residential burglars participated in the study.

One goal of the research was to shed light on the offending careers of the residential burglars. Based on their interview questions, the researchers found that the active residential burglars averaged 10 or fewer burglaries a year over the course of their offending careers. They also found extremes among this average. For example, the researchers uncovered a group of extreme offenders, roughly 7% of the sample, who committed in excess of 50 burglaries per year.

Another key finding from this study linked to the arrest patterns of the active burglars. Although most members of the burglar sample had previously been arrested, the researchers did uncover a subgroup of burglars who had not been previously arrested but who had committed a large amount of residential burglaries. Among other things, the results revealed a number of criminals who were not only quite successful in their residential burglary careers, but also successful at avoiding official detection.

Beyond the specific findings relative to the offending patterns of active residential burglars, it is important to note that the qualitative nature of this study also produced important insight. For example, through the process of snowball sampling, the researchers explored ways in which to successfully locate, contact, and recruit hard-to-access active criminal populations. The researchers also explored the difficulty of working with active criminals, developing trusting relationships, and in general, gathering data in ways that are relatively �extreme� compared to other approaches.

Because this was a qualitative research study, an argument could be made that the results may not generalize to all active residential burglars. Indeed, because the sample of burglars was obtained via snowball sampling, a non-probability technique, there is no way to guarantee that the 105 burglars were �representative� of all residential burglars. This is because the sample was not randomly drawn from a larger population. As mentioned, however, it must be considered that no easy or complete list or sampling frame of active residential burglars is in existence. The very nature of this hard-to-access population virtually excludes all other sampling methods in efforts to understand the offending careers of active residential burglars.

The researchers also note some potential limitations. One limitation centered on defining eligible members for the sample. The researchers limited their sample to individuals who were residential burglars and who were currently active, meaning they had committed a residential burglary in the past two weeks. While these sample inclusion criteria appear simple, the researchers note that �in the field� sometimes the burglars were evasive about their activities. To verify their eligibility for the study, the researchers had to rely on confirmation by other burglars. On a broad level then, the limitation associated with this study is one that can be levied at any study where questions are asked of individuals�the ability to trust the responses of others.

This study impacted criminal justice in that it represented one of very few qualitative research studies in criminal justice. As noted by the authors, many criminologists at the time shied away (and perhaps still do) from this type of research based on the belief that it was impractical. Importantly, the authors showed through a unique sampling scheme that this research can be conducted on a practical basis. This research may have also spurred others to conduct qualitative research in criminal justice settings and with other less-researched criminal justice populations:

This research was also only one of a handful of studies that contacted, recruited, and fostered the cooperation of active criminals, as opposed to known criminals such as confined prisoners. Gaining the trust of active criminal populations is extremely difficult because these groups are often highly suspicious of outsiders. The researchers in this case were able to garner the trust of 105 active offenders and interview them about the frequency of their criminality. Their research uncovered a number of important insights about the active criminal. For example, the researchers uncovered a subset of extremely active and successful burglars adept at avoiding apprehension by criminal justice authorities. In other words, there are some criminals for which that old credence �crime pays� rings true. The finding also sheds some light on the notion that official estimates of crime, for example, may significantly underestimate the true level of crime.

What is Sampling?

Generally, sampling refers to a process of selecting a smaller group from a larger group �in the hope that studying this smaller group (the sample) will reveal important things about the larger group (the population).� 4 In some forms of sampling, such as probability sampling, the goal is that the smaller sample is representative of the larger population. For example, officials at your university might select a random sample of criminal justice students and ask their opinion on whether students should have the right to carry weapons on campus. In selecting a sample randomly, university officials� goal is that the results obtained from the sample of students would be similar to the results obtained if all criminal justice students (the population) were asked their opinion on this topic. If the randomly drawn sample is representative of the population, the opinion results obtained from the sample of criminal justice students can then inform about the opinions of the entire population.

It is noteworthy to consider that sampling is certainly not limited to the social sciences. Indeed, smaller subsets of larger populations are taken in any number of scientific contexts so that researchers might learn things about some larger population. Consider environmental researchers who take core samples from glaciers in Antarctica. Environmental researchers drill deep into the glaciers with hollowed tubes to take core samples of snow and ice that have been compressed over many years. The resulting core samples are then analyzed to gather data in such areas as temperature change and atmospheric conditions over the age of the frozen core samples. Or consider beer brew masters. Brew masters also engage in a process where once a beer batch has fermented and is processed, they take a sample from the massive swirling vats of beer to determine whether the smaller sample (small glass of beer) passes muster. In essence, the small glass of beer serves as a sample of knowledge about how the larger vat of beer (or the population) might taste. The examples above also demonstrate that samples and populations need not be animate objects�samples can be produced from any number of different populations.

In both instances above, researchers as drillers or brew masters are interested in getting a smaller but representative sample of a larger population. Because these researchers are concerned with representativeness, their techniques are in ways variants of probability sampling. For example, the brew master wants to be able to generalize or apply the results of the sample beer to the larger vat of beer. Provided the sample was drawn in a way that makes it representative of the larger vat of beer, such a process negates the need for the brew master to drink hundreds and hundreds of gallons of beer to determine the quality of the batch! In many forms of sampling, especially forms of probability sampling, the nature of the sampling process allows the researcher to take a smaller but representative sample of a larger population of individuals and retrieve results that would be similar as if he or she had utilized the larger population.

Uses of Sampling

Sampling methods can be used in any number of the different research designs that are discussed in Chapters 4�6 of this text. For example, sampling can first be used to retrieve a small representative subset of individuals from a larger population. These individuals might then be randomly assigned to experimental and control groups in an experimental design as discussed in Chapter 5. Sampling can also be used as a tool to select a smaller but representative portion of individuals from a larger population for the administration of surveys, whether they are telephone, Internet, face-to-face, or mail surveys. Sampling can also be used in qualitative research as covered in Chapter 6. However, the nature of qualitative research lends itself best to non-probability sampling methods.

The bottom line is that sampling is utilized in a number of different research designs. Although the type of sampling used will vary depending on the goal of the study, sampling does have a place in many research studies in and beyond the social sciences. It is also important to understand that the nature of the research will determine whether the type of sampling required is probability- or non-probability-based sampling. This should become clearer in the sections that examine different types of probability and non-probability samples later in this chapter.

Why Sample?

We�ve hinted at the fact that utilizing a sample can lead to research results that would be similar to results if researchers instead examined the entire population. This is certainly a justification for sampling since it is generally easier and less tedious than utilizing an entire population. For example, consider the study highlighted at the beginning of the chapter where researchers were interested in citizen preferences for rehabilitation versus incarceration for serious juvenile offenders in four different states (Pennsylvania, Washington, Illinois, and Louisiana). The combined population of these four states is just over 35 million individuals. Instead of drawing a sample, suppose the researchers wished to conduct telephone interviews with the entire population of eligible phone numbers in each of these four states. The number of telephone surveys to be conducted among the eligible and participating population would be prohibitive in a number of areas�time, expense, staff needs, and length of time required to complete, analyze, and report results. In short, it would simply not be feasible for a small research team to conduct such a study with an entire population.

The good news is that sampling, under certain conditions, allows researchers to retrieve results from a sample that are similar to the results that would have been obtained by utilizing the entire population. Although there will be some degree of difference between the results produced from a sample compared to an entire population (called sampling error), this error can be estimated and considered. In short, taking a smaller representative sample of a larger population is often as sufficient as surveying or otherwise utilizing the entire population in a research study.

Representativeness and Generalizability

Two foundations of sampling are representativeness and generalizability. This is particularly true when researchers utilize probability sampling methods, because a major goal of probability sampling is that the sample is representative of the larger population. Representativeness is achieved when the sample provides an accurate picture of the larger population. And if the sample represents the larger population, the results from the sample can then be used to make generalizations about the larger population.

Consider a hypothetical population of criminal justice students at a large university. Let�s say the criminal justice student population comprises 2,500 students, half males and half females. Suppose researchers randomly sampled approximately 500 criminal justice students, and 85% of the sample turned out to be males and only 15% females. Based only on gender, it is clear that this sample does not accurately represent the larger population of criminal justice students. Therefore, any results produced from the 500-person criminal justice sample cannot accurately be generalized back to the larger population. In fact, the results produced were almost entirely responses from males. The results may well represent the population of male criminal justice students at the university, but the results would not generalize to all criminal justice students in the population. In sum, samples can only be generalized back to what they represent�in this case, male criminal justice students and not all criminal justice students at the university.

The previous discussion brings up the issue of generalizing to a specific population and that of generalizing results beyond a particular population. If a sample is representative of a specific population, researchers can be confident that the results of a study generalize or apply back to the specific population from which they selected their sample. For example, if the sample of 500 criminal justice students above accurately represented the criminal justice student population by gender, we might say that any results produced from surveying the 500 criminal justice students reflects the results that would have been found by gender if the whole population of 2,500 criminal justice students was surveyed.

Generalizing results from a representative sample to a specific population does not mean that the results automatically generalize to all similar populations. For example, the opinions on carrying personal weapons on campus from a representative sample of criminal justice students at one Texas university may represent well the opinions of all criminal justice students on that campus. But their opinions may be much different from those of students at a university in Iowa, or criminal justice students in Norway. Perhaps the bottom line is that consumers must be attuned to notions of representativeness and generalizability and must be very cautious of research findings that purport to generalize well beyond the specific population and sample utilized in a research study. Only through replication with different samples from varying populations can more confidence be attached to such broad claims of generalizability between different populations.

RESEARCH IN THE NEWS

�CDC Surveys Irk Citizens�

Each year, dozens of national, state, and local agencies enter into agreements with various contractors to conduct telephone surveys that address a number of issues. Apparently, however, the method employed by the calling contractors of being �polite but persistent� is enough to make some respondents boil over with anger. Recently, the Centers for Disease Control (CDC), and their contractor, the National Opinion Research Center (NORC), have drawn the ire of several citizens. According to one news article, those contacted by NORC on behalf of CDC via random digit dialing have slammed down phones, blown boat horns into the receiver, and cursed profusely in response to what they feel are aggressive, untimely, and repeated calls to participate in surveys.

The CDC says that citizen complaints are rare among the 1 million or more telephone calls and 100,000 interviews that NORC conducts each year on their behalf. There is even a website that tracks complaints about NORC, www.800notes.com , complaints that clearly show the annoyance of many citizens. Some respondents have gone to great lengths to show their displeasure with the continued calls. One respondent, for ex ample, explained in a post that she was going to provide false information and tie up employees by talking about her day.

Although respondents may register their number with DO NOT CALL registries, government researchers and surveyors are exempt from having to acknowledge the list. And despite the fact that individuals do not have to answer survey questions, it appears that such a denial is not enough to stop some surveyors.

1. Visit the website www.800notes.com and view some of the comments posted. What are your feelings toward repeated calls from a research or survey organization that identified your phone number via random digit dialing?

2. Based on what you know about human subjects� consent and research participant rights, what are your feelings on repeated �cold calls� based on random digit dialing?

Adapted from JoNel Aleccia, Dial it down: Pesky CDC callers incite fury. Retrieved on May 12, 2011, at http://www.msnbc.com .

Sampling Error and Sample Size

Inasmuch as the results from a probability sample are meant to be a close approximation of what would actually be found if an entire population were utilized, there is certain to be some degree of difference in the results produced from a sample compared to a population. For example, survey results from a sample of citizens on attitudes toward rehabilitation versus incarceration will not likely be exactly identical to the overall survey results if an entire state population of citizens was surveyed.

The difference in results or outcomes between a sample and a population is called sampling error. Researchers expect there to be a difference between the sample results and the results from an entire population, even when the sample is representative of the population. The good news is that this margin of error can be estimated and considered in research. For an example, go watch any major news program and be attuned to survey results from national surveys. During election time, for example, major news networks broadcast any number of survey or poll results from random samples of U.S. citizens, often called scientific surveys to denote the samples were randomly chosen, and hence, probability samples of some sort. Results from such polls are usually graphically displayed with bar or pie charts and show the percent of Americans in favor or opposed to a particular candidate or issue. Results are usually accompanied by an indicator, such as �margin of error +/� 3%� or some other variation. Such an indicator means that results can vary up or down three percentage points. For example, suppose a survey of randomly selected citizens revealed a presidential approval rating of 47% with a margin of error +/� 3%. Considering the error or difference produced by utilizing a sample instead of an entire population (+/� 3%), the approval could be as high as 50% or as low as 44%. These statistics noted above are an indicator of sampling error, or the expected difference in results produced by sampling versus surveying the entire U.S. population. In short, we know that there will be some degree of error by using a sample; the major question is how much error. Sampling error gives us that indication.

One of the most important factors related to the degree of sampling error is sample size. A general rule is the larger the sample, the lower the sampling error. This is because as the sample gets larger, it more closely approximates the population, and therefore error or difference between the sample and population decreases. When the sample is equal to the population, the error is zero, because the sample is the population! Conversely, very small samples are less representative of the population, results are less generalizable to the population as a whole, and sampling error is greater. Of course, one must consider that even if an entire population was selected to participate in a survey, some eligible participants would not respond, other persons in the population would be unable to be reached or would be unknown (e.g., homeless individuals), and these issues are relevant to consider in discussions of sample versus population, and sampling error. But as a very general rule, the larger the sample, the less sampling error.

Students often wonder about the appropriate sample size for a particular research study. Based on the previous discussion, it would seem that the larger the sample, the better. This is generally true when considering the notion of sampling error. However, constraints of the research process�high costs, staffing, tight deadlines�might mean that a larger sample is not feasible. Study constraints notwithstanding, there is no clear-cut rule concerning what constitutes the appropriate sample size. Sample size depends on a number of considerations: size of the population, how much variability exists in the population, and demands of certain statistical techniques, among others.

Consider the issue of population variability. Instead of a social science survey, consider how large a sample of the world population we might need to take to determine what a human heart looks like. There are variations in human hearts to be sure based on age and lifestyle and many other considerations, but there is no need to cut into hundreds of people to come to a conclusion of what a human heart looks like. This is because when it comes to human hearts, there is not a lot of variability in the population. Very small samples in this example would suffice and would be representative of the population and therefore generalizable to the population as a whole. The sample size situation is different when we want to ask people their opinions on any number of issues�people who are spread across different cultures and geographies and who each have unique influences and life experiences. As opposed to a human heart, a larger sample is needed because there is much more diversity in the population.

