example of verbal interpretation in thesis

Chapter 22: Using Interpretation to Develop Thesis

Part 4: chapter 22.

A n assertion differs from an interpretation by providing perspective on an underlying pattern, a perspective that implies what it means to you and why you think it’s significant. Without such a perspective, an interpretation merely becomes a statement with no potential for development. Just as one might utter a statement that kills the mood of a particular situation (“What a romantic dinner you cooked for me! Too bad I’m allergic to lobster and chocolate…”), one can make statements that block any possibility for further analysis. What follows are some of the most common examples, introduced in A Guide to Perspective Analysis , that limit further analysis::

Statements of Fact

Factual statements might help support an analysis but should not be the main force that drives it. You might notice that Vincent Van Gogh used twenty-five thousand brush strokes to create Starry Night , that global warming has increased more rapidly in the polar regions, or that Alfred Hitchcock used erratic background music throughout his film Psycho . But what else can you say about any of these statements? They are simply true or false. To transform these factual statements into assertions that can be explored further, you need to add your own perspectives to them. For instance, you could argue that the erratic music in Psycho underscores the insanity of the plot and results in a cinematic equivalent to Edgar Allen Poe’s frantic short sentences,

Alone by Edgar Allan Poe, 1875 From childhood’s hour I have not been As others were — I have not seen As others saw — I could not bring My passions from a common spring — From the same source I have not taken My sorrow — I could not awaken My heart to joy at the same tone — And all I lov’d — I lov’d alone — Then — in my childhood — in the dawn Of a most stormy life — was drawn From ev’ry depth of good and ill The mystery which binds me still — From the torrent, or the fountain — From the red cliff of the mountain — From the sun that ’round me roll’d In its autumn tint of gold — From the lightning in the sky As it pass’d me flying by — From the thunder, and the storm — And the cloud that took the form (When the rest of Heaven was blue) Of a demon in my view —

or that global warming in the polar regions will result in higher sea levels that will cause enormous damage if we don’t do anything to keep it in check.

Statements of Classification

Analysis requires more than simply asserting that your focus or topic fits into a pre-established category like “modernism,” “impressionism,” “neo-conservativism,” or “first wave feminism.” Of course it can be useful to understand the nature of these broader categories, but you still need to explore why it is important to see your subject in this light. For instance, rather than simply point out that Family Guy can be seen as a satire of the American family, you should also consider what this perspective reveals about the show’s development and reception. It might also be worthwhile to consider how a work transcends the standard notions of its period or genre. You might point out that while most of the time the Family Guy characters are depicted as broad and ridiculous, they can sometimes act in ways that are familiar and endearing, which helps the audience connect to them. Similarly, when looking at a policy or argument, you should not simply categorize it as belonging to a particular social attitude or political party, but consider it on its own merits. Though political pundits often use terms associated with their opposition as curse words and summarily dismiss anything they advocate, you want to appear much more reasonable in an academic analysis.

Statements of Taste

An analysis is not merely a review that states how you feel about a piece or dismisses an argument or policy as being “distasteful.” A good assertion will not only reveal how you feel about the focus of your analysis, but it should also also inspire you to explore why it makes you feel that way. In her article, “ Babe, Braveheart and the Contemporary Body,” Susan Bordo, Professor of Media Studies, explains that the reason she liked the film Babe better is that it shows the need for self-acceptance and connection to others in a society that overly values conformity and competition (Susan Bordo. Twilight Zones: The Hidden Life of Cultural Images from Plato to O.J. Berkeley, CA: University of California Press, 1999). This assertion allows her to explore different aspects of contemporary American culture that may have inspired each of these films. Had she simply stated her opinion without stating why her subject, the films, made her feel this way, her article would not have been as compelling or convincing.

Babe, Braveheart and the Contemporary Body

Statements of Intention

When looking at creative works, we often want to assert that our point of view is the one the author intended, yet when we equate our perspective with the author’s, we (rather arrogantly) assume that we have solved the mystery of the piece, leaving us with nothing more to say about it. And even if we can quote the author as saying “I intended this,” we should not stop exploring our own interpretations of what the piece means to us. John Lennon tells us that his song “Lucy in the Sky with Diamonds” was written in response to a drawing given to him by his son, Julian. Others suspect that his real intention was to describe a drug trip brought about by LSD, the initial letters in the words of the title of the song (John Lennon and Paul McCartney. “Lucy in the Sky With Diamonds,” Sgt. Pepper’s Lonely Hearts Club Band . Apple Records, 1967). Most people have never seen his son’s drawing, and even more have never tried psychedelic drugs, so neither interpretation works for them. Many people love the song because it guides them through a kind of Alice in Wonderland fantasy of “looking glass ties” and “tangerine trees.”

To be able to show why a given interpretation matters to us, we should not phrase our assertions as being about what we think the author intended but what it causes us to consider.

Likewise you should be careful to avoid simply stating that you know the “real intentions” behind a work of non-fiction, a social policy, or a particular action or decision. For example, consider if a business decides to move its operations overseas to save money. This may inspire some to say that the company’s real intention is to destroy the American economy or to exploit workers overseas, but it would sound far more persuasive and reasonable to actually show how these concerns could come about, even if they were never the stated intentions.

Worthwhile Assertions

In short, worthwhile assertions should reveal a perspective on your subject that provides possibilities for further exploration. Statements based on facts, classifications, opinions, and author intentions provide only inklings of perspectives and should be revised to inspire more prolific and meaningful analysis. Once you come up with some initial interpretations of your subject, reconsider it in light of what it means to you, perhaps by asking some or all of the following questions:

What immediate memories does the subject spark?

How does it cause you to react emotionally and intellectually?

What personal decisions/relationships does it cause you to ponder?

What social, political, or intellectual concerns does it make you consider?

How does it confirm or contradict your morals and beliefs?

Questions like these will help you to reflect on the subject further, enabling you to transform the aforementioned problematic statements into meaningful assertions. This is a great time to write down your responses; you may appreciate looking back at your initial ideas later in the drafting process. Now, consider how the following interpretation,

“The CEO is moving his company’s operations overseas because he hates America and wants to exploit the workers of the third world”

can be revised:

“Though the CEO’s stated intention for moving the company’s operations overseas is to save money, the end result could be disastrous for both the local economy and the new country’s employees who will have to work under unsafe conditions.”

Similarly, the statement

“John Lennon’s real intention in writing ‘Lucy in the Sky with Diamonds’ is to promote the use of LSD”

“Whatever John Lennon’s real intention, I see ‘Lucy in the Sky with Diamonds’ as being about the power of the imagination to transcend the deadening routine of daily life.”

For this reason, you do not always need to state your thesis as a definitive argument that shows how you feel in no uncertain terms. Instead, it is often desirable to show your ambivalence about your position as long as you are clear about why you feel this way. For example, you might feel uncertain as to whether your school should build a new football stadium. Although you might think the money could be spent on more pressing educational needs, you might also want to have a more safe and comfortable place to watch the games. You can discuss the advantages and disadvantages of such a proposal, making it clear that you haven’t yet decided which side to support. Some of the most intriguing essays are exploratory, highlighting the mysteries of a subject, rather than persuasive, trying to convince us of a particular point of view.

