16.5 – Instrument reliability and validity

Introduction

What’s in a measure? We’ve talked about measurement extensively, eg, Chapter 3.3 . As review, a measure is simply the result of some process used to quantify an object or item of interest. Instead of a number, a measure may return a classification: for a given sample unit, the unit may be categorized as meeting the definition and therefore given a “yes” or it does not (“no”), a dichotomous response. The method used to obtain the measurement is called the instrument. An instrument may indeed be an instrument like a sphygmomanometer or a thermocycler equipped with fluorescent optics. Instrument in this context however also includes questionnaires or surveys intended.

In biology and biomedical research, there are thousands of kinds of measurements one has to choose among depending on the question at hand. In many cases choices are straightforward: in morphometrics, the instruments of choice will be lengths and areas and shapes quantified by rulers and application of well-defined geometry equations. Where multiple measurement approaches apply, reliability analysis can help decide which method to use, or, importantly, whether the different approaches agree. For example, Kruse et al (2017) compared ultra sound and magnetic resonance imaging measurements of Achilles tendon cross-sectional area; they found that although both methods were internally consistent, the methods consistently yielded different results.

In other arenas, the choice of instrument will be less clear. For example, doctors use a questionnaire to rank cardiac patients for attention in perioperative care, the care a surgical patient receives from admittance to release from the hospital, to improve patient outcomes. The questionnaire will include a number of questions intended to provide a summary picture of each patient so that if resources are limited, the most at risk patients may get priority. To the extent the questionnaire in fact is a useful discriminant, then the instrument may benefit both hospital and patients.

In conducting measures one selects instruments that provide valid results. That is, provided the instruments are maintained and well-calibrated, use of a sphygmomanometer by a trained technician will return accurate and valid measures of a subject’s blood pressure. Survey questions also can be evaluated for validity although the extent to which survey questions measure what is intended may be more complex. For example, if the intent is to ascertain a subject’s chance (ie, risk) of graduating from college in the next year, how useful would the following question be if administered to a room filled with first-year students?

Survey question: How old are you now?

Simple enough question, but immediately, several questions come to mind. Do we want our responses in years, months, days, hours, minutes, or seconds?  What about for those individuals that know only approximately when they were born, ie, in many parts of the world, registration of birth is irregular. So, we may even wonder about how necessary it is we ask this question of college students in the first place, eg, do we really want to trigger our subjects for a case in which most of our subjects are about the same age?

Perhaps we decide this is important information to ask. When do you start counting? Most Western cultures start the clock at zero when the baby is born. In China and many other Eastern Asian countries, people are born at one. In India, once a person reaches a year plus six months, the person would be considered two years old, whereas in the USA, the person would still be considered one until the second birthday. Thus, depending on the person’s culture identity, responses to this simple question may differ by as much as a year.

Types of reliability

Regardless of instrument, all measures contain error. Hence, even a valid instrument may not return an accurate measure for each subject. The concept of instrument reliability is concerned with error of measurement. Reliability may be defined in at least four contexts:

  • internal consistency
  • inter-rater (also called inter-observer reliability)
  • parallel-forms
  • test-retest

For an instrument to show internal consistency, this implies that the survey has multiple questions that pertain to the same concept or topic, but written in different ways to reveal effects of word choice, for example. Inter-rater reliability refers to an instrument that when used by different observers (eg, science fair judges), the observers give the same or at least consistent scores for the same test. Parallel forms reliability implies that two surveys on, perhaps scientific literacy in high school students, yield the same conclusions even though each survey has  different questions. Test-retest reliability is a straight-forward concept — if the instrument is repeated, are the same scores achieved?