The bottom line is that sample size is less important than obtaining a representative sample. An extremely large unrepresentative sample is much less useful than a more modest sample size that is representative of a particular population. In this way, samples are akin to gifts�bigger is not always better!

Probability Sampling Methods

The key feature that makes probability sampling methods different from non-probability sampling methods lies in how the sample is selected. In probability sampling methods, selection of the sample is accomplished through a random process such that every member of the population has an equal chance at selection for the sample. To ensure that every member of the population has an equal chance at selection, probability sampling techniques require a random and unbiased process for selection. Researchers must have access to a complete listing of the population, also called a sampling frame. In many cases, researchers might not have access to a list of the population. Researchers might also not have the resources, need, or motivation to utilize a complete list of the population, even if it were available. In these cases, non-probability samples are utilized. Such samples are not comprised of individuals with an equal chance at being selected for the sample.

Before delving into the various probability and non-probability sampling techniques, it is important to briefly revisit the notion of representativeness and generalizability. Researchers who are interested in generalizing sample results to a larger population must ensure that the sample is not biased and is an adequate representation of the population. Accomplishing representativeness, and hence generalizability, is the province of probability sampling techniques. The four probability sampling techniques are examined below.

Simple Random Sampling

Simple random samples are simply samples drawn randomly from a larger population. The key to selecting a simple random sample is that every member of the population has an equal chance at being selected for the sample�no one individual or group of individuals has a greater or lesser chance of getting selected than another individual or group of individuals.

The procedure for drawing a simple random sample is relatively straightforward, as with other forms of probability sampling (see Figure 3.1). First, the researcher must identify the target population from where the sample will be drawn. Selecting a target population is obviously driven by the aim of a particular research study. For example, if a researcher wishes to elicit the opinions of undergraduate criminal justice students at a large southern university, the population would be all undergraduate criminal justice students enrolled at the university. In another example, if the goal is to elicit the opinions of residents in Dade County, Florida, the population would be residents of Dade County, Florida.

Once the target population is identified, the researcher must obtain a listing of the population. Obtaining a listing of the population is one of the more difficult, yet crucial, parts of probability sampling. This list of the population is often called a sampling frame. Because a sampling frame is a list of the population, sampling frames come in many forms. For example, general sampling frames might include phone books, voter registration records, census contacts, or records from the department of motor vehicles. Each of these sampling frames includes members of a certain population, for example, residents of a city or county or other regional indicator. If a researcher wished to survey undergraduate criminal justice students for their opinions on carrying weapons on campus, the sampling frame would be a listing of all criminal justice students�by name or student number or some other indicator.

FIGURE 3.1 | Simple Random Sampling

One crucial consideration involved in the use of a sampling frame is that it be a complete listing of the population. Sampling frames that do not include all members of a target population are problematic. In cases where the sampling frame is incomplete in some way, any samples drawn from the sampling frame may not be truly representative of the population. For example, if a researcher used a phone book as a sampling frame of county residents, there is likely to be a substantial number of members from the population missing because not all residents have phones. In these cases, the true representativeness of the sample could be called into question, and hence, the generalizability of results produced from the sample. Conversely, a target population and sampling frame of all enrolled undergraduate criminal justice students at a particular university is likely to be complete. Nonetheless, an important step in selecting a simple random sample, and all probability samples, is the presence of a complete sampling frame.

Once a sampling frame is identified, the process of selecting a simple random sample requires that members of the population be selected in a way that each member has an equal chance of selection. In essence, members of the population must be selected randomly. There are a number of different ways to draw a random sample�flipping a coin, rolling a die, or using a lottery type machine. Perhaps the most common way of randomly selecting a sample from a population is through the use of a computer program. A variety of statistical software packages exist (e.g., Statistical Package for the Social Sciences [SPSS]) that will randomly draw a sample from an identified list of the population. Note, for example, that the case study that began this chapter was a form of random selection via computer� random digit dialing. As opposed to using a phone book, however, the researchers utilized a computer program that randomly dialed the last four digits of phone numbers in the area codes among the specified states. Such a process means that every member of the population had an equal chance of having their phone number dialed. Unfortunately, those without a phone number could not be considered for the sample.

Whether members of a sample are selected by computer or some other random selection procedure, what can be ensured is that each member of the sample had an equal chance at selection. One problem with simple random samples, however, is that despite being randomly drawn, it cannot be ensured that the sample is representative of the population. In short, just because everyone in the population had an equal chance at being selected does not mean the sample automatically will represent the population. It is possible, for example, that by a chance occurrence the sample could be highly unrepresentative of the population. Consider, for example, the flipping of a coin. It is possible that flipping a regular coin over 100 times could result in the coin landing on heads 100 times, or 85 times, or 75 times�well beyond the 50 times we would expect by probability. Such an imbalance could occur simply by chance. The same problem could occur with simple random sampling. A population of 500 that included 50% males and 50% females could, by chance, result in a simple random sample with highly imbalanced proportions of one gender or the other. For example, a 200-person sample from this population that included 75% males would not be representative of the population�but this could occur, by chance.

In sum, simple random samples ensure that each member has an equal chance at being selected, but such samples do not guarantee representativeness of the population on known categories of information among the population (e.g., race, gender, age). Provided researchers have information on the population, it is possible to examine whether the sample indeed is an accurate reflection of the population. This is only possible on information to which the researchers are privy, however. For example, if researchers did not know the gender breakdown of the entire population, they would not be able to examine whether the sample is truly representative of the population. Because of the potential chance occurrence of nonrepresentativeness posed by simple random samples, researchers might choose to utilize a stratified random sampling technique.

Stratified Random Sampling

Stratified random sampling is quite similar to simple random sampling (see Figure 3.2 below). The major difference in a stratified sample versus a simple random sample is that the sampling frame is divided up into different strata, based on characteristics of the population. From there, smaller random samples are taken from each strata and then combined into a singular sample. This technique ensures that the final sample is representative of the population based on certain characteristics such as age, race, gender, or whatever is of interest in the research study.

Suppose we wished to conduct a survey on the alcohol drinking behaviors of undergraduates at your college. Let�s say we are most interested in determining whether alcohol consumption differs based upon credit-hour classification: freshman, sophomore, junior, and senior. The population of the college is 4,000 individuals, and we want to take a sample of 100 persons. Based on our knowledge of the population, we know the proportion breakdowns of each classification: freshman (20%), sophomore (25%), junior (25%), and senior (30%). To ensure that our sample of 100 is representative of the population, we first must divide the sampling frame (a list of the student population) into four different strata consistent with the classifications for which we are interested. In essence, we are taking a list of the population of students (the sampling frame), and breaking up this larger sampling frame into four different sampling frames (freshman, sophomore, junior, and senior) to represent each classification. Once we have four sampling frames corresponding to all freshman, sophomores, juniors, and seniors, we then take a random sample from each of the four sampling frames. The size of the random sample from each sampling frame is proportionate to each classification�s proportion of the population. For example, in our desired sample of 100 students, 20 freshman will be randomly selected from the freshman sampling frame, 25 sophomores will be randomly selected from the sophomore sampling frame, 25 juniors, and 30 seniors. This will result in a sample of 100 students, with each classification represented in the sample exactly to their proportion in the population.

FIGURE 3. 2 | Stratified Random Sampling (Proportionate)

The process above is an example of proportionate stratified sampling. In proportionate stratified sampling, each predetermined category of the sample (in this case freshman to senior) is represented in the sample exactly proportionate to their percentage of the population. For example, freshman make up 20% of the population, and likewise make up 20% of the final sample. The example above suggests that samples can be stratified based on any number of factors for which researchers have information about the population. For example, the sample could have been stratified by gender and credit-hour classification. To do this, the sampling frame of the entire population would be broken down into multiple sampling frames consistent with gender and classification: freshman women, freshman men, sophomore women, sophomore men, and so on. From there, the sample would simply be randomly selected from each strata, and the number of members in the sample from each strata would be proportionate to their existence in the population. For example, if freshman women make up 10% of the population, and we wish to take a final sample of 100 across gender and classification, 10 freshman women, or 10%, would be included in the final sample. At its essence, stratified sampling is a method researchers use to break down the population into particular sampling frames, and then take a random sample from each sampling frame to create a sample that is perfectly representative of the population (at least on the strata).

As a final note, sometimes researchers are interested in taking a sample from a larger population where one group or strata is overrepresented compared to the group�s proportion of the population. Of course, this smacks against all that has been learned thus far about representativeness. Indeed, in these situations, researchers are actually taking an unrepresentative sample of the population, and they do so on purpose. In some cases researchers do this when a group or strata of interest is so small that drawing a sample proportionate to the group�s membership in the population would result in a sample that is relatively meaningless if comparisons were to be made among other groups. For example, in the hypothetical study on alcohol consumption by credit-hour classification, suppose that freshmen made up only 1% of the population of 4,000 students, sophomores equaled 33%, juniors equaled 33%, and seniors equaled 33%. Among 4,000 students, this would equal only 40 students as the population of freshman. If the population was stratified by classification, and samples proportionate to the population were drawn from those strata, in a 100-person sample, only 1 freshman would be selected. While this 1-person sample would technically represent the proportion of freshmen in the population, this one person would not likely be representative of all 40 freshmen in the population relative to alcohol consumption. What if this one freshman, for example, drank a case of beer a day! This would probably not be an accurate representation of the freshman class. To correct for such extreme imbalances, researchers may oversample the small group, also considered disproportionate stratified sampling. Although it sounds counterintuitive, in some situations researchers must sample in a way that is unrepresentative to ensure adequate representation of a particular group in a sample.

WHAT RESEARCH SHOWS: IMPACTING CRIMINAL JUSTICE OPERATIONS

The Impact of Prison Rape Research

In 2003, the Prison Rape Elimination Act (PREA) was signed into law and became the first federal law dealing with sexual victimization in prisons and other confinement facilities. Spurred by PREA, dozens of research studies have been conducted over the last several years, addressing a variety of topics related to sexual victimization in prisons. For the most part, these studies have employed self-report surveys and examinations of official data collected by correctional agencies. Among other goals, the intent of such research is to understand the nature and extent of sexual victimization in prison with the goal of decreasing this form of violence behind bars. And because of the insight provided by these research studies, correctional agencies have developed or are developing a number of methods to help decrease the sexual victimization of prisoners and are having a substantial impact on correctional system operation.

One of the best sources of information on the strategies used by correctional agencies to address prison sexual victimization following PREA comes from the Urban Institute. The study was meant to provide a national-level picture of what is being done to address prison sexual victimization following PREA, and also to identify specific practices that appear promising in addressing this problem. To assess what correctional agencies are doing in the aftermath of PREA, Urban Institute researchers surveyed state correctional administrators, conducted phone interviews with 58 department of corrections representatives, and conducted case studies in 11 different states.

Overall, results from the Urban Institute study revealed that correctional agencies are responding to PREA�s call to identify and help reduce sexual victimization in prisons. Their study identified several new or developing policies, including enhanced data collection efforts to understand the extent of sexual victimization in prison, prevention efforts that include the hiring of special staff to deal with inmate reports of sexual victimization, and educational efforts for inmates on how to prevent sexual victimization, among others.

One example highlighted was the Texas prison system�s �Safe Prisons Program.� Developed following PREA, the Safe Prisons Program was created to address sexual victimization and other forms of violence and disorder in Texas prisons and includes components of data analysis, incident monitoring, staff training, and policy development. The program also includes a database to track perpetrators and victims of violence. Moreover, a special prosecution unit was developed to ease the burden on the local district attorney from prosecuting crimes that occurred in prison.

The research by the Urban Institute, and others, has not only shed light on a significant problem in correctional environments but has also spurred the development of significant correctional policy to help tackle this problem.

Zweig, J., Naser, R., Blackmore, J., & Schaffer, M. (2006). Addressing sexual violence in prisons: A national snapshot of approaches and highlights of innovative strategies. Washington, D.C.: Urban institute.

Systematic Random Sampling

Systematic random sampling is another form of probability sampling. Like simple random and stratified random samples, systematic sampling utilizes a random process in the selection of the sample (see Figure 3.3 below). Systematic sampling involves a few basic, but important, steps to ensure that each member of the sample has an equal chance at being selected.

Systematic sampling begins with some sort of list or grouping, and members or items on the list or in the grouping are selected in intervals. The interval is often referred to as taking �every n th individual.� This means that one portion of selecting the sample entails taking every 5 th , or 6 th , or some other � n th �.

Consider taking a 50-person sample from a 100-person criminal justice class. In this scenario, the professor likely has a list of student names or student ID numbers. Or, the professor could simply line up all the students in front of the class (we are assuming all, of course, are in attendance for a complete population of the class).

Utilizing the student lineup, the professor has to first calculate the sampling interval, or n th value. To determine this interval, the professor simply divides the population (100) by the number of individuals desired in the sample (50). In this example, the sampling interval is 2 (100/50 = 2). This means every 2 nd person will be selected.

FIGURE 3.3 | Systematic Sampling

The next step to a systematic sample is critical and is what makes it a probability sample. Instead of automatically starting at the top of the list (or front of the lineup), and picking every 2 nd person, the professor must begin with a random starting point. A traditional way to pick a random starting point is to take all of the numbers involved in the interval (1 or 2 in this example), and randomly pick one of the numbers. If the professor picks 1, he or she will start at #1 in the lineup and then take every 2 nd person�1, 3, 5, 7, 9, and so on. If the professor picks 2, he or she will start at #2 and take every second person in the student lineup�2, 4, 6, 8, 10, and so on. An alternative method would be to take 100 numbers, select a number, and then proceed by taking every second person. For example, if the number 6 were chosen, the sample would consist of 6, 8, 10, 12, and so on. In each example, the outcome is essentially the same. In this latter example, once the professor reaches the end of the student lineup, he or she could simply continue selecting every 2 nd person by starting at the beginning of the student lineup until the 50-person sample has been achieved.