Developing a Thesis from an Assertion

While a thesis does not need to be limited in terms of argument, it should be limited in terms of scope. Perhaps the most common mistake students make is to choose a thesis that encompasses too many aspects of the subject. Remember that it is almost always better to write “a lot about a little” than “a little about a lot.” When you discuss too many aspects of your subject, it becomes difficult to provide any new perspectives. Challenge yourself to write about an aspect of your subject that may appear too small to inspire even a page response. Then think about the nature of your perspective a bit further, putting it to the following tests before you put too much more time into it.

The Evidence Test

Before engaging in further analysis, look again at your subject and ask yourself, “Is there enough evidence here to support my point of view?” If you were to write about the film Office Space as showing just how much employees love to go to work in the Tech Industry, you might have a difficult time finding enough scenes to match your perspective. You should also research the details surrounding your subject to see if your assertion needs to be modified, for instance, by considering the historical circumstances that were in place at the time the event happened or the piece was created. One student, when writing about the speech from The Tempest , (quoted in Chapter 19), wrote that when Prospero’s actors disappear into “thin air,” they must have been projected on film with the camera suddenly switching off.

Of course, Shakespeare could not have had that in mind given that he wrote three hundred years before we had the technology to carry this out. Still, one could argue that the scene might best be performed this way now. If a statement cannot be justified or at least modified to match the evidence, then you may have even more problems with the next category.

The Explanation Test

Oftentimes when there isn’t enough evidence to support a thesis, writers will be accused of stretching their explanations. For example, a speaker suggested that technicians assigned terms associated with women to parts of the computer in order to give themselves an illusion of control can be considered a stretched explanation. Some of the assertions can be supported—for instance, that “mother” in motherboard shows how men may want to recall/dominate the nurturing figure of their childhoods. However, when the speaker pointed out that the “apple” in Apple Computers recalls the forbidden fruit that Eve handed to Adam, I started to squirm. The speaker even tried to argue that the name Macintosh was chosen because it’s a “tart” apple, and “tart” is a derogatory term that men use to refer to women of ill repute. Nonetheless, most instructors would rather see an analysis that focuses too heavily on evidence than an analysis with an explanation that isn’t even necessary because the thesis is so obvious: “Othello reveals the destructive consequences of jealousy,” or “Beavis and Butthead’s stupidity often gets them into trouble.” Ideally, the assertion should require some explanation of the relevant details within or directly implied by the thesis. Remember that the goal is not to come up with an answer to the question “what’s THE meaning of the piece?” but rather to explore dimensions of the subject that do not have definitive answers, allowing you to consider your own subjectivities.

The Significance Test

You should also try to avoid wasting time on a thesis that does not have any significance by applying what many teachers call the “so what?” test.

If your assertions do not lead to a deeper consideration of any of the questions raised earlier, then it probably will be boring for both the writer to write and the audience to read. Oftentimes, to make an assertion more interesting, we simply need to add more to it.

Asking the question “so what” will help your thesis become clearer, nuanced, and unique. In addition, it will allow your research questions (discussed in-depth in chapter 30) to become more precise and fruitful as you compare and contrast your points of view with those of others. Remember that the goal of a careful examination should not be to arrive at the same conclusions and have the same thoughts as everyone else. If we all came to the same conclusions when looking at a subject, then there would be no reason to write a new essay on it. Your instructor likely wants you to explain what you think about a topic instead of only presenting opinions that have already been stated by someone else.

Developing a perspective that is both unique and worthwhile takes time, and although carefully examining a piece may help you to form an initial understanding and lay the cornerstone for your analysis, you still need to build the rest of the essay. In the next chapter, we’ll look at ways to do this, first by helping you to explain more thoroughly how you arrived at your perspective and second by helping you to explore the significance of your perspective in a manner that moves beyond the most obvious lessons.

Adapted from “Chapter 3” of A Guide to Perspective Analysis , 2012, used according to creative commons CC BY-SA 3.0 US

Sometimes, often, and always: Exploring the vague meanings of frequency expressions

• Published: 07 July 2011
• Volume 44 , pages 144–157, ( 2012 )

Cite this article

• Franziska Bocklisch 1 ,
• Steffen F. Bocklisch 2 &
• Josef F. Krems 1  

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The article describes a general two-step procedure for the numerical translation of vague linguistic terms (LTs). The suggested procedure consists of empirical and model components, including (1) participants’ estimates of numerical values corresponding to verbal terms and (2) modeling of the empirical data using fuzzy membership functions (MFs), respectively. The procedure is outlined in two studies for data from N = 89 and N = 109 participants, who were asked to estimate numbers corresponding to 11 verbal frequency expressions (e.g., sometimes ). Positions and shapes of the resulting MFs varied considerably in symmetry, vagueness, and overlap and are indicative of the different meanings of the vague frequency expressions. Words were not distributed equidistantly across the numerical scale. This has important implications for the many questionnaires that use verbal rating scales, which consist of frequency expressions and operate on the premise of equidistance. These results are discussed for an exemplar questionnaire (COPSOQ). Furthermore, the variation of the number of prompted LTs (5 vs. 11) showed no influence on the words’ interpretations.

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Since the 1960s, researchers in different scientific areas have sustained an interest in studying the relationship between verbal and numerical expressions—particularly, probability words and quantifiers (Bocklisch, Bocklisch, & Krems, 2010 ; Dhami & Wallsten, 2005 ; Lichtenstein & Newman, 1967 ; Teigen & Brun, 2003 ). Moreover, expressions of intensity or frequency of occurrence (e.g., sometimes or often ) are of interest with regard to their wide application in questionnaires. Several studies consistently showed that people prefer to use words instead of numbers to indicate their opinions and uncertainty (e.g., Wallsten, Budescu, Zwick, & Kemp, 1993 ). Even experts such as doctors or lawyers frequently use qualitative rather than quantitative terms to express their beliefs, on the grounds that words are more natural and are easier to understand and communicate. Words are especially useful in most everyday situations when subjective belief or uncertainty cannot be precisely verbalized in quantitative terms. Therefore, while it may be more natural for people to use language to express their beliefs, it is also potentially more advantageous to use numerical estimates: Their standard interpretation renders them easily comparable, and they form the basis of calculations and computational inferences. Accordingly, many researchers have developed translation procedures (e.g., Beyth-Marom, 1982 ; Bocklisch et al., 2010 ; Budescu, Karelitz, & Wallsten, 2003 ) and have established numerical equivalents for common linguistic expressions (for a broader literature review, see Teigen & Brun, 2003 ). One outcome of these efforts is that linguistic terms have often been conceptualized as fuzzy sets and mathematically described using fuzzy membership functions (MFs; Budescu et al., 2003 ; Zadeh, 1965 ; Zimmer, 1984 ).

Figure  1 shows an example of the fuzzy MF for the linguistic term probable reported by Bocklisch et al. ( 2010 ). The function’s shape and position represent the vague meaning of probable on a 0–1.0 probability scale. The numerical probabilities occurring between approximately P = .6 and P = .75 show the highest membership values and, therefore, are most representative and describe the meaning of probable best. Because the vague linguistic term has no sharp boundary, the membership values for the other numerical probabilities decrease continuously from the function’s peak. Hence, they are less representative of the meaning of probable .