Split-half reliability

Split-half reliability is a method for assessing a measurement tool’s internal consistency by determining if different parts of it produce consistent results. This is done by splitting a single measure (eg, a test or a set of behavioral observations) into two comparable halves, calculating a score for each half, and then finding the correlation between the two sets of scores. For example, the ManyDogs research consortium used split-half design to investigate whether or not dogs consistently interpret human pointing instructions (Espinoza et al 2023). Alternatives to split-half reliability include test-retest reliability, parallel-forms reliability, and inter-rater reliability — see Chapter 12.3, Chapter 14.3 — (Cook and Beckman 2006). These methods assess different aspects of consistency, such as stability over time (test-retest), consistency between two different but equivalent tests (parallel-forms), or agreement between observers (inter-rater). Both ICC and split-half reliability address consistency. The primary distinction is that split-half reliability is a method for assessing the internal consistency of a single test administration, while the Intraclass Correlation Coefficient (ICC) is a more flexible statistical measure used to assess absolute agreement or consistency across different measures, time points, or, most commonly, between multiple raters.

For split-half reliability, a product moment correlation is first calculated between the score of the two halves. A high correlation indicates that all items or observations within the measure are likely assessing the same underlying construct, while a low correlation suggests inconsistency. The correlation coefficient is typically adjusted using the Spearman-Brown prediction formula to estimate the reliability of the original full-length measure.

    \begin{align*} \rho*_{xx'}=\frac{n\rho_{xx'}}{1+\left ( n-1 \right )\rho_{xx'}} \end{align*}

Equation of the Spearman-Brown prediction (or prophecy) formula.

Reliability estimators

In general, correlation-type measures can be used to quantify the extent of reliability (also termed reproducibility or repeatability). The product moment correlation is used to quantify the relationship between two measures where there is clear distinction between the two variables. For example, to quantify the association between body weight and height, the proper correlation to calculate would be the product moment correlation because it is clear that a measure of weight goes with the variable weight whereas height measures goes with the variable height.

But it is less clear which variable should go first in the calculation when you have repeat measures of essentially the same thing. Which goes first, the first observation of sprint running speed over a 100 meters of the second measure of the same person’s performance? Logically, we may say take the first as the X variable and the second as the Y variable, but there is no mathematical justification.

For a more challenging example, consider measures of body mass on male and female birds that are mated, and the researcher wants to assess whether there is a correlation between male and female weight — which variable goes first in the analysis, male weight or female weight? In such cases the intraclass correlation coefficient may be used (Chapter 12.3, Chapter 16.4). The intraclass correlation can be estimated as the ratio of the variance of interest over the sum of the variance of interest plus the variance error. Interest in the case of sprint running would be the two (or more) trials or for the bird weights the variable of interest is weights of male and female birds within a mated pair. The formula for ICC in this context is given by

    \begin{align*} \rho = \frac{\sigma_{B}^2}{\sigma_{B}^2 + \sigma_{e}^2} = \frac{MS_{B} - MS_{e}}{MS_{B} + \left (k - 1 \right ) MS_{e}} \end{align*}

where k is the number of repeat measures and MS refers to the mean squares from the one-way ANOVA and B and e refer to between (among) subjects and e is the error or within subjects variability.

Cronbach’s alpha is a reliability measure that quantifies the internal consistency of items in a survey or instrument by calculating the average among these items. Cronbach’s alpha will tend to increase as the intercorrelations among test items increase, and in this sense can be taken as an internal consistency estimate of the reliability of test scores.

    \begin{align*} Chronbach's \ \alpha = \frac{k}{k - 1}1 - \frac{\sum_{i = 2}^{n} s_{i}^2 }{s_{T}^2} \end{align*}

where k is the number of items, si2 is the variance of the i-th item, and sT2 is the variance of the total score after summing all items.

Cronbach’s alpha is one of the oldest measures, and at least in part because of how long ago it was introduced, is very common as a measure of reliability. However, there are other estimators and in some aspects these perform better than Cronbach’s alpha.

Reliability statistics like Cronbach’s alpha are available in the R package psych. See also R package agRee.