By using a random start, the professor ensures that each member of the class population has an equal chance at being selected for the sample. But to further ensure that systematic sampling results in an equal probability of selection, the professor must be sure that the student lineup is not sorted in any particular way that might lead to bias. For example, if the professor sorted the students in such a way that all even-numbered students had the highest class grades and odd-numbered students had the lowest class grades, a resulting sample might be highly biased and not representative of the class as a whole. Such is the case in any form of systematic sampling procedure�it must be ensured that the elements to be sampled are randomly arranged, and do not follow a particular pattern.

Cluster/Multistage Random Sampling

It is sometimes the case that researchers wish to take a sample of individuals dispersed across wide geographical areas. For example, suppose a researcher wanted to conduct a paper-and-pencil survey with a representative sample of 5,000 prison inmates across the sprawling state of Texas. Texas incarcerates more than 150,000 inmates spread across more than 100 incarceration facilities. Because Texas is so large and prison inmates are dispersed all over the state, the thought of drawing a representative sample is a daunting task.

Cluster sampling is a way to narrow down the process of sampling to help ensure that samples are representative of the large population of focus. In this way, cluster sampling first begins by narrowing down large geographic areas�whether they are states, census tracts, or any other large area�into more manageable parts. From there, a series of random samples are drawn of different units, for example, randomly selected prison facilities, randomly selected housing areas within those prison facilities, and finally, randomly selected inmates from the housing areas. The series of random samples implicates the multistage part of cluster/multistage sampling�multiple random samples (see Figure 3.4 below).

Operationally, the first step in the hypothetical prison inmates study would be to narrow down the state of Texas by breaking it down into manageable clusters. Although methods vary, perhaps the state of Texas could be broken down into areas based upon the regions in which prison facilities are located located (or N, S, E, and W as in Figure 3.4). For example, the Texas Department of Criminal Justice is divided into six regions. This could be an initial clustering of the state of Texas. Next, the researcher might obtain a listing of all prison facilities located within each of the six regions. This list serves as a sort of sampling frame. From there, the researcher may choose to draw a simple random sample of five prison facilities in each region, for a total of 30 prison facilities across the state. Note that instead of a simple random sample, the researcher could have stratified the list of prison facilities by any number of measures, such as size, type of inmate population, and so on.

Once the 30 prison facilities are randomly selected, the researcher continues to select random samples. For example, the researcher might obtain a list of all inmates at each of the 30 prison facilities. Once the sampling frame of each facility is obtained, the researcher then selects a random sample of inmates from each facility. In this hypothetical study, this would equal approximately 167 inmates sampled from each of the 30 randomly selected prison facilities for a total of approximately 5,000 inmates.

FIGURE 3.4 | Cluster/Multistage Sampling

As before, any number of steps could be added to the process above. For example, separate housing areas could be randomly sampled within each of the 30 prison facilities. Then, inmates could be randomly chosen from the sampled housing areas. Still further, in each stage, proportionate or disproportionate stratification could occur to help make the sample as representative of the population as possible. As can be seen, cluster/multistage sampling can become tedious. In reality, however, cluster/multistage sampling can be boiled down to the successive drawing of random samples from populations that are large and widely dispersed.

Non-Probability Sampling Methods

As opposed to probability sampling techniques, non-probability samples are not drawn through a random and unbiased procedure. There are many potential reasons that might preclude utilizing a random sample. A main reason is that a ready-made list of the population simply may not be available. For example, suppose a researcher was interested in studying the subculture of hoboes. Although the town of Britt, Iowa, holds the National Hobo Convention each year, there is little in the way of a complete list of hoboes. Moreover, even if Britt kept a list of all hoboes who attend the National Convention, this list would certainly not be complete and capture all hoboes in America. In this situation, the researcher may only have access to a defined number of hoboes. As previously mentioned, a research team may not have the resources, need, or motivation to utilize a complete list of the population, even if it was available. In some cases, those who utilize non-probability sampling techniques are actually interested in a sample that does not necessarily represent some larger population. In these cases, non-probability samples are utilized. Such samples are comprised of individuals from known or unknown populations who did not have an equal chance at being selected for the sample.

At this juncture it is important to note the potential issues faced when members of a particular sample are selected through non-probability sampling methods. Regardless of the reasons for selecting a non-probability sample, the important point to consider is that the resulting sample is likely not representative of a larger population. Absent representativeness, the results generated from a non-probability sample cannot be generalized to a larger population. An examination of different non-probability samples may make it clearer why a researcher might want to utilize these sampling techniques, as opposed to a probability sampling technique.

Convenience Sampling

Perhaps the most basic of all sampling techniques is convenience sampling (also called accidental or haphazard or person-on-the-street sampling). With convenience sampling, individuals in the sample are chosen based on convenience. In this way, it is a form of first-come, first-serve sampling.

Convenience sampling is perhaps the most common form of sampling consumed by the average citizen. Local news casts, for example, that stop people on the street and ask their opinion on any number of topics are typically convenience samples. The advent of the Internet has made surveys based on convenience sampling ever-present. Go visit any 10 websites from sporting websites to governmental research organizations to magazine websites and you are sure to have abundant opportunities to take a survey on any pressing issue. These surveys may come in the form of more aggressive pop-ups, or more passive enticements to complete a survey. Regardless, all of these forms of gathering data are based on convenience sampling�anyone can respond, and usually, multiple times.

The obvious problem with convenience sampling is that it is likely the sample is not representative of the larger population. This does not mean that convenience sampling is not useful. However, if the sample does not represent the population, the results from the sample cannot be generalized to the larger population. This is the critical piece of knowledge that should be understood by research consumers. In many cases, results generated from convenience samples are portrayed to represent the attitudes, opinions, and perspectives of the larger population. This is erroneous. Survey results from a convenience sample, in reality, only represent and hence are generalizable to the sample. For example, suppose a local news crew was on your campus today and stopped 100 students on their way to class to ask their opinions regarding whether students should be able to carry concealed handguns on campus. Suppose the local news crew revealed that 90% of students they surveyed believed that concealed handguns should be allowed on campus. What if 90 out of 100 students who believed guns should be allowed on campus just exited an organizational meeting of a group whose members� sole purpose is to promote the carrying of weapons on campus. The result from this survey would surely represent the feelings of the convenience sample, but the results might be completely different from students on the campus as a whole if they were instead selected randomly.

Convenience sampling has its uses in the research process. However, results generated from a convenience sample are not likely generalizable to the larger population from which the sample was obtained. As a result, data produced from a convenience sample is quite limited to the specific attributes of the sample and often must be interpreted with some caution.

�Wild West Universities�

In a study of guns and gun threats on college campuses, researchers Miller, Hemenway, and Wechsler surveyed a random sample of more than 10,000 undergraduate students from 119 four-year colleges in the U.S. Utilizing a mailed questionnaire, survey questions specifically asked whether respondents possessed a working firearm at college and also whether they had been threatened with a gun at college. Results of the survey revealed that just over 4% of students had a working firearm at college and just under 2% had been threatened with a gun while at school. Interestingly, the researchers revealed that students most likely to have a gun and/or have been threatened with a gun were male, lived off campus, binge drank, and engaged in risky or aggressive behavior after drinking.

This study is interesting in and of itself, but it seems particularly relevant as state legislatures are increasingly debating the merits of allowing guns on college campuses. In Texas, for example, the state senate voted in May 2011 to allow guns on campus for individuals who have completed a state-mandated concealed handgun course. Because the bill allowing guns on college campuses is heavily favored in the Texas House, and by Governor Rick Perry, it appears that Texas college students may have the opportunity to come to class strapped, locked and loaded.

1. Based on the research findings by Miller and colleagues, do you feel comfortable with a law allowing college students to carry concealed guns on campus?

2. Take time to look up research and commentary concerning guns on college campuses. Based on your research, has this changed your opinion on allowing college students to carry concealed guns on campus?

Miller, M., Hemenway, D., & Wechsler, H. (2002). Guns and gun threats at college. Journal of American College Health, 51, 57�65

Purposive Sampling

Purposive sampling (also called judgmental sampling) is aptly named because the researcher is specifically interested in the attributes of the particular sample that was purposely chosen for its characteristics. It is also called judgmental sampling because the researcher is using his or her judgment in selecting a sample that is specific to the goal of the research. A case in point might be the selection of mock jury samples by individuals who work as jury consultants. Jury consultants may, for example, choose members of a sample based on factors such as age, income, education, or anything else that might be useful. Once a particular sample of mock jury members is chosen on these criteria, the jury consultant may present particular pieces of evidence and survey sample members on their feelings toward guilt or innocence at particular phases of evidence presentation. Such consultants might further modify certain variables, such as the method of presentation, the type of presentation, who presents the material, and any other factor so that the jury consultant can examine the impact of these changes on mock juror opinions of guilt and innocence.

In this example, the jury consultant is specifically interested in selecting mock jury members who have particular attributes. Information obtained from a mock jury sample can be used in any number of ways, particularly in voir dire proceedings in which the defense, for example, may attempt to select or strike jurors based on certain characteristics that have been found to influence juror opinions. For example, if through the mock jury trials, high-income individuals were more likely to vote for conviction of a residential burglar than individuals of low income, the jury consultant may recommend to defense lawyers to avoid selecting jurors of a high-income bracket.

No matter how purposive sampling is utilized, the goal of the individual selecting the sample is to be very purposive in selecting the particular sample needed. There is little interest in selecting a representative sample from a larger population; rather, the interest lies in selecting a specific sample that fulfills the goal of the research.

Quota Sampling

Quota sampling is quite similar to convenience sampling. The one major difference is that quota samples are based on some known characteristic of the population. For example, suppose researchers were interested in the opinions of students at a mid-size college on whether students should be allowed to conceal and carry guns on campus. Instead of simply surveying students on a first-come, first-serve basis as in convenience sampling, suppose the researchers were interested in making sure the sample at least reflected the gender composition of the population on campus. For example, at the campus of interest, the student population of 5,000 is evenly split, 50% female and 50% male. The researchers want to take a sample of 100 students. In a quota sample, the researchers will simply ensure that 50 opinion surveys are given to women, and 50 surveys are given to males�largely on a convenience basis. In short, the researchers are interested in obtaining a quota based on the gender composition of the college population.

Although quota sampling is slightly more rigorous than convenience sampling, it is not by much. Despite the fact that researchers are ensuring that the sample is reflective of the proportion of students in the population by gender or some other known characteristic, the sample is still essentially convenience based. As a result, every member of the campus population does not have an equal chance at being selected, and thus, is not representative of the larger college population. Because it is not representative, the results generated from the quota sample do not represent the college population.

Snowball Sampling

Snowball sampling is a non-probability sampling technique utilized when one is attempting to study hard-to-access populations, or more typically, populations whose members are not easily identifiable. For example, there is likely no sampling frame or list (at least not a public one) that contains members of a particular subculture from which to draw a sample. Such groups could range from gang members to those involved in an underground fight club. In a general way, snowball sampling might be considered referral sampling. Because members of a particular population may not be easily identifiable, the researcher attempts to initiate contact with one known member, and through referral, is introduced to subsequent members of the group. Through this referral process, the sample begins to snowball, or grow.

As with all non-probability samples, something to consider with snowball samples is that the end sample may not be representative of the entire population. This is because each member of the population did not have an equal chance at being selected for the sample. In many cases of snowball sampling, the researcher may ultimately only be privy to a small number of members from a larger population. Because representativeness cannot be ensured, neither can generalizability.

Noting the above, recall the many purposes of research: describe, explore, explain, apply, and evaluate. In many cases of non-probability sampling, and specifically with snowball sampling, researchers are interested in exploring a lesser known topic in hopes that future research can delve further. In this way, sometimes non-probability sampling is utilized to provide an overall explanation of a particular area�a sort of starting point on which to build future research efforts.

Chapter Summary

This chapter covered forms of probability and non-probability sampling. Probability sampling is used when the goal of a research study is to obtain an accurate representation of the population for the purposes of generalizability. Whether the population consists of students, city residents, or others, probability sampling techniques ensure that each member of the known population has an equal chance at selection. Simply ensuring that each member of a particular population has an equal chance at selection does not ensure representativeness. And, a representative sample does not ensure results from the sample will generalize to different places and times. However, probability sampling makes achieving the goals of representativeness and generalizability more likely than non-probability samples. Although probability samples are superior to non-probability samples when the goal is representativeness and generalizability, this should not be taken to mean that non-probability samples are not useful in research methods. Non-probability samples�samples in which each member of a particular population does not have an equal chance of selection�are often very useful in particular research studies.

In an overall view, both probability and non-probability sampling techniques should be viewed as a set of tools. Sometimes the right tool is a probability sampling technique, and sometimes the right tool is a non-probability sampling technique. In many cases, the tool used is highly dependent upon the research question that is being asked. Knowing which tool is appropriate to a particular research question is a good step on the path to becoming an informed consumer of research.