Example of a fuzzy membership function for the linguistic term probable (see Bocklisch, Bocklisch, & Krems, 2010 )

The two studies presented herein support the objectives of our article. First, we present a general two-step procedure for the translation of linguistic expressions into numbers and show that this is a methodological innovation. To this end, in study 1, we outline the method exemplarily for verbal frequency expressions. Second, we apply the procedure to the field of verbal rating scales and, thereby, test and construct scales with nearly equidistant response categories. In study 2 we use the verbal response scale of the Copenhagen Psychosocial Questionnaire (COPSOQ; Kristensen, Hannerz, Høgh, & Borg, 2005 ) as an example. In the Conclusions section, we summarize and outline implications of our results, which include recommendations for the construction of verbal rating scales. Additionally, we discuss interesting future prospects using fuzzy methodology.

Translation procedure as a methodological innovation

The translation procedure is composed of (1) a direct empirical estimation method that yields data from participants who assign numbers to presented words and (2) a fuzzy approach for the analysis of data resulting in parametric MFs of potential type (Bocklisch & Bitterlich, 1994 ). Our method differs from existing approaches, and the proposed MF type offers advantages over other MF concepts. First, the direct estimation method is very frugal, efficient, and easy to use for yielding empirical data from decision makers. Moreover, our method conserves resources (e.g., as compared with Budescu et al., 2003 ) because only three numbers per verbal expression are required for estimation. In our opinion, this is an important criterion regarding potential fields of application (such as medicine) where expert knowledge is crucial but difficult to obtain or expensive. In contrast, Budescu and colleagues proposed a multistimuli method where participants viewed one phrase and 11 probability values (0, .1, . . . , .9, 1) and then judged the degree to which the phrase accurately described each probability. Thus, while these judgments were used to create individualized MFs, they were only partly defined according to the 11 numerical probability values reported by participants. Second, our parametric MFs are defined for a sample or specific population so that a generalized model for the vague linguistic expressions that are suitable for a group of people is obtained. It is a well-known fact that the interindividual variability of estimates is large (Teigen & Brun, 2003 ). Therefore, if group MFs are fitted, it is necessary to consider variability and potential contradictions in the estimation behavior of participants. The presented MF approach takes this into account by using parameters (see the Method section). Furthermore, we argue that continuous modeling of group MFs of verbal expressions is useful in that it serves as a flexible basis for further calculations. Additionally, such modeling is easily implemented in a variety of existing models or applications, such as decision support systems (Boegl, Adlassnig, Hayashi, Rothenfluh, & Leitich, 2004 ).

In Bocklisch et al. ( 2010 ), the suggested translation method was outlined for verbal probability expressions (e.g., probable ). The proposed general procedure can be broadly applied to other linguistic terms. In this article, we present the results of two studies. Study 1 included 11 expressions indicative of frequency of occurrence (e.g., occasionally ) with regard to the potential interest of different research areas and applications that apply verbal rating scales with frequency expressions. After presenting the method, results are discussed with respect to the selection of frequency terms considered appropriate for verbal rating scales in questionnaires. Study 2 employed the translation procedure to explore the COPSOQ response scale in more detail.

Application in verbal response scales

In psychology and the social sciences, many research questions are addressed by directly interrogating participants with the help of questionnaires. Often, responses to presented questions are given by choosing a category of a related verbal answering scale. Although such data collection is determined directly by the verbal categories of the scales, little systematic research has been done (Rohrmann, 1978 ), as compared with the construction of questionnaire items. Spector ( 1976 ) summarized the consequences of how response categories are commonly selected: “This selection is often made on no more solid basis than habit, imitation, or subjective judgment. Yet the equal interval properties of the response continuum is assumed even though this assumption may, in fact, be false. . . . When faced with a scale of unequal intervals, subjects sometimes complain of a difficulty in making responses because some adjacent choices are closer together than others. To eliminate this problem, equal interval response categories should be used” (p. 374). Here, we show that our proposed translation procedure can serve as a useful basis for testing and constructing verbal rating scales and determining equidistant verbal response categories.

For the selection of frequency terms, three main criteria are suggested: equidistance, percentage of correct reclassifications, and discriminatory power of the MFs. First, frequency words should be distributed equidistantly along the numerical scale so that data can be interpreted as having interval-scale properties and, therefore, further statistical analyses are feasible. Generally, verbal rating scale categories are assumed to have rank order, but the distance between intervals is not necessarily equal (Jamieson, 2004 ). That is, verbal rating scale responses comprise ordinal- but not interval-level data, and this precludes the application of parametric statistical analyses. It is common practice to apply mathematical operations, such as multiplication or division (necessary for the calculation of means, etc.) to such data, although these operations are not valid for ordinal data. Moreover, employing inappropriate statistical techniques may lead to the misinterpretation of results and to incorrect conclusions.

Second, the percentage of correct reclassifications—that is, how many original data points were reclassified correctly according to the frequency expression to which they originally belonged—gives information about the discriminability and steadiness of the words’ meanings. Third, the criterion of discriminatory power reveals whether MFs differ considerably or not. On the basis of this measure, it is possible to conclude whether the meanings of LTs are interpreted similarly or differently by study participants.

In study 2, fuzzy MFs for the scale of an example questionnaire—namely, the COPSOQ (Kristensen et al., 2005 )—are discussed. The COPSOQ is a free screening instrument for evaluating psychosocial factors at work, including stress and employee well-being, as well as selected personality factors. The questionnaire consists of five frequency words: almost never , infrequently , sometimes , often , and always . We constructed three response scales with alternative frequency expressions and empirically tested an alternative scale consisting of never , sometimes , in half of the cases , often , and always . We hypothesized that the distance between each of the alternative response labels is nearly equal and compared results of both scales (original vs. alternative COPSOQ).

Two-step translation procedure

Here, we present details of the two-step translation procedure for the numerical translation of verbal frequency expressions. First, the estimation technique and method applied in the empirical study are outlined. Thereafter, fuzzy analysis and MFs are specified.

Step One: empirical investigation

Participants.

Eighty-nine undergraduate students (9 males) at Chemnitz University of Technology with an average age of 21.5 years ( SD = 2.7) took part in the study. Four persons stated that they did not understand the task and were therefore excluded from further data analyses.

Materials and procedure

The survey instrument was a paper questionnaire and consisted of two parts. In the first part, participants were asked to consider their workload and related requirements that their course of study imposed on them. Then they were asked to answer the following three questions of the COPSOQ (the original material was presented in German): (1) Is it always necessary to work at a rapid pace? (2) Is your work unevenly distributed such that it piles up? (3) How often do you not have enough time to complete all of your work tasks? An explanation as to how the paper questionnaire should be filled out followed, and participants were then asked to assign three numerical values to each of the 11 exemplars of frequency expressions (see translations from the original German in Table  1 ). Words were chosen according to their frequent usage in questionnaires and in daily communication and on the basis of former research (e.g., Rohrmann, 1978 ). Three numerical values were estimated: (1) the typical value that best represented the given frequency word, (2) the minimal value , and (3) maximal value that corresponded to the given verbal expression. The semantic meaning of the words can be characterized as follows: The first value identifies the most typical numerical equivalent for the word, whereas other values indicate lower and upper boundaries of the verbal frequency expression. Participants were instructed to give their estimates in frequency format (e.g., Is it hardly ever necessary to work at a rapid pace means “in X of 100 work tasks/cases”). We used this format because it is a natural mode of representing information and it turned out that encoding and estimating information in frequency format is easier than in probability or percentage form (Gigerenzer & Hoffrage, 1995 ; Hoffrage, Lindsey, Hertwig, & Gigerenzer, 2000 ).