Example. Judging of posters

library(psych)
alpha(myJudge)

Output from R

Reliability analysis 
Call: alpha(x = myJudge)

raw_alpha std.alpha G6(smc) average_r S/N   ase  mean   sd median_r
     0.74      0.74   0.59       0.59 2.9  0.21   2.8  1.1     0.59

lower alpha upper 95% confidence boundaries
0.33 0.74 1.15

Questions

1. Write up three learning outcomes for this page. Hint: Point your favorite generative AI to this page and ask for help.

2. A team of behavioral ecologists designs a new test to measure problem-solving ability in domestic dogs. Each dog is given a puzzle box, and the experimenters record the time taken to retrieve a treat and number of errors made. They want to ensure that their measurement instrument is reliable before drawing conclusions about individual differences in cognition.

Question: Describe how you would test the reliability of this instrument. Which type(s) of reliability would be most appropriate (split-half, test–retest, parallel-forms, inter-rater), and why?

Quiz Chapter 16.5

Instrument reliability and validity

Chapter 16 contents

5 – Experimental design

Beyond numbers.

During this course, you will learn about statistics, yes, but my hope and goal for your experience in this class is much more than that: statistical reasoning. To have statistical reasoning skills, you need to be comfortable with the context of how data are acquired, ie, data acquisition. It’s not just about knowing how an instrument performs, eg, under or over the limits of quantification characteristics of the instrument lead to censored missing values, missing not at random (MNAR). In a broad stoke view, data are obtained from two kinds of studies: observational studies and experimental (manipulative) studies. what a correctly designed experiment can tell you about the world, and how a poorly designed experiment works against you. At the end of the semester, you should be familiar with the issues of Randomization, Control, Independence, and Replication.

The basics explained.

Experimental design is a discipline within statistics concerned with the analysis and design of experiments. Design is intended to help research create experiments such that cause and effect can be established from tests of the hypothesis. We introduced elements of experimental design in Chapter 2.4. I recommend a quick re-read: We talk about the different types of bias that can affect research. We also point out parts of the problem you’ll need to think about when planning a project, so you can avoid or reduce bias from the start.

Note 1: Just the highlights from Chapter 2.4: use randomization, blinding, and control measures in the study design. Different strategies may be needed depending on the type of bias.

Here, we expand our discussion of experimental design. We begin with review of definitions and an outline of an important concept in statistics. First, we need to be clear about why we measure or conduct experiments. We do so to collect data (datum is the singular) about a characteristic or trait from a population. Data are observations: they include observations and measurements from instruments. At their best, data are the “facts” of science.

Measurement, Observation, Variables, Values.

Measurement is how we as scientists acquire our data (Chapter 3.4). The process of measuring involves the assignment of numbers, codes, or labels to observations according to rules established prior to any data collection (Stevens 1946, Houle et al 2011). Observations refer to the units of measurement, whereas variables are the characteristics or traits that are measured. A value refers to the particular number, score, or label assigned to a particular sample for a variable. Variables are generic, the thing being measured, values are specific to the subject or sample being measured for the variable. Each discipline in biology has its own set of variables and samples may or may not have different values for each variable measured. Variables are summarized as a statistic (eg, the sample mean), which is a number taken to estimate a parameter, which pertains to the population. Variables and parameters in statistics were discussed in Chapter 3.4. Because numbers or scores or labels can be assigned according to different rules, this means that variables may be measured on different kinds of scales or data types. The different kinds of data types were presented in Chapter 3.1.

Missing values.

A missing value refers to lack of a value for an observation or variable (Chapter 3.5).  Censored values, a type of missing value, include observations for which limited or partial information is available, eg, range, while a missing value lacks any information about the true value.

Missing values can affect analysis and many R algorithms are sensitive or may fail to run in the presence of missing values. Missing data may be of three kinds, and one of them, missing not at random (MNAR), can substantially influence the analysis. MNAR implies some observations are missing because of a systematic bias. Instrument limits of quantification are an example of systematic bias — for example, spectrophotometric absorbance readings of zero for a colorimetric assay (eg, Bradford protein assay) may not represent complete absence of the target, but rather, the lower detection limits of the instrument or the assay method (0.1 mg protein/mL in this case).