Critical Thinking Questions

1. What is the difference between probability and non-probability samples?

2. What are some reasons a researcher would utilize a sample instead of a population?

3. What is sampling error?

4. What is the difference between representativeness and generalizability?

5. What is more important: sample size or representativeness? Explain your thoughts.

cluster/multistage sampling: A type of probability sampling in which large geographical areas are clustered, or divided, into smaller parts. From there, random samples of individuals or groups or locations are taken in successive or multiple steps. For example, breaking a state down into regions would be a form of clustering. From there, taking a simple or stratified or systematic random sample of schools from each cluster would be one stage of sampling. A next stage of sampling might be randomly selecting students from each randomly selected school

convenience sampling: A form of non-probability sampling in which the sample is composed of persons of first contact. Also known as accidental or haphazard sampling, or person-on-the-street sampling

generalizability: In reference to sampling, refers to the ability of the sample findings to generalize or be applied to the larger population. For example, let�s say the findings of a sample survey on attitudes toward the death penalty reveal the majority of the sample is in support of the death penalty. If the sample is a good representation of the population, the results from this survey can be generalized or applied to the population

non-probability sampling methods: As opposed to probability sampling methods, non-probability sampling methods include those sampling techniques in which every member of the population does not have an equal chance at being selected for the sample

population: A population is a complete group. A population could be all students at a university, all members of a city, or all members of a church. A defining feature of a population is that it be complete

probability sampling methods: As opposed to non-probability sampling methods, probability sampling methods include those sampling techniques where every member of the population has an equal chance at being selected for the sample. Such procedures increase the probability that the sample is representative of the population, and hence, that the results produced from the sample are generalizable to the population

proportionate stratified sampling: A sampling method in which each predetermined category of the sample is represented in the sample exactly proportionate to their percentage or fraction of the total population

purposive sampling: As a non-probability sample, purposive sampling involves the researcher selecting a specific or purposeful sample based on the needs of the research. If a researcher was interested in the techniques of residential burglars, their sample would be focused only on such burglars

quota sampling: Similar to convenience sampling, quota sampling does involve taking into account a known characteristic of the population. For example, if 50% of the population is female, and the researcher wants a 100-person sample to survey, the researcher must survey exactly 50 females in a quota sample. Once the quota of 50 females is met, no other females will be surveyed

random digit dialing: A sampling process involving phone numbers where a computer randomly dials the last 4 digits of a telephone number in a given area code using a known prefix. Random digit dialing, in this way, can help remedy the problem of unlisted phone numbers or numbers for which there is no so-called phone book (e.g., cell phones)

randomly drawn sample: A sample for which each member of the population has an equal chance at being selected. Samples not drawn through a random process are those in which each member of the population does not have an equal chance at being selected for the sample

representativeness: In probability sampling processes, representativeness occurs when the smaller sample is an accurate representation of the larger population

sample: A sample is a smaller part of a population

sampling: The process of selecting a smaller group from a larger group. In probability sampling, for example, a smaller group or sample of individuals is taken from the larger group or population. The goal is that the smaller sample accurately represents the population, despite being smaller in number

sampling error: The percentage of error or difference in using a sample instead of an entire population

sampling frame: A complete list of the population that the researcher will use to take a sample. If the sampling frame does not include each member of the population, and hence is not complete, a researcher must question how those who are listed on the sampling frame differ from those who are not accounted for on the sampling frame

simple random samples: As a form of probability sampling, simple random samples are samples randomly drawn from a larger population. Although each member of the population has an equal chance at being selected for the sample, this form of sample cannot guarantee representativeness

snowball sampling: A non-probability sampling technique utilized when a researcher is attempting to study hard to access populations. It is also referred to as referral sampling. In snowball sampling a researcher makes a contact, and that contact refers another, and so on. After time, the sample snowballs or gets larger. Because there is no ready to use sampling frame for some populations (e.g., gang members), researchers must use contacts and referrals to get a sample

stratified random sampling: Stratified sampling is a form of probability sampling where several simple random samples are taken from a population that has been divided up into strata, such as age, race, gender, or any number of strata based on information about the population

systematic random sampling: Systematic random sampling involves selecting every nth person (e.g., 5th, 10th, etc.) from a list. To be considered a probability sample, the starting point on the list must be chosen at random

target population: The population of interest for a particular research study (e.g., all prison inmates, all domestic violence arrestees)

1 Piquero, A., and L. Steinberg. (2010). �Public preferences for rehabilitation versus incarceration of juvenile offenders.� Journal of Criminal Justice, 38, 1�6.

2 Nagin, D., A. S. Piquero, E. S. Scott, and L. Steinburg. (2006). �Public preferences for rehabilitation versus incarceration of juvenile offenders: Evidence from a contingent valuation survey.� Criminology and Public Policy , 5, 301�326.

3 Wright, R., S. H. Decker, A. K. Redfern, and D. L. Smith. (1992). �A snowball�s chance in hell: Doing fieldwork with active residential burglars.� Journal of Research in Crime and Delinquency, 29, 148�161.

4 Vogt, W.P. (1993). Dictionary of statistics and methodology: A nontechnical guide for the social sciences. Newbury Park, CA: Sage, p. 200.

Applied Research Methods in Criminal Justice and Criminology Copyright © 2022 by University of North Texas is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Sampling: An Overview

Table of Contents

Importance of sampling in sociological research, types of sampling.

• Challenges and Considerations in Sampling

Sampling is a critical concept in sociological research and forms the foundation for empirical investigation. It refers to the process of selecting a subset of individuals, groups, or cases from a larger population for the purpose of conducting research. Sociologists rely on sampling techniques to collect data that represent the broader social phenomena they are studying, making it essential to understand its different forms, advantages, and limitations. This article provides an overview of the major types of sampling methods, their importance, and how they contribute to sociological inquiry.

In sociological research, it is often impossible, impractical, or unnecessary to collect data from every member of the population being studied. Populations are often too large, geographically dispersed, or dynamic to make a census—a complete enumeration of all individuals—feasible. This is where sampling becomes essential. By selecting a representative subset of the population, sociologists can draw conclusions that are generalizable to the larger group.

Sampling also allows researchers to save time and resources while maintaining the validity and reliability of their findings. This efficiency is crucial, especially in large-scale studies or when working with limited budgets and time constraints. Proper sampling techniques ensure that the data collected reflects the diversity, complexity, and characteristics of the population under study. Moreover, effective sampling techniques reduce bias and improve the credibility of research findings, which are fundamental to advancing sociological knowledge.

Sampling can be broadly divided into two categories: probability sampling and non-probability sampling . Each of these categories contains various techniques, each suited for different research goals and conditions.

Probability Sampling

Probability sampling is a method that allows every member of the population to have a known, non-zero chance of being selected. This approach is often considered the gold standard in sociological research because it leads to greater representativeness and generalizability of findings. Some common forms of probability sampling include:

1. Simple Random Sampling

Simple random sampling is the most straightforward type of probability sampling. In this method, each member of the population has an equal chance of being selected. This could be achieved by randomly selecting individuals through methods such as drawing names from a hat or using a random number generator. Simple random sampling is advantageous because it reduces bias and allows for straightforward statistical analysis. However, it requires a complete list of the population, which may not always be available or practical.

2. Systematic Sampling

Systematic sampling involves selecting every nth individual from a list of the population after randomly choosing a starting point. For example, if a researcher wants to sample every 10th person from a list of 1,000 individuals, they might randomly select the 5th individual and then every 10th individual after that (15th, 25th, etc.). This method is simpler than simple random sampling and can be effective if the population list does not have a pattern that could introduce bias. However, if the list is arranged in a particular order, systematic sampling may inadvertently introduce bias.

3. Stratified Sampling

Stratified sampling involves dividing the population into different subgroups, or strata, based on a specific characteristic (e.g., gender, age, or income level) and then randomly selecting individuals from each stratum. This method ensures that specific subgroups are adequately represented in the sample. For example, if a population consists of 60% women and 40% men, a stratified sample would ensure that the sample reflects these proportions. Stratified sampling improves the precision of estimates for each subgroup and can lead to more accurate results, but it requires prior knowledge of the population structure.

4. Cluster Sampling

Cluster sampling involves dividing the population into clusters, such as geographic regions or schools, and then randomly selecting entire clusters to be part of the sample. Researchers then collect data from all individuals within the selected clusters. This method is often used when it is difficult or expensive to obtain a complete list of the population, such as in studies of rural populations or large urban areas. While cluster sampling can reduce logistical costs, it is generally less statistically efficient than other probability sampling methods because individuals within a cluster may be more similar to each other than to those in other clusters.

Non-Probability Sampling

Non-probability sampling methods do not provide every individual in the population with an equal chance of being selected. These methods are often used in qualitative research or in situations where probability sampling is impractical. While they may introduce bias, non-probability samples can still provide valuable insights, especially when the research is exploratory or seeks to understand complex social phenomena. Common non-probability sampling methods include:

1. Convenience Sampling

Convenience sampling is a method where the researcher selects individuals who are easiest to reach or who are available at the time of the study. For example, a researcher might survey students in their classroom because they are readily accessible. While this method is easy and cost-effective, it is prone to selection bias because it does not represent the broader population. The findings from a convenience sample cannot be generalized, but they can provide preliminary insights or be used in exploratory research.

2. Purposive Sampling

Purposive sampling, also known as judgmental or expert sampling, involves selecting individuals based on specific criteria or characteristics that are relevant to the research question. For example, if a study is focused on the experiences of single mothers, the researcher will intentionally select participants who fit that description. This method allows the researcher to focus on individuals who have particular knowledge or experience, making it well-suited for qualitative research. However, purposive sampling introduces subjectivity, as the researcher decides who is included, and this can limit the generalizability of the findings.

3. Snowball Sampling

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2.1 Carry out Longitudinal Studies using methods of observation to assess the changing developmental needs of children

September 6, 2024

Table of Contents

Longitudinal Studies : Observing the same group of children over time helps health and social care professionals assess their changing developmental needs and identify key milestones. Methods of Observation : Techniques like narrative observation, time sampling, event sampling, and checklists provide valuable insights into cognitive, physical, and emotional development. Data Analysis : Organising observations and identifying patterns enables professionals to document progress effectively, ensuring tailored support for each child’s unique journey. Collaboration and Ethics : Regular communication with parents and collaboration with colleagues enhance intervention strategies, while ethical considerations ensure the confidentiality and respect of children’s data.

This guide will help you answer 2.1 Carry out Longitudinal Studies using methods of observation to assess the changing developmental needs of children.

Longitudinal studies in early years education involve observing the same group of children over a period of time. This systematic approach allows you to collect data about the same subjects at multiple points. By focusing on each child’s development over weeks, months, or even years, you assess their evolving needs effectively. This method differs from one-time observational studies because it provides deeper insights into developmental trends and patterns.

Longitudinal Studies

Understanding a child’s developmental arc allows you to create tailored educational strategies. These studies help you identify key developmental milestones and alert you to any delays. Early intervention can significantly improve outcomes for children facing challenges. As a childcare worker, you can use longitudinal studies to:

• Recognise specific needs and tailor learning activities.
• Build a supportive environment aligned with each child’s pace.
• Communicate effectively with parents and carers regarding their child’s progress.

Methods of Observation

Several observational methods can help capture comprehensive longitudinal data. Some effective techniques include:

Narrative Observation

Narrative observation involves writing detailed accounts of what you observe. This method gives you an in-depth understanding of a child’s behaviour and interactions. To perform a narrative observation:

• Choose a focus: Select a specific aspect of development, such as social interaction or language acquisition.
• Observe discreetly to avoid influencing the child’s behaviour.
• Record exact details: Note the time, context, and behaviours without interpretation.
• Reflect on observations: Consider what the child’s behaviour indicates about their development.

Time Sampling

Time sampling involves observing a child at regular intervals. This method helps you to gather quantitative data on certain behaviours. For example, if you’re assessing how often a child engages in social play, you might:

• Divide an hour into 5-minute intervals.
• Record whether the child is participating in social play during each interval.
• Analyse the frequency and context of the behaviour over time.

Event Sampling

Event sampling focuses on specific behaviours or ‘events’ whenever they occur. This technique is useful for observing less frequent behaviours. For example, if you want to observe a child’s response to conflict , note:

• Every occurrence of conflict.
• The child’s immediate reactions and resolution attempts.
• Changes in their strategies over time.

Checklist and Rating Scale

Using a checklist or rating scale involves pre-defined criteria that assess specific developmental areas. These are efficient for monitoring milestones or skills, such as:

• Language development.
• Fine and gross motor skills.
• Social and emotional behaviours.

Assessing Developmental Needs

Your goal is to understand how different aspects of a child’s development change over time. Developmental areas include:

Cognitive Development

Observe how children think, explore, and solve problems. This includes both theoretical understanding and practical skills. Through longitudinal studies, assess cognitive development by:

• Observing problem-solving tasks.
• Tracking the range of vocabulary or use of language over time.
• Noting children’s attention span and memory.

Physical Development

Monitor both fine and gross motor skills. Longitudinal studies show how children gain control over their movements:

• Observe children in different physical activities.
• Record milestones such as crawling, walking, or drawing.
• Take note of their coordination and strength.

Social and Emotional Development

Children’s ability to interact with others and express their emotions is crucial:

• Observe play to assess social skills like sharing and cooperation.
• Watch for how children express feelings and manage emotions.
• Look for the ability to form friendships and relate to adults.

Data Analysis and Documentation

Once observations are collected, the next step is analysis:

• Organise your observations chronologically.
• Look for patterns and consistencies in behaviour.
• Compare progress against developmental norms.

Effective documentation is crucial:

• Maintain clear and organised records.
• Use charts or graphs to visualise progress.
• Write concise reports that highlight key findings and recommendations.

Feedback and Collaboration

Sharing insights from longitudinal studies is imperative for effective collaboration:

With Parents and Carers

• Regularly update parents about their child’s development.
• Highlight strengths and areas for development.
• Provide recommendations for supporting their child at home.

With Colleagues and Specialists

• Collaborate with other practitioners to plan targeted interventions.
• Share findings with specialists if additional assessment is needed.
• Use collective insights to refine teaching strategies.

Ethical Considerations

Conduct longitudinal studies ethically by:

• Gaining consent from parents or guardians before starting.
• Ensuring confidentiality of children’s data.
• Being aware of any biases you might have as an observer.
• Respecting cultural and familial contexts.

Challenges in Longitudinal Studies

Although valuable, longitudinal studies present challenges:

• Time-consuming nature demands consistent observation and documentation.
• Flexibility is needed to adapt study focus based on emerging data.
• Resources may be constrained, requiring strategic planning.

Longitudinal studies using observational methods are powerful tools for assessing children’s changing developmental needs. They provide invaluable insights into individual growth patterns, helping you tailor educational and caretaking strategies. Although resource-intensive, the benefits of understanding and supporting each child’s journey are immense. By effectively conducting, documenting, and analysing these studies, you ensure children receive the support they need for a successful developmental transition.