Step two: Fuzzy analysis

• Fuzzy membership functions

MFs are truth value functions. The membership value ( μ ) represents the value of the truth that an object belongs to a specific class (e.g., the numerical frequency that 70 of 100 cases belong to the class frequency expression often ). For the analysis of empirical data provided by the 85 participants, a parametric MF of the potential type (Bocklisch & Bitterlich, 1994 ; Hempel & Bocklisch, 2009 ) was used (see Fig.  2 ).

Parametric membership function of potential type

This function is based on a set of eight parameters: r marks the position of the mean value of the empirical estimates of the typical value , while a represents the maximum value of the MF. Regarding class structure, a expresses class weight in the given structure (we used a = 1 for all classes in this investigation, such that all frequency terms were weighted equally). The parameters c l and c r characterize left- and right-sided expansions of the class and, therefore, mark the range of the class, in a crisp sense. In addition to the mean of typical estimates ( M typ ), the means of minimum ( M min ) and maximum ( M max ) correspondence values estimated by participants were used for the calculation: c l = M typ − M min and c r = M max − M typ . A special feature of this function type is that there is no intersection with the x -axis ( μ is always >0). This characteristic is founded on the assumption that sample estimates are not representative of the whole population; therefore, no definite end-points are defined. The parameters b l and b r assign left- and right-sided membership values at the boundaries of the function. Therefore, b l and b r represent border membership, whereas d l and d r specify continuous decline of the MF starting from the class center and are denoted as representative of a class. The d parameters determine the shape of the function and, hence, the fuzziness of the class. The b and d parameters were calculated from the distribution of the empirical data using Fuzzy Toolbox software (Bocklisch, 2008 ), which is specialized for fuzzy analyses and modeling of MFs.

In contrast to the nonparametric individualized MF approaches of Wallsten, Budescu, Rapoport, Zwick, and Forsyth ( 1986 ) and Budescu et al. ( 2003 ), we fit group MFs to obtain a generalized model of a sample or certain population of participants. Furthermore, our MFs are defined continuously, such that, in addition to the expansions of the class ( c parameters), the MFs’ shape ( d parameters) carries information about the distribution of the empirical estimates. This is an advantage insofar as potential contradictions between participants’ estimates are considered. In contrast, a triangular MF type describes the graded interval between μ = 0 and μ = 1 with a rather arbitrary linear model and, thus, does not account for the empirical data provided by many individuals. On the level of individual estimates, a triangular MF would model the data appropriately, but on the level of a certain sample or population, this is not the case. Additional parameters are needed to model the expansion ( c ) and the distribution of the estimates ( d ), as well as the membership value at the border of the function ( b ), which is by definition always >0. A continuous variation of MFs, ranging from highly fuzzy to crisp, is available through this parametric function type. It also allows for asymmetry in fuzzy classes by providing individual parameters for the left- and right-hand branches of the function. As the results of former research show (Bocklisch et al., 2010 ; Budescu et al., 2003 ), many verbal expressions are best described by asymmetric MFs. Therefore, we expect this feature to be especially important for the present study.

We first present the descriptive statistics of the data set. Thereafter, the fuzzy MF procedure is specified. In our opinion, it is valuable to present both results for purposes of completeness and comparison, even though we favor the latter approach. It is important that the two approaches be understood independently. Moreover, fuzzy analysis and modeling of the MFs, by definition, do not refer to the background of probability theory and statistics. Although some parameters of our MF type can be interpreted statistically in this case (e.g., r values are equal to the arithmetic mean), an MF is not a probability density function, and conventional requirements (i.e., the integral of the variable’s density is equal to 1) are not valid. A more general comparative discussion of the statistical and fuzzy approaches is provided in Singpurwalla and Booker ( 2004 ).

Descriptive statistics

Table  1 shows the typical values that corresponded to the frequency expressions presented. Minimum and maximum estimates of the semantic meaning of linguistic terms were necessary for modeling the MFs ( c parameters). Hence, they are not presented here.

At first glance, the results show that frequency expressions are distributed almost over the entire numerical frequency scale with varying distances, ranging from never ( M = 1.37) to always ( M = 97.46). Clearly, the 11 expressions are divided into three frequency categories: lower and higher frequency categories, which refer to the middle point of the scale ( M = 50), and a medium frequency category consisting of one LT ( in half of the cases : M = 50.14). The first 5 expressions (ranging from never to sometimes ) are characterized by means less than M = 35 and, therefore, belong to the lower frequency group, whereas the remaining expressions (ranging from frequently to always ) show mean values larger than M = 65 and belong to the higher frequency category. Between the expressions sometimes and in half of the cases and between in half of the cases and frequently , there are intervals measuring approximately 15. These are the largest two intervals among all the intervals between the LTs. Similar findings were reported by Bocklisch et al. ( 2010 ) for verbal probability expressions, which are also split according to three categories (low, medium, and high probability). Standard deviation ( SD ) values show a systematic pattern: Frequency expressions near the borders of the numerical frequency scale have smaller SD s. Starting with the minimum of the verbal scale ( never : SD = 2.23), the SDs increase up to midscale, reaching their highest values with the words occasionally ( SD = 12.23) and sometimes ( SD = 10.96), as well as frequently ( SD = 15.43) and often ( SD = 12.91), and subsequently decrease again ( always : SD = 6.17). Again, the frequency expression that covers the middle of the scale ( in half of the cases : SD = 1.21) is an exception, because its SD is the smallest one. By tendency, skews are higher at the borders of the verbal scale. Expressions belonging to the lower category (e.g., never ) are slightly skewed to the right, and in the higher category (e.g., always ), they tend to be skewed to the left. Kurtosis values are considerably higher for the expressions in half of the cases , almost always , and always , while values for the other frequency expressions are almost normally distributed (i.e., kurtosis = 0 according to the SPSS software’s definition). These findings are consistent with results reported by Bocklisch et al. ( 2010 ) as well as Budescu et al. ( 2003 ) that investigated verbal probability expressions.

Fuzzy analysis

Figure  3 shows the MFs for the 11 verbal frequency expressions. The representative values ( r ) indicating the highest memberships are identical to the reported means in Table  1 . Obviously, the functions differ in shape, symmetry, overlap, and vagueness. The functions for the verbal frequency expressions at the borders of the scale, never and always , are narrower than those in the middle, such as sometimes or often , which is in accordance with reported SD s and kurtosis values. Most of the functions are slightly asymmetric and are clearly not distributed equidistantly along the scale. Some (neighbor) functions overlap to a large extent (e.g., occasionally and sometimes ), while others are quite distinct (e.g., in half of the cases and frequently ).

Membership functions of the 11 verbal frequency expressions

The area of MF overlap A ov (see Fig.  4 , gray area) is informative about the similarity of the words’ meanings. Overlap is defined as the surface imbedded by the MFs and the x -axis. One important characteristic of our parametric potential MF type is that the function has no points of intersection with the x -axis and, therefore, the surface integral is infinite. Additionally, the function type has no general integral solution. Hence, the surface covered by the function (in a certain range) can only be approximated, which is done with the help of Fuzzy Toolbox software (Bocklisch, 2008 ) and operates as follows. The range of the MFs is identified: Here, the minimum is 0 and the maximum is 100 according to the numerical frequency scale. Thereafter, μ min is calculated numerous times using a high sampling rate with equidistant sample points along the numerical scale. Then the area of overlap A ov is determined by adding up the products of the sampling distance and μ min values for the whole number of sampling points. Thereafter, areas covered by MF1 and MF2 ( A MF1 and A MF2 ) are defined using the same procedure. A standardized quotient ( ov ) of the overlapping area of the MFs ( A ov ) is obtained by calculating the arithmetic mean: ov = 0.5 × [( A ov : A MF1 ) + ( A ov : A MF2 )].