The other kinds of missing values are missing completely at random (MCAR) and missing at random (MAR). MCAR implies there is no association between any element of the experiment and the absence of a value. Analysis on MCAR data sets may support unbiased conclusions. MAR includes the random errors that occur during data acquisition: data may be lost because of operator error or power outage (shudder). Unlike MNAR, analysis of MAR, like MCAR data sets may still result in unbiased conclusions.

Note 2: The word “may” is doing some heavy lifting here; obviously, the size of the data set influences whether this claim holds.

One consequence of missing values — a decision how best to handle the lost information. The options are either pairwise deletion or listwise deletion. Listwise means the entire row, all variables, are lost for any record with one or more missing values. You recorded root length, stem length, numbers of secondary roots, time to germinate, but the SPAD meter failed and there is no chlorophyl reading for seedling #212? Sorry, the entire row is deleted. Optionally, the missing data is deleted pairwise — only chlorophyl reading for #212 is lost, all other variables remain.

Users of R will quickly learn some algorithms immediately fail to run if not instructed how to process missing values. In some cases, missing values can be replaced by imputed values.

Note 3: This being an introduction to applied biostatistics, we won’t dwell on the pros and cons of imputation. Instead, I point interested readers to two heavily cited articles: Schafer 1999; Schafer and Graham 2002.

Cause & Effect.

Observations or measurements gathered under controlled conditions (experiments) are essential if we are to answer questions about populations, to separate cause and effect, where one or more events is directly the result of another factor, as opposed to anecdote, a story about an event which, by itself, cannot be used to cause from association (eg, spurious correlations, see Ch 16.2). Recall that anecdotal evidence typically comes from personal experience, where observations may be obtained by non-systematic methods. Well designed experiments, in the classical sense, permit discrimination among competing hypotheses in large part because observations are collected according to strict rules. Well developed hypotheses tested by well-designed experiments permit ruling out alternative explanations (sensu Pratt [1964] strong inference). Observational studies, or epidemiology studies if we are talking about investigations of risk assessment, also may contribute to discussions of cause and effect (see history of smoking by Doll 1998).

Medicine is replete with stories about how a patient showed a particular set of symptoms, and how a physician applied a set of diagnostic protocols. An outcome was achieved, and the physician reports the outcome and circumstances related to the patient to her colleagues. This is an example of a case study, and the focus of investigation is the individual, the patient. The doctor’s report will sound like, “I tried the standard treatment given the diagnosis, and the patient’s symptoms diminished, but later returned. I tried a higher dose, but the patient’s symptoms persisted unabated.” No inferences are made to a wider set of individuals and the report is anecdotal. If observations are made on several patients, this may be a case series.

In ecology, a field biologist may notice a six-legged adult frog (Scott 1999, Alvarez et al 2021) — since frogs typically have four legs, the six-legged frog attracts the biologist’s eye and he jots down the circumstances in which the frog were found: relative humidity, air temperature, ground temperature, where the frogs was found (on lower leaf of a philodendron plant). A water source near where the frog was found is tested for pH with a meter the biologist carries, and a water sample is taken for later testing of herbicides. The frog is collected so that it can be checked for skin parasites. Upon further inspection, the frog did indeed have parasites known to cause deformities in other frog species. However, note that this example too, is a case study. Although the biologist makes additional observations, any conclusions about why the frog has six legs is anecdotal.

From these case studies, no conclusions can be drawn. We cannot say why the patient failed to respond to treatment, nor can we say why the frog has six legs. Why? Because these are singular events, and a variety of explanations can be given as to their causes — importantly, no controls are available, so there’s no way to distinguish among possibilities.