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Anti/De-icing Technologies Coupling with Active Methods

• First Online: 07 September 2024

Cite this chapter

• Xianghuang Zhou 2 ,
• Yizhou Shen 2 ,
• Zhen Wang 2 ,
• Senyun Liu 3 &

This chapter explores various active and passive anti-icing and de-icing technologies, focusing on their integration for enhanced efficiency. Active methods such as mechanical vibration, electrical pulse, and hot liquid/air techniques are detailed for their effectiveness in ice removal. Mechanical vibration uses high-frequency oscillations to weaken ice adhesion, while electrical pulse de-icing employs capacitors to generate magnetic fields that break the ice. Hot liquid/air de-icing utilizes high-temperature fluids or gases to melt ice. The chapter also discusses the coupling deicing behavior of these active methods with passive approaches, such as coatings with low solid-ice interface fracture toughness and superhydrophobic materials combined with piezoelectric and magnetic field technologies. This integrated strategy aims to improve energy efficiency and reliability, ensuring effective ice prevention and removal in various environmental conditions.

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Zhou, X., Shen, Y., Wang, Z., Liu, S., Fu, X. (2024). Anti/De-icing Technologies Coupling with Active Methods. In: Shen, Y. (eds) Icephobic Materials for Anti/De-icing Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-97-6293-4_13

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DOI : https://doi.org/10.1007/978-981-97-6293-4_13

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• Published: 05 September 2024

Comparative research on monitoring methods for nitrate nitrogen leaching in tea plantation soils

• Shenghong Zheng 1 , 2 ,
• Kang Ni 2 ,
• Hongling Chai 1 ,
• Qiuyan Ning 3 ,
• Chen Cheng 4 ,
• Huajing Kang 1 &
• Jianyun Ruan 2 , 5  

Scientific Reports volume  14 , Article number:  20747 ( 2024 ) Cite this article

Metrics details

• Plant ecology

Great concern has long been raised about nitrate leaching in cropland due to its possible environmental side effects in ground water contamination. Here we employed two common techniques to measure nitrate leaching in tea plantation soils in subtropical China. Using drainage lysimeter as a reference method, the adaptability of estimating drainage and nitrate leaching by combining the water balance equation with the suction cup technique was investigated. Results showed that the final cumulative leachate volume for the calculated and measured method was 721.43 mm and 729.92 mm respectively during the study period. However, nitrate concentration exerted great influence in the estimation of nitrate leaching from the suction cup-based method. The cumulative nitrate leaching loss from the lysimeter and suction cup-based method was 47.45 kg ha −1 and 43.58 kg ha −1 under lysimeter nitrate concentrations ranging from 7 mg L −1 to 13 mg L −1 , 156.28 kg ha −1 and 79.95 kg ha −1 under lysimeter nitrate concentrations exceeding 13 mg L −1 . Therefore, the suction cup-based method could be an alternative way of monitoring nitrate leaching loss within a range of 7–13 mg L −1 of nitrate concentrations in leachate. Besides, lower results occurred in suction cup samplers due to lack of representative samples which mainly leached via preferential flow when in strong leaching events. Thus, it is advisable to increase sampling frequency under such special conditions. The results of this experiment can serve as a reference and guidance for the application of ceramic cups in monitoring nitrogen and other nutrient-ion leaching in tea plantation soils.

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Introduction.

The intensive and extensive land-use activities associated with crops and animal production cause the most substantial anthropogenic source of nitrate, among which over-use of nitrogen fertilizer is one of the most contributing factors for nitrate pollution 1 , 2 . Compared with other crops, the tea plant (Camellia sinensis) requires an elevated nitrogen supply for the growth of tea shoots to enhance tea yield and quality 3 , 4 . The mean annual N application rate ranges from 281 to 745 kg ha −1 in the main tea production provinces in China. This means about 30% of the surveyed tea gardens applied excessive chemical fertilizers according to the current recommendation 5 . Meanwhile, higher N input levels increased concentrations of NO 3 − and NH 4 + in the 90–200 cm soil of the tea gardens, posing a high risk of N leaching loss in the tea gardens 6 . Thus, nitrate leaching from tea gardens should be of great concern for both scientists and producers.

In terms of monitoring methods for nitrate nitrogen leaching in agricultural soils, ceramic suction cup samplers and buried drainage lysimeters are the two most commonly employed techniques 7 . Ceramic suction cups are favored for their ease of installation and potential for repeated sampling at the same location 8 . They are deemed suitable for monitoring nitrate nitrogen leaching in non-structured soils 9 , 10 . However, ceramic suction cups are limited in their capacity to assess nitrate nitrogen concentrations only at specific soil depths and during particular sampling times 11 . This limitation makes it challenging to establish a comprehensive mass balance unless simultaneous quantification of soil water flux is undertaken. Additionally, characterized by low soil water retention and vulnerability to drought in coarse sandy soils, obtaining adequate sample volumes and capturing representative pore samples can be problematic 10 , 12 .

On the contrary, drainage lysimeters yield both the leachate volume and the nitrate nitrogen concentration, facilitating the calculation of nitrogen load passing below the defined soil layers. Other advantages embody larger sample volumes, enabling a representative sample of the soil pore network. Nonetheless, the installation and burial of drainage lysimeters traditionally introduce considerable soil disturbance, resulting in significant deviations from the original soil’s hydraulic properties and natural attributes, including the pathways for water and solute flow 13 . Strictly speaking, this approach constitutes a comprehensive method that integrates both temporal and spatial dimensions 14 . It thereby offers a more systematic and precise assessment of nitrogen leaching losses compared to other methodologies, which often capture relatively small-scale nitrogen leaching events and provide only a momentary glimpse into nitrogen leaching patterns 15 .

Due to the advantages and limitations inherent in both ceramic suction cup extraction and drainage lysimeter methodologies, these techniques are widely applied in empirical research. Several studies have also undertaken comparative analyses of their respective monitoring performance 10 , 12 , 13 . Nevertheless, extant research often relies on the ceramic suction cup approach to estimate nitrate nitrogen leaching quantities through multiplying the nitrate nitrogen concentration within the extracted solution by the measured volume obtained from the drainage lysimeter. This practice poses constraints on the application of the ceramic suction cup method, as the calculation of soil water flux becomes the key limiting factor when drainage lysimeter equipment is unavailable. Thus, it is imperative to explore alternative methods for calculating water flux and, on this basis, to conduct a comparative analysis of the two techniques. This approach is essential for promoting the practical utility and quantitative operability of the ceramic suction cup method.

Currently, there is limited research on localized nitrate nitrogen leaching in tea plantation soils, and a lack of comparative assessments of monitoring methods. In this study, we employed two methodologies, ceramic suction cup sampler and drainage lysimeters, to concurrently monitor nitrate nitrogen leaching in tea plantation soils. We put particular emphasis on the ceramic suction cup method, combined with a water balance equation, to evaluate the accuracy and efficacy of nitrate nitrogen leaching monitoring. Our objective is to provide insights and reference points for research efforts related to nitrogen leaching in tea plantation soils.

Materials and methods

Site description.

The field experiment was conducted at Tea Research Institute of Chinese Academy of Agricultural Sciences (TRI-CAAS) Experimental Station in Zhejiang province of China (29.74°N, 120.82°E). The experimental site has a typical subtropical monsoon climate, with 12.6 °C in mean annual temperature and 1200 mm yr −1 in annual total precipitation. Before the experiment, tea plants (clone variety Baiye1 and Longjing43, hereafter referred to BY1 and LJ43) were planted in rows (1.5 m between rows and 0.33 m between plants) at a density of approximately 6000 plants ha −1 and allowed to grow for 4 years in the research site. The soil at the site was acidic red soil, developed from granite parent material with a texture that is clay. Before the experiment, the surface (0–20 cm) soil properties were pH 4.47, SOC 5.71 g kg −1 , TN 0.47 g kg −1 , available potassium (AK) 20.42 g kg −1 , and low available phosphorus (AP) 1.48 g kg −1 .

Experimental design

The experiment included different nitrogen (N) treatment levels, ranging from 150 kg N ha −1 to 450 kg N ha -1 , with three replicates arranged in a randomized complete block design. Urea was used as the nitrogen fertilizer, and nitrogen fertilization was divided into spring (30% of the total), summer (20% of the total), and fall (50% of the total) applications. In addition to nitrogen, each plot received a one-time application of 90 kg ha −1 phosphorus (as P 2 O 5 ), 120 kg ha −1 potassium (as K 2 O), and 1200 kg ha −1 of organic fertilizer as a basal application. The phosphorus fertilizer used was calcium superphosphate (13% P 2 O 5 ), the potassium fertilizer was potassium sulfate (50% K 2 O), and the organic fertilizer was rapeseed cake (5% N). Fertilization was conducted during the fall season using manual trenching (10–15 cm depth). The required amount of fertilizer for each plot was evenly spread in the trench, followed by soil backfilling.

Sample collection method

Lysimeter installation and water sample collection.

Drainage lysimeters were installed in July 2015 in such a way that they were collected for a representative transect of the production bed. This involved digging pits with 1.5 m length × 1 m width × 1 m height in the middle of the tea plant rows. In case of the side-seepage of soil solution, each lysimeter pit was surrounded by a piece of plastic leather before soil backfilling. Each lysimeter was paired with two 1.5-m pipes among which one was for air passage and another was fitted with a 1.0-cm butyl rubber suction tube to allow extraction of the leachate collected at the bottom of the lysimeter by a vacuum pump. leachate was regularly removed bi-weekly by applying a partial vacuum (25–30 kpa) using a 10-L vacuum bottle placed in the vacuum line for each lysimeter. Leachate volume was determined gravimetrically and subsamples were collected from each bottle for drainage and nitrate analysis. Please refer to our previous study reported by Zheng et al. 16 for detailed information on the installation of lysimeters and the collection of water samples.

Soil solution extraction using ceramic suction cups

The soil solution extraction using the negative pressure ceramic suction cup method involved burying ceramic suction cups at a specific soil depth and connecting them to PVC pipes. Before sampling, a vacuum pump was used to create a vacuum inside the ceramic suction cup through the PVC pipe. This vacuum pressure allowed soil solution to be drawn into the ceramic suction cup, from which soil solution samples can then be extracted. In this experiment, ceramic suction cups were installed at a depth of 100 cm in the middle of tea rows. Four ceramic suction cups were placed horizontally at distances of − 0 cm, − 25 cm, − 50 cm, and − 75 cm from the tea tree roots. Before rainfall events, the ceramic suction cups were subjected to a vacuum pressure of approximately − 80 kPa to collect soil solution generated during rainfall. This sampling way was conducted simultaneously with the lysimeter method throughout the experiment.

Meteorological data were automatically collected by a weather station located about 100 m from the research site, and soil moisture was monitored using soil moisture sensors as described in our previous study reported by Zheng et al. 16 . The average temperature and rainfall during the experiment are shown in Fig.  1 . It can be observed from the figure that the total rainfall for March to December 2019 and January to June 2020 was 1374.60 mm and 1095 mm, respectively. The average daily temperature fluctuated within the range of 4.97 °C to 29.18 °C, with the highest daily average temperatures occurring in July and August and the lowest temperatures often emerging in December or January. Rainfall was most abundant from June to September, while November and December experienced lower levels of rainfall.

Total monthly precipitation and mean daily temperature by month from March 2019 to June 2020 at the research site.

Sample analysis and data processing

After filtering the collected soil solution and leachate samples, the nitrate nitrogen concentration, NO 3 − –N concentration, was determined using a UV dual-wavelength spectrophotometry method with wavelengths of 220 nm and 275 nm 17 , 18 .

For the calculation of nitrate nitrogen leaching amount (CL) from the leachate collector, it is calculated by multiplying the volume of the collected water sample by its nitrate nitrogen concentration, and the specific calculation method is as follows in Eq. ( 1 ).

where Ci is the measured NO 3 − –N concentration in the water sample, kg N L −1 , Vi is the volume of leachate collected per extraction. The numbers 1.5 and 1.0 represent the length and width of the lysimeter, m. 0.01 is the conversion factor.

For the ceramic cup method, we need to apply a water balance equation to calculate the water flux over a specific time period. After that, you can multiply it by the concentration of nitrate nitrogen in the extracting solution to obtain the nitrate nitrogen leaching amount. The specific calculation process is as follows in Eq. ( 2 ).

The cumulative nitrate nitrogen leaching amount (CLs) for the ceramic cup method can be calculated as follows:

where C ἰ and C ἰ+1 (kg N L −1 ) represent the average concentrations of nitrate nitrogen in the soil-extracting solution for two consecutive sampling times. n represents the total number of sampling events.

D represents the water flux over the time interval between the two sampling events, which can be calculated using the water balance equation as shown in Eq. ( 3 ).

where P is the precipitation (mm), I means the irrigation water quantity (mm), which is not relevant in this study and is not considered in the calculations. VR is the change in soil water storage (mm). D is the leachate flux (mm). ETc is the crop evapotranspiration (mm), calculated as ETc = kc* ET 0 , where ET 0 is the reference evapotranspiration for crops calculated from meteorological data according to FAO-56 Penman–Monteith equation 19 . The calculation of ET 0 can be simplified as follows in Eq. ( 4 ).

where ET 0 is the reference evapotranspiration (mm day −1 ), R n is the net radiation at the crop surface (MJ m −2  day −1 ), G is the soil heat flux density (MJ m −2  day −1 ), T is the mean daily air temperature at 2 m height (°C), u 2 is the wind speed at 2 m height (m s −1 ), e s is the saturation vapor pressure (kPa), e a is the actual vapor pressure (kPa), (es-ea) is the saturation vapor pressure deficit (kPa), ∆ is the slope vapor pressure curve (kPa °C −1 ), γ psychrometric constant (kPa °C −1 ), and 900 is the conversion factor.

Statistical data analysis was conducted using SPSS 22 software (SPSS Inc., New York, USA). One-way analysis of variance (ANOVA) was performed, followed by Duncan's post hoc test (p < 0.05 indicates significant differences, while p < 0.01 indicates highly significant differences). All graphs were generated using Sigmaplot 12.5 software (Systat Software Inc., Milpitas, USA).

Results and discussion

Comparison of drainage flux and leachate volume calculation.