Approximation of the discriminatory power of two membership functions

The ov is used to define the discriminatory power ( dp ) between two MFs: dp = 1 – ov (Bocklisch, 2008 ). The dp is standardized taking values from 0 (MFs are identical) to 1 (no overlap at all). Hence, the larger the overlap (e.g., occasionally and sometimes ), the smaller the dp and the more similar the meanings of the verbal expressions are. The ov of the MFs in Fig.  4 is approximately .37 which corresponds to dp = .63. Table  2 shows dp values for the 11 LTs.

If dp values are greater than or equal to .7, then MFs (and LTs) are interpreted as being considerably different, because the area of shared overlap is less than 30%. This is the case for a lot of LTs (see Table  2 ), except for infrequently and occasionally ( dp = .46), occasionally and sometimes ( dp = .25), frequentl y and often ( dp = .19), often and most of the time ( dp = .32), frequently and most of the time ( dp = .38), and most of the time and almost always ( dp = .69). Most of these LT pairs are direct “neighbors.”

The COPSOQ answer scale (Kristensen et al., 2005 ) consists of five frequency expressions: almost never , infrequently , sometimes , often , and always . Figure  5 shows the MFs of the verbal rating scale utilized in the COPSOQ (upper left corner) and three proposed alternative scales that are almost equidistant, consisting of four and five frequency expressions.

Membership functions of the original COPSOQ and alternative COPSOQ (I-III) response scales

In the original COPSOQ scale, the distances between the representative values vary. The LTs almost never and infrequently have approximately the same distance (10.21) as infrequently and sometimes (14.61), but the words sometimes and often (36.53), as well as often and always (27.8), are separated by a greater distance. Therefore, this scale is not equidistant. Furthermore, no verbal term is associated with the middle of the scale, which indicates a frequency of occurrence of approximately 50 out of 100. That is, such a term is unavailable, even to participants who should wish to express this frequency.

The interpretation of verbal frequency scales as interval scales relies on the premise of equidistance (Jamieson, 2004 ). While authors of the COPSOQ may have wanted the frequency words to be distributed as shown in Fig.  5 , such a distribution is rather unlikely, for two reasons: First, if a middle category is not intended, an even number of LTs is usually chosen for a verbal response scale. Second, a scale that combines highly similar words (such as almost never and infrequentl y) with highly discriminatory terms (e.g., often and always ) seems to be inconsistent.

To remedy this problem, we propose three scales that meet the criterion of equidistance quite well (see Fig.  5 ): first, two 5-point scales consisting of the frequency terms never , sometimes , in half of the cases , often , and always (alternative COPSOQ I) and almost never , sometimes , in half of the cases , often , and almost always (alternative COPSOQ II) and, second, a 4-point scale with the expressions almost never , sometimes , often , and almost always (alternative COPSOQ III). The frequency expressions for these scales were chosen according to results presented in Table  2 and Fig.  3 . Both 5-point scales (alternative COPSOQs I and II) are distributed almost equidistantly, do not overlap to a great extent (see dp values in Table  2 ), and are almost symmetric in shape. However, they differ according to their psychological width, which “. . . refers to the extent of the psychological continuum suggested by the rating labels” (Lam & Stevens, 1994 , p.142). Therefore, alternative COPSOQ I is wider, because the LTs at the borders of the scale approximate the numerical endpoints ( never , M = 1.37; always , M = 97.46) and, hence, mark a wider psychological continuum than the LTs of alternative COPSOQ II ( almost never , M = 8.31; almost always , M = 88.11). The 4-point alternative COPSOQ III (see Fig.  5 , lower left) is also nearly equidistant, where MFs are highly distinct and the middle of the scale is not covered.

In addition to the criteria of equidistance, symmetry, and overlap of the MFs’ distribution, the percentage of correct reclassifications of the participants’ original estimates is informative of the quality of the scales. For the reclassification task, the original data were used and reassigned to the MFs. Basically this is done by using a participant’s typical estimate for a certain verbal expression and entering it into the equations of all MFs (see Fig.  2 ) as u . Then the membership values ( μ ) can be calculated. Therefore, 11 membership values (i.e., for the 11 MFs of the 11 frequency expressions) are generated for one data point (i.e., estimate of a respondent). Among these, the highest membership value indicates the frequency word to which the estimate is reclassified. The reclassification is correct if this frequency word is the same as the one for which the estimate was originally given. The reclassification step was done with the help of Fuzzy Toolbox software (Bocklisch, 2008 ). Table  3 shows reclassification results obtained by counting the number of original data points correctly reclassified according to the frequency expression to which they originally belong.

For the original scale consisting of 11 frequency expressions, the correct reclassification percentages lie between 1.18% for occasionally (only 1.18% of the typical estimates for occasionally were reclassified as belonging to occasionally , and the other 98.82% were erroneously reclassified as belonging to other frequency expressions) and 98.82% for in half of the cases (nearly all estimates for in half of the cases were reclassified as belonging to in half of the cases ). The mean percentage of reclassification for this scale ( M = 56.35) is rather low, which is mainly due to the large overlap of the MFs of the frequency expressions (see Fig.  3 ). The original COPSOQ scale ( M = 79.99) and all alternative scales ( M > 85.3) with four to five linguistic terms have higher mean percentages of correct reclassification. Hence, the more terms that are included in a scale, the lower the reclassification percentages will be, due to the similarity of the words’ meanings that can be observed in the overlap of the MFs. In summary, all suggested alternative COPSOQ scales showed better reclassification results and were nearly e quidistant, as compared with the original COPSOQ scale. To optimize all criteria, it would be advisable to choose the alternative COPSOQ I with the five frequency expressions never , sometimes , in half of the cases , often , and always.

In study 1, we outlined a general procedure for the translation of verbal expressions based on empirical estimates and using fuzzy MFs for modeling. The results (see Table  1 and Fig.  3 ) showed that the MFs of frequency expressions at borders of the numerical scale (i.e., never and always ) showed less vagueness than did midscale expressions (i.e., often and sometimes ), suggesting that they more clearly reflected the given expression. This was also found for probability expressions (Bocklisch et al., 2010 ) that differed even more in vagueness when midscale terms and boundary terms are compared. The LT in half of the cases is an exception ( SD = 1.21; see MF in Fig.  3 ): Its meaning is rather crisp with regard to other frequency expressions in the middle of the scale and as compared with the midscale probability LTs thinkable ( SD = 20.24) and possible ( SD = 21.60) in Bocklisch et al. ( 2010 ). This could be due to the relatively “precise” meaning of the word “half.”

The dp values (see Table  2 ) and percentages of correct reclassification (see Table  3 ) were introduced as means for measuring the disparity and steadiness of the MFs. Hence, a differentiated evaluation of the MFs is possible, and conclusions concerning the meaning of the modeled LTs are straightforward. For a few MFs, dp values are rather low, and therefore, the meanings of the corresponding LTs are very similar. However, most of the words are distinct. The percentages of correct reclassification are very high for never (81.18), in half of the cases (98.82), and always (91.57), which supports the idea that these LTs are more precise in their meanings.