From such anecdotes, however, experiments can be designed. The physician may decide to recruit additional patients with the diagnosed illness and apply the standard treatment to see if her anecdote is a single, unique event, or more indicative of a problem with the treatment. The biologist may collect other frogs from the area near where he found the six-legged frog and check to see if they, too, have the parasites. If additional patients fail to respond to the treatment, then the singular even is more likely to be a phenomenon. If the normal frogs also have similar levels of the parasite then it is unlikely that these parasites caused the malformed frog. With this simple step (recruiting similar patients, finding additional frogs), we can begin to make inferences about cause and effect and in some cases, to generalize our findings.

This is the objective of most statistical procedures, the concept of sampling from a reference population and making distinctions between groups within the sample. The difference between observational and experimental studies then is how the subjects are selected with respect to the groups. In an experiment, the researcher controls and decides which subject receives the treatment, therefore, allocation to groups is manipulated by the researcher. In contrast, subjects included in an observational study have already been “assigned” to a group, but not by us. Assignment to groups, smokers or non smokers, Type II diabetes or no diabetes (etc.), is done by nature.

Now, I do not wish to imply that research that cannot be generalized back to a reference population are worthless. Far from it. In fact, there is a strong argument for specificity. For example, much basic biological research depends on work or model organisms, which in turn may be further partitioned into specific genetic lines (cf. discussion in Rothman et al 2013). And my goodness, what we have learned about the devastation to oceanic islands like Guam when the brown tree snake was introduced (Fritts and Rodda 1998).  Strictly speaking, what has happened to Guam is a case history. But no one would argue that what has happened to Guam cannot happen to Hawaii and other oceanic islands (eg, United States Federal law 384-108 “Brown Tree Snake Control and Eradication Act of 2004”). In other words, even from case histories, generalizations can sometimes be made.

There can also be real reasons to ignore the issue of generality. One benefit of specificity is experimental control. Transgenic lines may differ by single gene knockout or by gene duplication, and clearly the aim of such studies is to evaluate the function (hence purpose) of the gene product (or its absence) on some phenotype. In this sense it may not seem important that an transgenic mouse is not representative of a wild outbred mouse population. However, this argument is fundamentally one of expedience — such studies do result in specific results, results that cannot be generalized beyond the strains involved. It ignores the issue of genetic background — all of the genes that affect a trait in addition to the candidate gene under study (Sigmund 2000; Lariviere et al 2001). Transgenic mice of different inbred strains or their hybrids may have very different alleles at other genes that may influence a phenotype, hence, the results of the gene knockdown or other engineering result in different outcomes. Results of genetic manipulations on inbred strains, no matter how sophisticated, the conclusions are strain- or hybrid cross-specific. Thus, although technically and financially difficult, conclusions are better, more generalizable, when conducted with many different inbred lines and verified in outbred mouse populations precisely because genetic background often influences function of single genes (Sigmund 2000; Lariviere et al 2001).

“Causal criteria:” Logic of causation in medicine.

This section is in progress. Just a list of key points and references

Throughout this text, emphasis on the power of experimentation is emphasized. Well designed experiments …

Henle-Koch’s postulates (1877, 1882), working on tuberculosis reported a set of causal criteria to establish link between a microorganism and a disease, are the following:

  • The microorganism must be found in abundance in all organisms suffering from the disease, but should not be found in healthy organisms.
  • The microorganism must be isolated from a diseased organism and grown in pure culture.
  • The cultured microorganism should cause disease when introduced into a healthy organism.
  • The microorganism must be reisolated from the inoculated, diseased experimental host and identified as being identical to the original specific causative agent.

Robert Koch wrote these more than 100 years ago, so, clearly, understanding of infectious disease has improved. Evan’s postulates, quoted from A Dictionary of Epidemiology, 5th edition (pp. 86-87).