During the experimental period from March 2019 to June 2020, 22 samples were taken both for BY1 and LJ43. The drainage flux for each sampling interval was calculated using the water balance equation. Based on the results from our previous study 16 , for BY1, Kc was set to 0.71 to calculate evapotranspiration. When the rainfall exceeded 78.02 mm, the drainage flux was fixed at the maximum value of 20.63 mm. For LJ43, Kc was set to 0.84 to calculate evapotranspiration, and when the rainfall reached or exceeded 90.98 mm, the drainage flux was fixed at the maximum value of 21.45 mm. For other rainfall levels, the drainage flux was calculated using the actual rainfall and the water balance equation. On this basis, the calculated drainage flux was compared and analyzed against the equivalent water depth calculated by converting the leachate volume extracted from the lysimeter (Lysimeter leachate). The equivalent water depth (mm) is calculated as the extracted water volume (L) divided by the lysimeter's area (1.5 m 2 in this study). The results are shown in Fig.  2 .

Correlation analysis of lysimeter leachate and calculated drainage ( a ) and comparison of cumulative leachate and cumulative calculated drainage ( b ) for the BY1 and LJ43 during the study period.

From Fig.  2 a, it can be observed that the volume data points for both methods are distributed close to the 1:1 line, indicating that the calculated drainage flux and the lysimeter leachate volume measurements are generally in good agreement. Furthermore, the total volume sums for both methods were calculated separately (Fig.  2 b). The results indicate that the cumulative calculated drainage flux for BY1 during the experimental period was 389.21 mm, slightly higher than the total lysimeter leachate volume measured at 367.77 mm. For LJ43, the total calculated drainage flux was 332.22 mm, slightly lower than the total lysimeter leachate volume of 362.15 mm. Finally, when combining all results for BY1 and LJ43, the total calculated drainage flux and the total lysimeter leachate volume were 721.43 mm and 729.92 mm, respectively, with the former only 1.16% lower than the latter. Therefore, the application of the water balance equation for soil drainage flux calculation demonstrated high accuracy and feasibility.

Comparison of soil solution and leachate nitrate nitrogen concentrations

A relationship was created with the nitrate nitrogen concentration of the lysimeter leachate during the experimental period as the x-axis and the nitrate nitrogen concentration of the soil solution extracted using the ceramic cup method as the y-axis. Additionally, a logarithmic transformation was applied to further analyze the impact of the two extraction methods on nitrate nitrogen concentration. The results are shown in Fig.  3 . It can be observed in Fig.  3 a that when the nitrate nitrogen concentration in the lysimeter leachate is less than 7 mg L −1 , all nitrate nitrogen concentrations in the soil solution extracted from the ceramic cup method are higher than those in the lysimeter leachate. Subsequently, as the nitrate nitrogen concentration in the lysimeter leachate increases from 7 mg L −1 to 13 mg L −1 , approximately half of the soil solution extracted from the ceramic cup method has a higher nitrate nitrogen concentration than the lysimeter leachate, while the other half has a lower nitrate nitrogen concentration. Then, when the nitrate nitrogen concentration in the lysimeter leachate exceeds 13 mg L −1 , all soil solution extracted using the ceramic cup method has a lower nitrate nitrogen concentration than the lysimeter leachate.

Correlation between ( a ) nitrate concentration from lysimeter and suction cup and ( b ) nitrate concentration from lysimeter and logarithmic conversion value of the ratio of nitrate concentration from lysimeter to suction cup nitrate concentration.

Further analysis was conducted by taking the ratio of the nitrate nitrogen concentrations in the lysimeter leachate and the soil solution extracted using the ceramic cup method as a real number, with a base of 2 for logarithmic transformation. The trend of this transformed value with respect to the nitrate nitrogen concentration in the lysimeter leachate is shown in Fig.  3 b. It is evident that as the nitrate nitrogen concentration in the lysimeter leachate increases, the logarithmic transformation value increases from its minimum value of − 3.51 to 1.93. The transformation value exhibits distinct trends and characteristics based on the grouping of nitrate nitrogen concentrations in the lysimeter leachate. When the lysimeter leachate concentration is less than 7 mg L −1 , the transformation value is consistently less than 0. When the lysimeter leachate concentration exceeds 13 mg L −1 , the transformation value is consistently greater than 0. However, when the lysimeter leachate concentration falls between 7 mg L −1 and 13 mg L −1 , both positive and negative transformation values coexist.

Comparison of nitrate nitrogen leaching between two methods

Similarly, a relationship was created with the nitrate nitrogen concentration of the lysimeter leachate (Lysimeter method) as the x-axis and the nitrate nitrogen concentration obtained using the ceramic cup method combined with the water balance equation (Ceramic cup method) as the y-axis. Additionally, a logarithmic transformation was applied to further analyze the impact of the two methods on nitrate nitrogen leaching. The results are shown in Fig.  4 . From Fig.  4 a, it can be observed that when the nitrate nitrogen concentration in the lysimeter leachate is less than 7 mg L −1 , almost all nitrate nitrogen leaching calculated using the ceramic cup method is higher than the nitrate nitrogen concentration in the lysimeter leachate. When the lysimeter leachate concentration falls between 7 mg L −1 and 13 mg L −1 , more than half of the nitrate nitrogen leaching calculated using the ceramic cup method is lower than the lysimeter method, while the other half is higher. Then, when the lysimeter leachate concentration exceeds 13 mg L −1 , all nitrate nitrogen concentrations calculated using the ceramic cup method are lower than the lysimeter leachate.

Correlation between ( a ) nitrate leaching from lysimeter and suction cup and ( b ) nitrate leaching from lysimeter and logarithmic conversion value of the ratio of nitrate leaching from lysimeter to suction cup nitrate leaching.

Further analysis was conducted by taking the ratio of the nitrate nitrogen concentrations in the lysimeter leachate and those calculated using the ceramic cup method as a real number, with a base of 2 for logarithmic transformation. The trend of this transformed value with respect to the nitrate nitrogen concentration in the lysimeter leachate is shown in Fig.  4 b. It is evident that the transformation value follows a trend highly similar to the concentration transformation trend mentioned above. As the nitrate nitrogen concentration in the lysimeter leachate increases, the logarithmic transformation value increases from its minimum value of − 3.51 to 1. This transformation value exhibits distinct trends and characteristics based on the grouping of nitrate nitrogen concentrations in the lysimeter leachate. When the lysimeter leachate concentration is less than 7 mg L −1 , the transformation value is consistently less than 0. When the lysimeter leachate concentration exceeds 13 mg L −1 , the transformation value is consistently greater than 0. However, when the lysimeter leachate concentration falls between 7 mg L −1 and 13 mg L −1 , both positive and negative transformation values coexist.

In addition, statistical analysis was performed on the total nitrate nitrogen leaching for each concentration group. The results indicate that when the lysimeter leachate concentration was less than 7 mg L −1 , the total nitrate nitrogen leaching obtained by the lysimeter method and the ceramic cup method is 22.24 kg ha −1 and 44.05 kg ha −1 , respectively. When the lysimeter leachate concentration fell between 7 mg L −1 and 13 mg L −1 , the total nitrate nitrogen leaching calculated by the lysimeter method and the ceramic cup method was 47.45 kg ha −1 and 43.58 kg ha −1 , respectively. When the lysimeter leachate concentration exceeded 13 mg L −1 , the total nitrate nitrogen leaching obtained by the lysimeter method and the ceramic cup method was 156.28 kg ha −1 and 79.95 kg ha −1 , respectively. In summary, there were differences in quantified nitrate nitrogen leaching losses between the two methods. If the lysimeter method was used as the standard, the ceramic cup method exhibited higher monitoring accuracy when the nitrate nitrogen concentration in the lysimeter leachate fell within the range of 7–13 mg L −1 .

Effect of rainfall on the application of the water balance model

The use of ceramic cup methods to monitor nitrate nitrogen leaching in farmland requires estimation of soil water flux through modeling. This inevitably introduces uncertainties in accurately quantifying nitrate nitrogen 20 . In this study, the application of a water balance model for quantitatively calculating soil drainage volume seemed to yield slightly lower water flux results compared to the corresponding measurements obtained through the lysimeter method, especially when rainfall was low (Fig.  2 a). One possible reason for this discrepancy could be that the water balance equation typically accounts for only the saturated flow above field capacity, neglecting unsaturated flow. However, it is reported that unsaturated flow, which occurs at lower soil moisture levels, is more common in practice, especially when rainfall is low and soil moisture levels remain relatively low 21 . Therefore, it is speculated that unsaturated flow is the primary reason for the water balance model calculating lower water flux than the lysimeter measurements under these conditions.

On the other hand, for conditions with higher rainfall intensity, when applying the water balance equation to estimate water flux, it should strictly include runoff as part of the water output, with the most accurate method being the construction of runoff tanks for precise measurement. However, this study lacked the necessary means to estimate runoff, which likely led to significant deviations in the final water flux calculations. Nevertheless, previous study reported that runoff typically occurs during heavy rainfall events and increases with higher rainfall amounts 22 , 23 , 24 and when a certain critical rainfall intensity is reached, water will be lost as runoff because the soil cannot absorb and retain it, and an eventual maximum leachate flux will occur 25 . Based on our previous study, critical rainfall amounts and maximum water leachate fluxes were determined for the tea varieties of Longjing 43 and BaiYe 1, thus mitigating the significant calculation bias arising from the absence of runoff monitoring.

Effect of soil texture on the accuracy of the suction cup-based method

The lysimeter method, being considered a relatively accurate technique for monitoring and quantifying soil nitrate nitrogen leaching, is often regarded as a true reflection of nitrate nitrogen leaching in soil 26 . This study indicated that when the nitrate nitrogen concentration in lysimeter leachate fell below 13 mg L -1 (especially within the range of 7–13 mg L −1 ), the ceramic cup method demonstrated relatively accurate monitoring results. However, when the leachate nitrate concentration exceeded 13 mg L −1 , A much lower result was obtained from the ceramic cup method compared to the lysimeter method. The reason for this may rely on the soil structure. From the perspective of soil texture, this experiment was conducted in a relatively heavy clay tea plantation, where the clay content within the top meter of soil ranged from 62.53 to 69.99% 16 . Under such soil conditions, nitrate nitrogen is likely to be transported downward through preferential flow. Preferential flow is characterized by the rapid movement of most soil water and solutes through the large and intermediate pores of the soil, bypassing the surface soil and moving downward 27 . Previous studies have found that the occurrence of preferential flow was much higher in clay soils than in sandy or loamy soils 28 , 29 , 30 . This often resulted in higher concentrations of nitrate nitrogen in leachate water 31 .

Ceramic cups, on the other side, have been reported to be unsuitable for use in clayey soils because the presence of preferential flow makes it difficult for ceramic cups to effectively collect water flowing through large pores, especially during heavy rainfall events 32 . Additionally, Barbee and Brown (1986) compared the performance of ceramic cups and lysimeters in monitoring chloride ions in soils with three different textures. The results showed that lysimeters generally provided higher and more stable monitoring results in loam and sandy loam soils, while ceramic cups were almost ineffective in clayey soils due to the rapid leaching and movement of water through large pores. Therefore, to some extent, ceramic cups were considered to be a flawed soil solution extraction technique for clayey soils. These factors need to be considered in soil nitrate nitrogen leaching studies, especially in soil types like clay, where choosing an appropriate solution extraction method is crucial for obtaining accurate data.

Conclusions

In comparison to direct measurements using lysimeters as a reference, the feasibility of the ceramic cup's negative pressure extraction estimation method was analyzed. The results demonstrated that the total calculated drainage flux and the total measured volume for lysimeter leachate were 721.43 mm and 729.92 mm, respectively, indicating that the application of the water balance equation for estimating soil drainage flux is accurate and feasible. Furthermore, through a comparative analysis of nitrate nitrogen concentrations in water samples collected by lysimeters and ceramic cups, it was observed that the ceramic cup method exhibited a certain accuracy in estimating nitrogen leaching, especially when the nitrate nitrogen concentration in lysimeter leachate fell within the range of 7–13 mg L −1 . However, under conditions of intense leaching (nitrate nitrogen concentration in lysimeter leachate exceeding 13 mg L −1 ), there was a risk of underestimation due to the potential lack of representative samples. Therefore, it is advisable to increase sampling frequency under such special circumstances.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Abbreviations

Variety Baiye1

Variety Longjing43

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• Open access
• Published: 04 September 2024

Exploring maternal and child healthcare projects in South African communities through Ubuntu lens: an appreciative inquiry

• S. Nyandeni 1 ,
• N. S. Raliphaswa 2 ,
• M. R. Musie 1 ,
• M. S. Maputle 2 ,
• R. Gundo 1 ,
• F. M. Mulaudzi 1 &
• N. V. Sepeng 1  

BMC Nursing volume  23 , Article number:  619 ( 2024 ) Cite this article

Metrics details

Ubuntu, a Nguni Bantu term meaning “humanity towards other”, embodies a philosophy of collectivism, interconnectedness and mutual respect, which is deeply ingrained in South African culture. Ubuntu led community-based collaborative projects enhance community engagement of several stakeholders to ensure improved health outcomes for the mothers and children in the region. Similarly, collaborations between universities are required to co-create evidence-based interventions with healthcare systems and communities to achieve healthcare objectives. This paper explores maternal and child healthcare (MCH) projects in South African communities through the Ubuntu lens, using an appreciative inquiry approach.

A Qualitative approach based on the 5-cycle of Appreciative Inquiry (AI) as proposed by Cooperrider and Srivastava were applied. A purposive sampling method was used to select participants ( n  = 14) who are members of the Ubuntu MCH project. Data was gathered through workshop group discussions. The interviews were audiotaped and transcribed verbatim. Data analysis followed the six steps of narrative analysis.

Three main themes were identified: Academic growth of personnel; professional empowerment and Ubuntu mentorship.

This study confirms that the Ubuntu principles emphasises solidarity, cohesion, and collaboration. The study recommends leveraging on Ubuntu principles to strengthen maternal and child healthcare services, suggesting that such this approach can lead to more sustainable and impactful health improvements in South African communities.