The emerging categories, low, middle, and high frequencies, may be due to the actual sample of verbal expressions. It would be interesting to determine whether the estimation of more or fewer LTs would lead to the same categories as those found in this study and in Bocklisch et al. ( 2010 ) or not.

Many questionnaires utilize verbal rating scales consisting of verbal frequency expressions. Thus, we exemplarily tested a well-established questionnaire, the COPSOQ, concerning equidistant distribution of its linguistic expressions and the quality of the scale (i.e., percentages of correct reclassification of the original data). It was found that the scale is in need of improvement because it fails to satisfy the criterion of an equidistant distribution. At present, strictly speaking, the scale cannot be interpreted as having interval level, and hence, further statistical analyses (e.g., the calculation of means for groups of participants) are not appropriate. To solve this problem we proposed three alternative COPSOQ scales with four or five frequency expressions distributed nearly equidistantly (see Fig.  5 ). The suggested 4-point scale (alternative COPSOQ III) should be employed for research questions where no middle category is intended. Alternatives I and II differ concerning LTs at the borders, and alternative I offers a wider psychological continuum for frequency estimation. Both scales produced positive results for mean reclassification percentages, dp s of the MFs, and equidistance and can thus both be applied according to intended utilization. Wyatt and Meyers ( 1987 ) found that scales with less extreme endpoints (e.g., alternative COPSOQ II: almost never and almost always ) lead to greater variability in respondents’ estimates than do scales with more extreme endpoints (e.g., alternative COPSOQ I: never and always ). However, it is not yet clear whether this finding can be generalized to other words and contexts (Lam & Stevens, 1994 ).

In summary, we showed that our translation procedure is a methodological innovation and, therefore, has potential for application in research. In study 2 we use the method again, exploring the COPSOQ scale in greater detail. That is, one could argue that the total number of frequency expressions influences the resulting MFs. If this were the case, it might be inappropriate to draw conclusions from a study that presented 11 LTs to a scale (COPSOQ) that consisted of only 5 LTs. Therefore, in study 2, we presented the 5 LTs and compared the results with those of study 1. Additionally, we manipulated scales of the original COPSOQ and alternative COPSOQ I, which allowed us to test whether our conclusions based on the MFs in study 1 were indeed correct.

One hundred nine undergraduate students (19 males) of Chemnitz University of Technology with an average age of 23.4 years ( SD = 3.3) took part in the study. Fifteen persons did not understand the task and were therefore excluded from further analyses.

The paper questionnaire employed in study 2 was identical to that used in study 1, except that the number of presented frequency expressions differed (study 1, 11 LTs vs. study 2, 5 LTs). Again, participants first answered three questions of the COPSOQ. One group of participants ( N = 51) received the original COPSOQ response scale ( almost never , infrequently , sometimes , often , and always ), while the other group ( n = 42) obtained an alternative COPSOQ answering scale ( never , sometimes , in half of the cases, often , and always ). In the second part, the study 1 translation procedure was also used to translate the five frequency expressions.

Table  4 shows the descriptive results of the typical values that corresponded to the frequency expressions of the original and alternative COPSOQ scales (middle and right columns), as well as the results of study 1 (left column; see also Table  1 ) for purposes of comparison.

For the LTs sometimes , often , and always , a direct comparison between all conditions is possible. In sum, mean values for often and always are very similar. The largest difference is 5.3 between always in the context of 11 LTs and always in the original COPSOQ scale using 5 LTs. For sometimes , the original COPSOQ ( M = 41.08) stands out, as compared with the other conditions (alternative COPSOQ, M = 29.0 and the 11-LT version, M = 33.13). The differences between conditions for never and in half of the cases (11 LTs vs. 5LTs. alternative COPSOQ) as well as for almost always and infrequently (11 LTs vs. 5LTs, original COPSOQ) are also rather small. The SD s are comparable in size between groups for a certain LT, except always (original COPSOQ: SD = 19.04), which has a larger SD than the other conditions.

Figure  6 shows the resulting MFs for the five verbal frequency expressions of the original versus alternative COPSOQ response scales in the context of 5 LTs vs. 11 LTs (see also Fig.  5 ).

Membership functions of the verbal frequency expressions of the original versus alternative COPSOQ I response scales for 5 versus 11 LTs

In the alternative scale version (5 LTs), the verbal terms at the borders ( never and always ) are closer to the borders of the underlying numerical scale, as compared with the original scale (5 LTs). The scales also differ in the extent of the MFs’ overlaps. For instance, in the original COPSOQ, the overlaps occurring at border terms are larger, and in the alternative version, midscale terms overlap more. The distribution of MFs is closer to equidistance for the suggested alternative response scale. The functions’ shapes of the word often are very similar, while the others differ slightly—for instance, in expansion (e.g., the MF for sometimes is broader in the alternative scale version). The frequency expression in half of the cases marks the middle of the scale. The function’s shape is salient; it is asymmetric, and the left-hand branch is very crisp, as compared with the right-hand branch.

A comparison of frequency expressions between the 5- and 11-LT versions of the original COPSOQ (see Fig.  6 , left side) and of the alternative COPSOQ (see Fig.  6 , right side) shows a highly similar appearance of MFs in terms of r -value positions (equal to the means in Table  4 ), shapes, and overlaps. MFs tend to be slightly narrower in the 11-LT versions of the two scales, and the border term always tends to be more extreme, as compared with the 5-LT versions. The frequency expression in half of the cases has equal r values (5 LTs, r = 50.24; 11 LTs, r = 50.14), but the MF’s shape deviates. In the 5-LT version of the alternative COPSOQ, it is rather fuzzy and asymmetric, whereas in the 11-LT version, it is very crisp and symmetric. For the evaluation of the differences between the 5- and 11-LT versions, again, dp values are calculated. Table  5 shows the dp values.

For instance, for sometimes , the difference between the 5- and 11-LT versions of the original COPSOQ scale is slightly larger ( dp = .29) than for the 5- and 11-LT versions of the alternative COPSOQ I scale ( dp = .14). Generally, dp values for never , almost never , infrequently , sometimes , in half of the cases , and often are all rather small ( dp s < .49), which means that the MFs are very similar and overlap in 50% to 90%. However, for always , there is a considerable difference between MFs in the alternative COPSOQ I (5 vs. 11 LTs: dp = .74), but not for the original COPSOQ (5 vs. 11 LTs: dp = .53).

Study 2 aimed to clarify (1) whether the suggested alternative response labels (see Fig.  5 : alternative COPSOQ I) also have equal distances in the context of 5 LTs and (2) whether the total number of prompted LTs (5 vs. 11) influences the interpretation of frequency words. First, we found that alternative COPSOQ I has nearly equal distances between the response categories (see Table  4 and Fig.  6 ). Hence, our presented method is generally suitable for application in choosing LTs for answering scales. Second, the resulting dp values (see Table  5 ) show that the total number of prompted LTs seems to have no systematic influence on the words’ interpretation, since nearly all MFs are identical to a great extent ( dp s < .53). There is only one considerable difference: MFs of always (alternative COPSOQ I) are distinct ( dp = .74). That is, always in the 5-LT version is broader and covers more of the numerical frequency scale than always in the 11-LT version does. Nevertheless, the difference is rather small, because the criterion value of dp > .7 is just met. Accordingly, this tendency is also the case for always in the original version (see Fig.  6 , left side). Our results show that the number of prompted LTs has no considerable influence on the interpretation of the LTs meanings, although there are, at least to some extent, small differences between the MFs depending on the number of LTs presented (see also Table  4 ).