  1. Prevalence of the disease should be significantly higher in those exposed to the hypothesized cause than in controls not so exposed.
  2. Exposure to the hypothesized cause should be more frequent among those with the disease than in controls without the disease—when all other risk factors are held constant.
  3. Incidence of the disease should be significantly higher in those exposed to the hypothesized cause than in those not so exposed, as shown by prospective studies.
  4. The disease should follow exposure to the hypothesized causative agent with a normal or log-normal distribution of incubation periods.
  5. A spectrum of host responses should follow exposure to the hypothesized agent along a logical biological gradient from mild to severe.
  6. A measurable host response following exposure to the hypothesized cause should have a high probability of appearing in those lacking this before exposure (eg, antibody, cancer cells) or should increase in magnitude if present before exposure. This response pattern should occur infrequently in persons not so exposed.
  7. Experimental reproduction of the disease should occur more frequently in animals or humans appropriately exposed to the hypothesized cause than in those not so exposed; this exposure may be deliberate in volunteers, experimentally induced in the laboratory, or may represent a regulation of natural exposure.
  8. Elimination or modification of the hypothesized cause should decrease the incidence of the disease (eg, attenuation of a virus, removal of tar from cigarettes).
  9. Prevention or modification of the host’s response on exposure to the hypothesized cause should decrease or eliminate the disease (eg, immunization, drugs to lower cholesterol, specific lymphocyte transfer factor in cancer).
  10. All of the relationships and findings should make biological and epidemiological sense.

Fredericks, D. N., & Relman, D. A. (1996). Sequence-based identification of microbial pathogens: a reconsideration of Koch’s postulates. Clinical microbiology reviews, 9(1), 18-33.

Correlation (association) does not imply causation, a well-worn truism in any application of critical thinking skills. Conversely, causation does imply association (correlation); If a change in one variable directly changes another variable, there will be a statistical association between the two variables. Thus, a correlation between two variables is necessary, but not sufficient evidence for cause and effect between two variables.

Note 4: Association is the more general term for a possible relationship between two or more variables. A correlation in statistics generally refers to a linear association (Chapter 16); the aforementioned truism should be restated as association (or statistical relationship) does not imply causation.

However, sometimes association does point to a cause. A familiar example is association between tobacco cigarette smoking causes lung cancer.  Surgeon General Luthar Terry’s 1964 report (link to document in National Library of Medicine), presented a strong case linking smoking to elevated risk of lung cancer and coronary artery disease.

Bradford Hill’s guidelines to evaluate causal effects based on epidemiology (Hill 1965, see also Sussar 1999, Fedak et al 2015). Set of necessary and sufficient conditions, inductive reasoning.

  1. Strength of association
  2. Consistency of observed association
  3. Specificity of association
  4. Temporal relationship of the association
  5. Biological gradient, eg, a dose-response curve
  6. Biological plausibility
  7. Coherence, the cause and effect inference should not conflict with what is known about the etiology of a disease.

Follows and extends David Hume’s (1739) causation criteria: association (Hill #1), cause precedes effect (Hill #4), direction of connection.

Validity.

An obvious objective of research is to reach valid conclusions about fundamental questions. A helpful distinction between the specific and the generalizable experiment is to recognize there are two forms of validity in research: internal validity and external validity (Elwood 2013). Internal validity is the quality of a designed study that determines whether cause and effect can be determined. Random assignment of subjects to treatment groups enhances the internal validity of the study. External validity relates to how general the assessment of cause and effect can be to other populations. Thus, random sampling from a reference population has to do with whether or not the study has external validity.

Additional definitions.

We proceed now with definitions. We use the term population in a special and restrictive way in statistics. Our definition includes the one you are already familiar with, but it also means more than that.

Populations are the entire group of individuals that you want to investigate. In statistics, the entire groups is actually the entire class with the observation — so if we are referring to the average body weight of house mice, we’re actually referring to the body weight as the population — it’s a subtle distinction, not essential for our introduction to biostatistics. When we conduct experiments and apply statistical tests on collected data, we generally intend to make inferences (draw conclusions) from our results back to the population.