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Introduction

MCH forms the heart of healthcare services in ensuring the socio-economic development of nations. The MCH services are rendered by healthcare professionals to provide quality patient care which is of importance to attain the third sustainable development goal (SDG) [ 1 ]. The key indicators are 3.1 and 3.2. SDG 3.1 focused in reducing maternal mortality ratio to less than 70 deaths per 100,000 live births [ 1 , 2 ]. Whereas 3.2 focused in ending preventable deaths of new born and children to less than 25 per 1000 live births under age five by 2030 [ 1 ]. Presently, there are existing challenges in delivering of MCH services that impacts the achievement of the targets for African countries such as Nigeria and South Africa amongst other countries [ 2 ]. Some of the greatest challenges MCH services are facing is that patients are receiving poor quality of care, inequal access to healthcare services, and low utilization of maternal and child health services, inadequate skilled healthcare providers [ 2 , 3 ].

According to the World Health Organisation’s (WHO), MCH services are to be improved through escalating research evidence, supply evidence-based clinical and programmatic guidance [ 3 ]. In 2020, the MCH project subunit under the Ubuntu community model in nursing was developed. The project aims at addressing MCH community related challenges experienced in South African communities. The Ubuntu community model in nursing is a 5- year project funded by the National Research Foundation (NRF) which seeks to enhance access to healthcare services amongst underprivileged communities through developing, piloting, and the implementation of Ubuntu- Innovation model [ 4 ]. The project is hosted by the University of Pretoria and is implemented in collaboration with four other South African universities namely the University of Venda, Northwest University, University of Johannesburg, and University of Limpopo. These members from various Universities are involved in Ubuntu Community Model in Nursing  subproject namely; Traditional and allopathic health care, MCH, TB and HIV management and training, community health, and multi, inter and trans disciplinary knowledge field. For the purpose of this paper, we will report the interventions performed by the MCH project including comprehensive formative research, appreciative inquiry, participatory action research, multi-stakeholder discussion of results and interactions with the project communities to co-design interventions that will address maternal health issues. These projects all incorporate the tenets of Ubuntu philosophy.

The ubuntu philosophy within Africa is a dominant ontological system of humanness used to bring all communities and cultures together through its principles [ 5 ]. Similarly, Ubuntu is a Nguni word, that generally means humanity and respectful treatment of others [ 5 , 6 ]. Ubuntu is viewed as a caring ethic in the African context [ 7 ], and the essence of Ubuntu's philosophy for MCH is the provision of culturally appropriate and respectful maternity care (RMC). Nurses and midwives should display values such as compassion, honesty, trust, and commitment which are also reflected in Ubuntu [ 5 ]. Albertina Sisulu who was an icon in the nursing profession in South Africa exemplified the Ubuntu core values in her nursing practice [ 5 ].

Furthermore, Ubuntu's philosophy emphasises the essence of shared humanity and collective well-being, co-creation, and solidarity [ 6 ]. Central to the philosophy of Ubuntu are its values which are summarised into The R’s as follows: mutual respect, reciprocity, responsibility, relational, and role modelling [ 8 ]. It has been previously observed that collaborative projects involving educational institutions and the community had improved MCH through the use of mobile clinics, initiating ways to resolve challenges of poor MCH [ 9 ]. Furthermore, the existing body of research on collaborative efforts to improve MCH, suggests that innovative and collaborative models had positive outcomes on MCH services [ 10 ].

There are different Ubuntu related activities that are planned for each subgroup. The MCH subunit implemented projects including: The papers that were published were focusing on teenage pregnancy through introduction of sexual education in high schools, the need for collaborating with TBAs when caring for pregnant women and training midwives on point of care ultrasound in African region [ 11 , 12 , 13 , 14 ]. The team members of the MCH subunit also contributed in writing a chapter in a book entitled: Working with indigenous knowledge: Strategies for health professionals [ 14 ]. The outputs of the project include community empowerment and engagement, publishing scientific manuscripts and writing of book chapters. In support, the Stanford Maternal and Child Health Research Institute (MCHRI) fosters transdisciplinary research to improve health of pregnant woman and children [ 15 ]. Through MCHRI clinical research components, there is facilitation and promotion of clinical and translational research, supporting research projects. Furthermore, the MCHRI clinical research support office (CRSO) works with research and hospital teams to eliminate MCH challenges [ 15 ].

MCH research goal is to investigate, develop, implement, and evaluate interventions and strategies to improve mother and child well-being and reducing the burden of disease and disability through research of evidence- based practices [ 1 , 15 ]. Amongst other challenges the researchers often identify limited research as a practice gap [ 15 ] and there is a need for international and regional continuous research to ensure the MCH research goal is reached [ 1 , 16 , 17 , 18 ].Additionally, in South Africa, structural fragmentation related to limited community involvement is seen as a threat. However, investing in community involvement to transfer valuable information and improve referral systems will have positive MCH outcomes [ 19 ]. One of the greatest challenges faced by MCH was having different universities working in silos to provide evidence-based research practices. Although there were notable improvements in the MCH services, significant shortcomings related to a lack of acknowledgment of evidence-based research practices in clinical practice [ 20 ]. The absence of universities and community collaborative work in the clinical practice will result in compromised MCH services. The experience-based achievements of the MCH subunit under the 5-year progressive and renewable research project has not been reported. Therefore, this paper sought to explore MCH projects in South African communities through the Ubuntu lens, using an appreciative inquiry approach.

Methodology

Study design.

The present study utilised an qualitative, explorative and descriptive design applying appreciative inquiry (AI) design to explore and describe ‘what gives life’ and fuel development within the MCH subunit of the Ubuntu community model in nursing [ 20 , 21 ]. The selected approach is beneficial and suitable for this study because its key strategy acknowledges the strengths and success of both individuals, group, and organizations [ 22 ]. AI approach was adopted to gain a detailed understanding of what is functioning well, what is positive and identifying the strength of the group through group members sharing their experiences [ 21 ]. AI was conducted through the five-D cyclical phases as proposed by Cooperrider and Srivastava namely: define, discovery, dream, design, and destiny/delivery phases [ 23 , 24 ].

Defining phase:

In this phase, the present study title “Exploring MCH projects in South African communities through the Ubuntu lens, using an appreciative inquiry approach” was clarified when describing the introduction and background.

Discovery phase.

In this phase, participants were asked to share their peak moments as members of the committee, as they appreciated the existence of the committee. In this phase the committee members reflected on their experiences in the Ubuntu MCH project, what had happened, and the forces that made it possible. The purpose of this phase was to identity the strengths and achievements of the group.

Dreaming phase.

In this phase, the following question was asked:

“ What would be the best and ideal function of the MCH subunit in the Ubuntu project ?”

Participants were asked to imagine the future of the committee and share the change and growth they wish for the future [ 23 , 25 ]. They were encouraged to envision new and different functions and share what they saw in terms of purpose, values, systems, other members, ways of working, and working relationships.

Designing phase.

“What are your three wishes in order of priority that would make improvements in MCH become reality?”. In this phase, participants were asked to create or design the MCH subunit of the Ubuntu project.

Destiny phase.

In this phase, the participants were asked to share how they would monitor the progress of the imagined future of the committee and sustain it [ 23 , 25 ].

Population and sampling

The population of this study comprised of: 12 academics staff members from the five South African Universities working in collaboration, and (1) professional nurse working in MCH sector. All academic staff were specialising in midwifery. A purposive sampling method was used to select participants ( n  = 14) of MCH subunit under the ubuntu community model in Nursing that has been working together for three years. The university is having self-paying students, bursary sponsored and those who are on scholarships. There are undergraduate and postgraduates’ students doing practical work in surrounding government hospitals and clinics.

Study setting

The study was conducted in one of the five collaborating Universities in South Africa, faculty of health, nursing sciences department. At the selected university the participants were interviewed in the board room.

Data collection

Data was collected during a workshop using group discussion methods which lasted between one hour to one hour and 30 min [ 26 ]. The group discussion was based on the work implemented by the Ubuntu MCH subunit from different academic institutions in South African Universities within the three-year cycle of working together. The participants were asked questions related to the 5D model appreciative inquiry, which have been discussed in Fig.  1 above. The interviews were recorded and transcribed verbatim. Field notes were taken during the discussions.

5-D cycle of appreciative inquiry [ 23 ]

Data analysis

Data analysis followed six steps of narrative analysis by Holstein and Gubrium namely: 1. Codes of narrative blocks were inductively created by grouping similar positive experiences shared by the participants; 2. all narratives were examined and coded with the same code; 3. similarities and differences were noted down; 4. codes were created and nested according to the story structure of the participants' positive experiences [ 27 ]. 5. Further search into the structure was done by categorising each positive experience based on story structure. 6. Study structure was compared by ensuring the overarching narrative was maintained when breaking up the narrative blocks by story structure. 7. The structure that was used and wrote a core narrative that encapsulates the commonalities between what the participants have reported for each life event [ 27 ].

Trustworthiness

Trustworthiness was achieved by using the four criteria of credibility, transferability, dependability, and confirmability. Credibility was maintained when conducting the interviews through prolonged engagement allowing researchers to build rapport with the participants and gain understanding of their positive experiences. Confirmability was maintained through the researchers' use of bracketing their thoughts and ideas to prevent contaminating the findings of the study. Transferability was maintained through purposive sampling to recruit members of the MCH healthcare groups. To ensure dependability the researchers had fully described the methodology followed to conduct the study.

Ethical considerations

The study obtained ethical approval from the University of Pretoria, Research ethics committee approval number: 297/2020 and the participants gave informed consent to participate in this study. The ethical principles used by the researchers were: the principle of beneficence and the principle of justice. The researchers ensured that there was no harm caused to the participants and they were treated with fairness and equality. Participants were informed that they could withdraw at any time if they felt uncomfortable with the research.

Sociodemographic characteristics

All fourteen (14) participants who agreed to participate were female professional nurses. Of the fourteen participants, five were above 50–60 years, while the other nine were 29–40 years. Educational status of the participants: PhD holders (7) and master’s holders (7). The participants held various job titles including four professor, nine lecturers and one professional nurse. All participants had published in a peer reviewed journal. See Table  1 above with summary of participants’ demographics. To protect participants identity pseudo names were assigned.

Emergent themes and sub-themes

Three main themes emerged from the discussions: academic growth of personnel, professional empowerment, and Ubuntu mentorship , were identified in this study and each theme had its sub-themes as presented in Table  2 : Summary of themes and sub themes are listed in the Table  2 below.

Theme 1. Academic growth of personnel

This theme describes the realities of each of the participants' journeys within the MCH subunit as they reflect on the defining and discovery of their journey. Participants described their achievements and how they have grown academically through the opportunities that were brought by the different institutions when working together. They reflected on personal and professional growth that improved MCH services..

i. Writing of book chapter

Nine of the participants reflected on the achievement of writing a maternal and child book chapter 6 “Titled African Indigenous Beliefs and Practices during Pregnancy, Childbirth, and Afterbirth” as part of the book called “Working with Indigenous Knowledge: Strategies for Health Professionals”.

“One of the highlights for me participating in the MCH subunit was when we were given a task to write a book chapter. As an emergent researcher, this empowered me as my Ph.D. work is now packaged in a book that will be accessible to pregnant women and health professionals sharing with them the indigenous and traditional practices performed by the traditional birth attendants during labour and childbirt” [Participant 14].

“Our nursing students will now have access to learn about the indigenous practices associated with pregnancy and childbirth because for long the information on indigenous practices has been condemned as witchcraft and disregarded” [Participant 12].

ii . Individual and group research publication in peer-reviewed and high-impact factor journals

The group conducted a scoping review on the ultrasound point of care. The participants co-authored a manuscript to improve the scope of practice of midwives:

“The training of midwives on basic ultrasound will assist midwives in remote areas to be able to detect problems on time and refer to the higher level of care timeously, therefore improving maternal and newborn outcomes” [Participant 10]. In addition, the participants expressed their gratitude when explaining their transformative journey of being part of the Ubuntu MCH subunit by leading research papers for publication in high-impact factor journals. Additionally, emerging scholars had an opportunity to lead papers, and publish their first papers in peer-reviewed and high-impact factor journals.

“I was excited to learn that the paper I have written with emerging scholars, collaborators, and senior researchers is accepted for publication in high impact factor and quartile 1 journals” [Participant 3].

The participants had an opportunity to receive comments from their peers and senior researchers and make corrections before uploading their papers to the online journal submission system. According to the participants, this practice enabled them to learn from one another, it enhanced their research output and their papers being published in high-impact factor journals. Therefore, based on this, they are of the view of continuing to attend writing retreats and possibly plan to have them every quarter within one year to improve publications in writing retreats. In addition, they indicated the need for planning the number of papers they want to publish within a year. They are also envisaging grooming young academics with writing skills and leading authors for publication.

iii. Leading a research project

The participants expressed that their transformative experience as members of the group provided an opportunity to lead a research project. Participants recounted positive experiences, including the satisfaction of crafting a proposal until its approval by various ethics committees.

“I started a research project that is advocating for the inclusion of Ubuntu philosophy in MCH and honestly speaking I was so happy to receive ethics approval from my institution and various departments to start with data collection of my project with my other co-researchers” [Participant 5].

“I was happy to take the lead and write a proposal that is focusing on the promotion of Ubuntu in MCH to the point of being approved by the ethics committee and the Department of Health” [Participant 1].

Most of the participants indicated that their dream is to see each member of the group leading her research project within 5 years of working together in this group. To achieve the purpose of leading a research project, other group members indicated that they have already started drafting their research projects for post-doctoral and for achieving their doctoral studies. The purpose of collaborating in research projects that are led by post-doctoral researchers and supervising students collaboratively is to empower and learn from one another. They agreed on the use of online meetings, the use of emails, and attending research supervision training workshops organised by their intuitions to empower themselves.

iv. Established collaborative relationships between the universities

As part of the dream phase of the AI process, the participants indicated that transformation to them in the MCH subunit meant building collaborative lasting relationships between the Universities, for long the Universities operated in silos and now the barriers have been broken and collaboration is facilitated, as supported by the following quotations.

“The Ubuntu MCH subunit is working collaboratively with different Universities working towards a common ground of improving MCH in South Africa, with the hope to replicate to other African countries” [Participant 4].

“The MCH subunit assisted some of us as midwifery lectures to research and supervise students in areas on MCH that will improve patient outcomes and revive the nursing profession” [Participant 6].