Conclusions

This article presents a general two-step procedure for the numerical translation of linguistic terms that are exemplars of frequency expressions. In two studies, we showed that the presented procedure is a methodological innovation and can serve as basis for choosing LTs for applications such as questionnaires. In study 1, the procedure was presented for 11 frequency expressions. First, three numerical values for each linguistic term (i.e., most typical, minimal, and maximal correspondence values) were estimated. Second, the resulting data were modeled using the parametric MFs of the potential type. While most alternative procedures are more costly (Budescu et al., 2003 ) or are not based on empirical estimates (Boegl et al., 2004 ), our approach is very frugal and efficient in terms of data collection.

Results show that the functions are capable of modeling the data in a very efficient way, yielding averaged MFs that describe the LTs continuously along a numerical frequency scale. They also take into account the asymmetry of the empirical data, resulting due to the parameters that model the left- and right-hand branches of the function (e.g., c l and c r ). MFs with fewer parameters would model the data without considering asymmetry and would, therefore, be less accurate and suitable for the reported data. The b and d parameters reflect features of the distribution of the empirical estimates and carry information about between-subjects differences. Another advantage of the proposed function type is that the semantic content of parameters can be interpreted at a meta-level. Hence, they render the vague meaning of linguistic terms more tangible. In addition to existing methods (e.g., Boegl et al., 2004 ; Budescu et al., 2003 ; Wallsten et al., 1986 ), this parametric MF approach is an interesting alternative that yields group MFs and contributes to the investigation of vague linguistic terms. Future research would benefit from a comparison of different translation procedures and MF concepts (e.g., individualized MFs vs. group MFs).

In study 2, we explored the COPSOQ scale in detail. Questionnaires are widely used in the social sciences and humanities to address empirical research questions. We exemplarily tested the COPSOQ questionnaire (see the Results sections of studies 1 and 2) and found that the scale employed in this tool is in need of improvement because its verbal labels fail to satisfy the criterion of an equidistant distribution. At present, this questionnaire scale is ordinal rather than interval level, and therefore, statistical analyses such as the calculation of arithmetic means for groups of participants are not valid. A counterargument might suggest that missing equidistance is compensated for by the conventional visual arrangement of scales. This might, indeed, have an influence on the interpretation of the words’ meanings. To clarify this issue, our translation approach may be useful for further studies. We suggest three nearly equidistant verbal frequency scales (see Fig.  5 ) with four or five frequency expressions as a starting point for such studies.

In constructing verbal response scales, we recommend adapting the context of the cover story according to the topic (e.g., psychology, medicine, or economy), because context is known to influence a word’s interpretation (Pepper & Prytulak, 1974 ; Teigen & Brun, 2003 ). Additionally, the purpose for which the LTs will be used afterward (e.g., questionnaire or decision support system) should also be considered. Future studies may benefit from choosing estimators from the target population—for example, medical experts or participants in experimental studies. According to the desired psychological width of the response scale, “choosing a scale for a particular application must take into account what needs to be measured” (Wyatt & Meyers, 1987 , p. 34).

Different samples of participants and different languages of investigation should also be considered in future studies. We report data from a student sample using German LTs. Although this might limit the generalizability of our results, the presented methodology (translation procedure) is not restricted to a certain sample or language. Therefore, it would be interesting to study how different samples of people (such as experts vs. novices in medicine) interpret LTs and whether or not the meanings of verbal expressions are understood similarly in different languages.

The reported MFs, especially in study 1, show large overlaps, indicating that contiguous expressions are very similar or almost identical in their meanings. It is noteworthy that despite the vagueness of natural language, MFs are a convenient tool for identifying words that are more distinct (i.e., with small overlap) in their meaning than others. The identification of unambiguous and distinct words that can be used for communication is of tremendous importance in areas such as medicine or the military, where misunderstandings could lead to severe consequences. Currently, we are exploring the availability of such distinct words for communication purposes with the help of our MFs. Karelitz and Budescu ( 2004 ) devised promising criteria for the conversion of phrases from a communicator’s to a recipient’s lexicon—for instance, the peak rank order between MFs. Our MF approach could contribute additional criteria to such an approach, such as the mathematical quantification of MF overlaps.

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Author Note

Thanks to Martin Baumann, Marta Pereira, Diana Rösler, Andreas Neubert, Lydia Obermann, Thomas Schäfer, David V. Budescu, and the students of Chemnitz University of Technology for their contributions and support.

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Bocklisch, F., Bocklisch, S.F. & Krems, J.F. Sometimes, often, and always: Exploring the vague meanings of frequency expressions. Behav Res 44 , 144–157 (2012). https://doi.org/10.3758/s13428-011-0130-8

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Bu araştırmanın amacı, gençlik merkezi faaliyetlerine katılan bireylerin bazı değişkenlere göre serbest zaman tatmin düzeylerinin incelenmesidir. Evreni Türkiye İç Anadolu bölgesindeki Gençlik merkezine üye gençler oluşturmaktadır. Araştırma grubunu ise bu bölgede bulunan 11 ildeki Gençlik merkezlerine üye olan yaşları 13-27 arasında değişen 906 birey oluşturmaktadır. Araştırma verileri toplanmasında, serbest zaman tatmin düzeylerini belirlemek amacıyla Beard ve Ragheb&#39; in (1980) geliştirdikleri, Karlı ve arkadaşlarının (2008) yılında geçerlilik güvenilirlik çalışmasını yaparak Türkçe literatüre kazandırdıkları 39 sorudan ve altı alt boyuttan oluşan iç tutarlılık (Chronbach Alfa) katsayısı 92 olarak bulunmuş Serbest Zaman Tatmin Ölçeği (Leisure Satisfaction Scale/LSS) kullanılmıştır. Verilerin analizinde değişkenlerin gruplara göre dağılımları incelenmiş, dağılımların normalliği ve varyansların homojenliği değerlendirilerek dağılımların parametrik özellik sergilemediği sonucuna ...

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• J Grad Med Educ
• v.5(4); 2013 Dec

Analyzing and Interpreting Data From Likert-Type Scales

Likert-type scales are frequently used in medical education and medical education research. Common uses include end-of-rotation trainee feedback, faculty evaluations of trainees, and assessment of performance after an educational intervention. A sizable percentage of the educational research manuscripts submitted to the Journal of Graduate Medical Education employ a Likert scale for part or all of the outcome assessments. Thus, understanding the interpretation and analysis of data derived from Likert scales is imperative for those working in medical education and education research. The goal of this article is to provide readers who do not have extensive statistics background with the basics needed to understand these concepts.

Developed in 1932 by Rensis Likert 1 to measure attitudes, the typical Likert scale is a 5- or 7-point ordinal scale used by respondents to rate the degree to which they agree or disagree with a statement ( table ). In an ordinal scale, responses can be rated or ranked, but the distance between responses is not measurable. Thus, the differences between “always,” “often,” and “sometimes” on a frequency response Likert scale are not necessarily equal. In other words, one cannot assume that the difference between responses is equidistant even though the numbers assigned to those responses are. This is in contrast to interval data, in which the difference between responses can be calculated and the numbers do refer to a measureable “something.” An example of interval data would be numbers of procedures done per resident: a score of 3 means the resident has conducted 3 procedures. Interestingly, with computer technology, survey designers can create continuous measure scales that do provide interval responses as an alternative to a Likert scale. The various continuous measures for pain are well-known examples of this ( figure 1 ).