Population has a strict application in statistics, but the definition also includes our general understanding of the word population. For example, Examples of a population in the general sense one may refer to include: .… the entire human population existing today .… the entire collection of U.S. citizens. .… all the individuals in an entire species. .… all individuals in a population of a species (eg, house mice in a dairy barn in Hawai’i) .… all of us in this classroom (if we are ONLY interested in US) If you could measure the entire population then there would be no need to do (or learn) statistics! Populations usually are in the thousands, millions, or billions of individuals. Here, population is used in the everyday sense that we think of — a collection of individuals that share a characteristic.

A more formal definition of “population” in statistics reads as follows: A statistical population is the complete set of possible measurements on a trait or characteristic corresponding to the entire collection of sampling units for which conclusions are intended.

To conclude, in this class, when we talk about population, we will generally be using it in the everyday sense of the word. However, keep in mind that the definition is more restrictive than that and the key is to identify what sampling units are measured.

Conclusions.

This is only the beginning, the basics of experimental design. Beginning with RA Fisher‘s 1935 book, The Design of Experiments (nine editions, some freely accessible via internet search), which is still very much worth reading, entire books are written on the subject, as you can well imagine.

Note 5: Fisher’s The Design of Experiments concerned experimental design, randomization, blocking, and replication, particularly from perspective of agriculture research. Most readers of this eBook likely know that RA Fisher’s broader career absolutely included eugenics and hereditarian views about human populations and race. Those views are not in The Design of Experiments, which is largely about methods and statistical framework for inference.

We will also return to the subject of Experimental Design throughout the book. We will return to random sampling in Chapter 5.5. Next we discuss distinctions between experiments and observational studies with respect to sampling of populations.

A bit of a disclaimer here before proceeding; while I cite several papers for examples in experimental design in Chapter 5, reader’s should not read into this that I am either criticizing or endorsing the published experiments. Experimental design will always have elements of compromise — the trick of course is knowing which choices influence validity (Thompson and Panacek 2006).

Questions.

  1. Define in your own words the following terms
  • reference population
  • subjects
  • specific versus general conclusions
  • random sampling
  • convenience sampling
  • haphazard sampling
  • research validity
  1. Revisit our cell experiment, “What is the sampling unit in the following cell experiment?” How would you change this experiment so that there will be biological and not just technical replication?
  2. Describe the type of sampling for each research scenario described?
  • All African snails on a staircase at Chaminade University are collected on a Thursday evening.
  • All African snails on a staircase at Chaminade University are collected every Thursday evening for six months.
  • All African snails on all staircases at Chaminade University are collected.
  • African snails are studied in the lab, then returned to the areas from which they were collected. Days later, the researcher collects snails from the same area.
  • African snails are studied in the lab, then returned to the areas from which they were collected. Days later, the researcher collects snails from a different area.
  • The Chaminade University campus is divided into grids. Grids that include stairwells are marked. Before collecting snails, the researcher randomly selects from the list of grids and searches for snails only in those grids selected from the list.
  • Giant African Snail (Lissachatina fulica, formerly Achatina fulica). Image by M. Dohm
    Figure 1. Giant African Snail (Lissachatina fulica, formerly Achatina fulica). Image by M. Dohm.
  1. This next scenario may be evaluated by you for potential sources of bias. Review the list of bias listed above. A researcher wants to do a population count of feral cats on campus. Feral cats are active at night, so he decides to set up a feeding station near a light post. The researcher sits all night in a parked car yards away and watches the feeding station for visits by cats. The researcher repeats these observations over the course of a week, moving the feeding station to different campus locations each night, and reports the total number of cats seen during the week as an estimate of the population size. Be able to discuss this study in terms of potential and actual bias.

Quiz

Including the quiz on this page, a total of 65 questions among the several subchapters, a mix of true or false and multiple choice question format.

Quiz Chapter 5

Experimental design

Chapter 5 contents