Collaboration across the different universities will assist in improving the image of midwifery care, the collaborations went beyond research and assisted with the teaching activities as well.

Theme 2: Professional empowerment

The participants engaged in the dream and design phase of the AI process and identified some of the accomplished dreams within the Ubuntu MCH unit which includes the existing projects that are currently implemented within the project. The theme is reflected in the sub themes: promotion of (RMC) through the incorporation of Ubuntu philosophy; training on CTG and ultrasound point of care for midwives, and provision of health education regarding the prevention of teenage pregnancy among different stakeholders.

i. Promotion of (RMC) through incorporation of Ubuntu philosophy

The MCH subunit is currently implementing a project towards the promotion of respectful maternity care through the incorporation of Ubuntu philosophy. The group identified the need for the project as currently, the status of midwifery care is not up to standards, the midwives display disrespect to the birthing women and poor attitudes of dehumanising the women. Thus, the project was developed to improve the image of maternity care by introducing Ubuntu philosophy, which is based on humanness and treating others with respect.

“As part of the unit, we are supervising a master’s degree student implementing the project of promoting respectful maternity care by introducing the principles of ubuntu to the midwives” [Participant 11].

“Many of the childbearing women experience obstetric violence during childbirth, where the midwives perform some procedures (such as performing episiotomy) without obtaining informed consent from the women, thus it is important to talk about respectful maternity care with the midwives. Where they need to treat the women the way they would like to be treated which is Ubuntu” [Participant 14].

The group agreed that all women deserve dignity and respect in the promotion of quality maternity care. Moreover, ensuring the women are free from harm and mistreatment, midwives uphold the ubuntu philosophy values such as humanity, thoughtfulness, caring, and social sensitivity.

ii. Training on CTG and ultrasound point of care for midwives

The second identified subtheme on the existing projects includes the project on CTG (cardiotocography) and ultrasound point of care for midwives.

“This project is part of my Ph.D. study where the need to train midwives on daily cardiotocography interpretation was necessary, as I noted some inconsistencies with how midwives interpret the CTG. Some midwives in tertiary hospitals wait for the obstetricians to interpret and sign the CTG trace” [Participant 12].

“We teach our students how to interpret the CTG, but when they get to the maternity wards the midwives are reluctant to teach the students. Or if they teach them the interpretation is different from what they are taught at the University” [Participant 8].

“We conducted pre- and post-ultrasound training programs for the midwives working in the nearby hospitals as we organised a well-trained sonographer to come to the University and train the midwives to conduct basic ultrasounds, as the midwives are working in the wards with ultrasounds machines and have to wait for a long time before a doctor can come and perform the ultrasound” [ Participant 7].

iii. Provision of health education regarding the prevention of teenage pregnancy among different stakeholders

One of the activities that were done by the MCH subunit members were giving health education regarding prevention of teenage pregnancy to different stakeholders. Members of this group indicated that what inspired them to provide health education regarding teenage pregnancy amongst these different stakeholders was the way they are being planned and conducted. Participatory action research approaches were used when conducting this community engagement of providing health education amongst different stakeholders.

“It was good to see members of the community responding positively and being involved about the issues that seemed to be problematic within their areas, for example, teenage pregnancy and its consequences from the planning phase to the end of the session” [ Participant 3]. “I have learned about the trans-disciplinarity involvement of different stakeholders needed for the prevention of teenage pregnancy, for a change the community members were involved from the planning phase to implementation of health-related education about the prevention of pregnancy” [ Participant 7].

The participants of this group indicated that the way the community engagement was done in the past three years was very interesting because they learned to combine community engagement and research in which community members were involved from the planning phase. In addition, they have discovered that the community appreciates and opens doors to researchers who are involving them from the planning phase, and implementation phase and respect their opinions. On the other hand, the team members also felt that they were learning a lot from the community on how they are tackling health-related problems within their areas thus making their involvement visible. Also, members of this group indicated that there is a need for teamwork and motivation to conduct community engagements from the planning phase not only when there is a research project that is done for publication.

Theme 3 Ubuntu mentorship

Theme 3 reflects more on the destiny of the Ubuntu MCH subunit as part of the AI process. The participants reflected on how the subunit has capacitated them to promote empowerment and mentorship of others. One subtheme supports the theme of mentoring from senior midwives to novice midwives.

i. Mentoring from senior midwives to novice midwives

Four participants explained that one of their transformative journeys in community engagement was to empower healthcare professionals by organising sonography training for midwives. The training was done in collaboration with the selected hospital as part of community engagement to empower midwives in sonography training which is a debated and needed skill for task shifting of scope of practice amongst the midwives. They explained that the empowerment of midwives to perform sonography was done by a licensed sonographer. The part that they enjoyed in this training was the practical component because midwives were not only empowered through teaching them, but they were also allowed to perform sonography after the sonographer had presented the theory to them . The journey in the MCH subunit has assisted the participants in acquiring mentorship skills.

“I am confident now to show the students how to perform the certain midwifery procedures inward as I have learned the skills of teaching and lifting others and working together from research group” [ Participant 13].

In other instances, the mentorship also took place between the senior lecturer and junior lecturer.

“The research group has made me a leader and empowered me with the skills of mentoring and helping others with midwifery skills” [ Participant 11].

Additionally, the nursing students studying in a selected University and registered nurses were trained on the application of Ubuntu principles and values when caring for patients in health care systems and maternity wards. The participants of this study also perceived this training as interesting and practical based on the feedback received from nursing students and registered nurses.

“Most of the registered nurses and nursing students that were trained reported that the trainers used the real case scenarios showing how other nurses are not applying Ubuntu values and principles when caring for their patients in antenatal care, labour, and post-natal care” [Participant 7].

“…Also, other case scenarios were practical and realistic on s how other nurses are applying Ubuntu values and principles when caring for their patients in the hospital setting.” [Participant 2].

After the training, registered nurses and nursing students felt that they needed to reflect and strive to apply Ubuntu values and principles when caring for their patients. They were satisfied with the training received from this group and indicated that it reminded them of the basics of nursing and ethos of professional practice that they were taught while studying nursing.

The present study aimed to explore the MCH projects in South African communities through the Ubuntu lens, using an appreciative inquiry approach.

Theme 1: Academic growth of personnel

This study used the Ubuntu Lens to investigate and describe the experience-based achievements of MCH subunit. The findings of this study demonstrated the pride and sense of achievement reported by the participant as they accomplished writing of book chapters in MCH that focus on indigenous knowledge systems, as well as teaching nursing students about indigenous practices related to pregnancy and childbirth. There are few books written with an emphasis on indigenous knowledge systems, particularly those focusing on MCH. According to Hlatywayo, indigenous cosmology has received little, if any, attention when it comes to pregnancy and childbirth [ 28 ]. Furthermore, Drummond asserted that, in the context of nursing education, Indigenous peoples have consistently recommended improvements in nurse educational preparation for Indigenous peoples' health, as evidenced by numerous national reviews and reports [ 29 ]. Despite these recommendations, the implementation of indigenous knowledge system (IKS) in the nursing curriculum, particularly in MCH, is slow in South Africa. As a result, it is critical to address this issue of policy implementation as soon as possible because nurses and midwives must cater to their patients' holistic health needs through incorporating their cultural belief system when caring for patients.

The study findings of this paper revealed that the members of the group indicated that there is a need for them to have individual and group research focus areas addressing MCH issues, particularly through conducting studies that advocate for task shifting the importance of teaching midwives on the use of obstetric basic ultrasound and cardiotocography (CTG).

One of the findings of this study is that leading projects that focus on and promote the Ubuntu philosophy in MCH. Participants in this study believe that universities are working in silos to improve MCH in South Africa. The findings highlighted the importance of collaboration among university staff to improve MCH through applying Ubuntu principles. The ubuntu philosophy plays an important role in the history of caring. Ubuntu, which roughly translates as "human kindness," is frequently interpreted as "humanity toward others'' [ 5 ]. The state of MCH has deteriorated over the years, necessitating the need for midwives to reflect on the history of caring by applying Ubuntu to their patients. It was also stated that most pregnant women face violence in the labour ward, and thus there is a need to provide respectful maternity care to them using Ubuntu. Ndwiga and colleagues also asserted that many women experience uncaring and abusive treatment from health care providers during facility-based labour and delivery [ 30 ]. Ubuntu philosophy is defined by the following values: humanity, caring, sharing, respect and compassion, warmth, empathy, giving, commitment and love almsgiving, sympathy, care, sensitivity to others' needs, respect, consideration, patience, and kindness [ 31 , 32 ].

The findings of this study revealed the need for training midwives on CTG and ultrasound point of care. Midwives are the primary providers of antenatal care and are frequently the first point of contact for many expectant women, so they play an important role in the care of both mothers and babies [ 33 ]. Thus, the implementation of ultrasound training programs for all healthcare workers, including midwives, is justified. Ultrasound in pregnancy has been shown to be safe and accurate when used correctly by trained healthcare professionals, and it provides valuable information for diagnosing and managing pregnant patients [ 12 , 33 ]. As a result, more ultrasound research studies are required to provide evidence that it is indeed and must be carried out by midwives to promote access to health care for pregnant women and to reduce maternal and neonatal morbidity and mortality in low- and middle-income countries such as South Africa.

Cardiotocography (CTG) has long been recognized by midwives as an effective diagnostic tool during the intrapartum period [ 34 ]. The goal of intrapartum foetal monitoring is to avoid adverse foetal outcomes, so midwives frequently use CTG to make diagnoses during the critical period of labour [ 35 ]. As midwives are the primary caregivers for labouring women, it is critical that they have adequate knowledge of CTG to accurately interpret cardiotocography [ 35 ]. While the study findings indicated that MCH could be improved by training midwives on CTG and ultrasound.

On the other hand, it was revealed that there is a need to collaborate with families and community members to plan health education for the prevention of teen pregnancy. A review of the literature revealed that the use of family health strategies with adolescents should be prioritised, particularly in health education practice, to prevent the issue of adolescent pregnancy, which is still common and relevant today, despite being extensively studied [ 36 ].

Theme 3: Ubuntu mentorship

The study participants believed that older midwives should mentor and educate young midwives on the application of Ubuntu principles. Most nurses are female. Previous study found that a mentorship program based on African values such as compassion, cooperation, and love can provide female academics with better knowledge and skills to compete equally with their male counterparts [ 36 , 37 ]. This mentorship, which incorporates Ubuntu values, may be used by older midwives to mentor young female’s midwives and students in both academic and clinical settings to improve their skills and knowledge of MCH. It is critical to conduct MCH studies to provide evidence-based practices that show Ubuntu could be used as a strategy to improve the standard of care in MCH in low middle-income countries such as South Africa.

Strengths and limitations

There is a notable strength of the study in the use of AI which gave the participants the ability to share and commemorate achievement-based experience of being a member of the MCH subunit under the Ubuntu Community of Nursing model project. Through the workshop discussions, the research captures the experiences of the various members from the different universities and give reflections from different settings. Furthermore, this MCH project enhances community engagement, by encouraging collective participation and involvement in healthcare projects. Another limitation of appreciative inquiry tends to focus on qualitative data, which may make it difficult to quantify the impact and outcomes of the healthcare project. In future the study population may have representatives from all focus subunits.

Study implications and recommendations

The findings of this study recommends that the integration of ubuntu principles in MCH projects may encourage their acceptance and support by the community, leading to improved maternal and child outcomes. Appreciative inquiry projects may also enhance capacity building and empowering healthcare providers and community to collaborate in the project aimed at improving healthcare delivery. The study may also inform policies to prioritize the community involvement and promote cultural relevant practices for MCH. In support some of the projects that were led within the MCH subunit included capacity building for midwives to revive the skills on CTG interpretation. Another project that was facilitated included training the midwives working in local communities on point of care ultrasound in the region. This will help with early detection and referral for effective prevention and treatment of conditions and save lives. Overall, this study strengthens the idea that midwifes need empowerment regarding the incorporation of Ubuntu philosophy in their working environment to improve their attitude and respect towards patients. The study contributes to our understanding of the importance of collaborative relationships between the universities and clinical practice to improve evidence-based practices and promote quality patient care. The study recommends leveraging Ubuntu principles to strengthen MCH services, suggesting that such an approach can led to more sustainable and impactful health improvements in South African communities. More research work is needed to support the integration of Ubuntu philosophy and basic CTG in the curriculum for training midwives.

The use of AI was able to identify the strengths and weakness of the MCH project implemented in one of the South African communities. The study's findings demonstrate that academic researchers were appreciative of how successful and satisfying their transforming experience through the Ubuntu lens was. When it came to writing book chapters, publishing peer-reviewed articles, and locating pertinent high-impact journals, they were capable, self-assured, and empowered. The findings also demonstrated that participants learn how to actively participate in research and engage with the community. By giving midwives the necessary skills, they would be able to take on new responsibilities and make timely clinical judgments that will prevent and minimise obstetrical difficulties while also delivering RMC. Working together as universities and in partnerships with communities to improve the way the Ubuntu Philosophy is integrated.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

• Appreciative inquiry

Cardio topography

Maternal and child health care

Doctor of Philosophy

Respectful maternal care

Sustainable developmental goals

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Indigenous Knowledge system

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Acknowledgements

The researchers would like to acknowledge the members of Ubuntu community model in nursing, maternal and childcare group for participating in the study.

The study was funded by the National Research Foundation (NRF) under the Ubuntu community model in Nursing, South Africa. NRF cost centre is N00696 and the project number is #0531440620.

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S wrote introduction, methodology, layout of manuscript, and reference. N.S wrote introduction, abbreviation list. M.R wrote findings. S.M wrote discussion. R wrote abstract. F.M wrote conclusion. N.V wrote and revised findings and discussion. All authors reviewed the manuscript.

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Nyandeni, S., Raliphaswa, N.S., Musie, M.R. et al. Exploring maternal and child healthcare projects in South African communities through Ubuntu lens: an appreciative inquiry. BMC Nurs 23 , 619 (2024). https://doi.org/10.1186/s12912-024-02267-3

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DOI : https://doi.org/10.1186/s12912-024-02267-3

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