Continuous Measure Example

Please tell us your current pain level by sliding the pointer to the appropriate point along the continuous pain scale above.

Typical Likert Scales

The Controversy

In the medical education literature, there has been a long-standing controversy regarding whether ordinal data, converted to numbers, can be treated as interval data. 2 That is, can means, standard deviations, and parametric statistics, which depend upon data that are normally distributed ( figure 2 ), be used to analyze ordinal data?

A Normal Distribution

When conducting research, we measure data from a sample of the total population of interest, not from all members of the population. Parametric tests make assumptions about the underlying population from which the research data have been obtained—usually that these population data are normally distributed. Nonparametric tests do not make this assumption about the “shape” of the population from which the study data have been drawn. Nonparametric tests are less powerful than parametric tests and usually require a larger sample size (n value) to have the same power as parametric tests to find a difference between groups when a difference actually exists. Descriptive statistics, such as means and standard deviations, have unclear meanings when applied to Likert scale responses. For example, what does the average of “never” and “rarely” really mean? Does “rarely and a half” have a useful meaning? 3 Furthermore, if responses are clustered at the high and low extremes, the mean may appear to be the neutral or middle response, but this may not fairly characterize the data. This clustering of extremes is common, for example, in trainee evaluations of experiences that may be very popular with one group and perceived as unnecessary by others (eg, an epidemiology course in medical school). Other non-normal distributions of response data can similarly result in a mean score that is not a helpful measure of the data's central tendency.

Because of these observations, experts over the years have argued that the median should be used as the measure of central tendency for Likert scale data. 3 Similarly, experts have contended that frequencies (percentages of responses in each category), contingency tables, χ 2 tests, the Spearman rho assessment, or the Mann-Whitney U test should be used for analysis instead of parametric tests, which, strictly speaking, require interval data (eg, t tests, analysis of variance, Pearson correlations, regression). 3 However, other experts assert that if there is an adequate sample size (at least 5–10 observations per group) and if the data are normally distributed (or nearly normal), parametric tests can be used with Likert scale ordinal data. 3

Fortunately, Dr. Geoff Norman, one of world's leaders in medical education research methodology, has comprehensively reviewed this controversy. He provides compelling evidence, with actual examples using real and simulated data, that parametric tests not only can be used with ordinal data, such as data from Likert scales, but also that parametric tests are generally more robust than nonparametric tests. That is, parametric tests tend to give “the right answer” even when statistical assumptions—such as a normal distribution of data—are violated, even to an extreme degree. 4 Thus, parametric tests are sufficiently robust to yield largely unbiased answers that are acceptably close to “the truth” when analyzing Likert scale responses. 4

Educators and researchers also commonly create several Likert-type items, group them into a “survey scale,” and then calculate a total score or mean score for the scale items. Often this practice is recommended, particularly when researchers are attempting to measure less concrete concepts, such as trainee motivation, patient satisfaction, and physician confidence—where a single survey item is unlikely to be capable of fully capturing the concept being assessed. 5 In these cases, experts suggest using the Cronbach alpha or Kappa test or factor analysis technique to provide evidence that the components of the scale are sufficiently intercorrelated and that the grouped items measure the underlying variable.

The Bottom Line

Now that many experts have weighed in on this debate, the conclusions are fairly clear: parametric tests can be used to analyze Likert scale responses. However, to describe the data, means are often of limited value unless the data follow a classic normal distribution and a frequency distribution of responses will likely be more helpful. Furthermore, because the numbers derived from Likert scales represent ordinal responses, presentation of a mean to the 100th decimal place is usually not helpful or enlightening to readers.

In summary, we recommend that authors determine how they will describe and analyze their data as a first step in planning educational or research projects. Then they should discuss, in the Methods section or in a cover letter if the explanation is too lengthy, why they have chosen to portray and analyze their data in a particular way. Reviewers, readers, and especially editors will greatly appreciate this additional effort.

Gail M. Sullivan, MD, MPH, is Editor-in-Chief of the Journal of Graduate Medical Education, and Anthony R. Artino Jr, PhD, is Associate Professor of Medicine and Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences.

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30 Interpretation Strategies: Appropriate Concepts

Allen Trent, College of Education, University of Wyoming

Jeasik Cho, Department of Educational Studies, University of Wyoming

• Published: 04 August 2014
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This essay addresses a wide range of concepts related to interpretation in qualitative research, examines the meaning and importance of interpretation in qualitative inquiry, and explores the ways methodology, data, and the self/researcher as instrument interact and impact interpretive processes. Additionally, the essay presents a series of strategies for qualitative researchers engaged in the process of interpretation. The article closes by presenting a framework for qualitative researchers designed to inform their interpretations. The framework includes attention to the key qualitative research concepts transparency, reflexivity, analysis, validity, evidence, and literature. Four questions frame the article: What is interpretation, and why are interpretive strategies important in qualitative research? How do methodology, data, and the researcher/self impact interpretation in qualitative research? How do qualitative researchers engage in the process of interpretation? And, in what ways can a framework for interpretation strategies support qualitative researchers across multiple methodologies and paradigms?

“All human knowledge takes the form of interpretation.” In this seemingly simple statement, the late German philosopher Walter Benjamin asserts that all knowledge is mediated and constructed. He makes no distinction between physical and social sciences, and so situates himself as an interpretivist, one who believes that human subjectivity, individuals’ characteristics, feelings, opinions, and experiential backgrounds impact observations, analysis of these observations, and resultant knowledge/truth constructions. Contrast this perspective with positivist claims that knowledge is based exclusively on external facts, objectively observed and recorded. Interpretivists then, acknowledge that, if positivistic notions of knowledge and truth are inadequate to explain social phenomena, then positivist, hard science approaches to research (i.e., the scientific method and its variants) are also inadequate. So, although the literature often contrasts quantitative and qualitative research as largely a difference in kinds of data employed (numerical vs. linguistic), instead, the primary differentiation is in the foundational, paradigmatic assumptions about truth, knowledge, and objectivity.

This chapter is about interpretation and the strategies that qualitative researchers use to interpret a wide variety of “texts.” Knowledge, we assert, is constructed, both individually (constructivism) and socially (constructionism). We accept this as our starting point. Our aim here is to share our perspective on a broad set of concepts associated with the interpretive or meaning-making process. Although it may happen at different times and in different ways, interpretation is a part of almost all qualitative research.

Qualitative research is an umbrella term that encompasses a wide array of paradigmatic views, goals, and methods. Still, there are key unifying elements that include a generally constructionist epistemological standpoint, attention to primarily linguistic data, and generally accepted protocols or syntax for conducting research. Typically, qualitative researchers begin with a starting point—a curiosity, a problem in need of solutions, a research question, or a desire to better understand a situation from the perspectives of the individuals who inhabit that context (what qualitative researchers call the “emic,” or insider’s, perspective).

From this starting point, researchers determine the appropriate kinds of data to collect, engage in fieldwork as participant-observers to gather these data, organize the data, look for patterns, and then attempt to make sense out of the data by synthesizing research “findings,” “assertions,” or “theories” in ways that can be shared so that others may also gain insights from the conducted inquiry.

Although there are commonalities that cut across most forms of qualitative research, this is not to say that there is an accepted, linear, standardized approach. To be sure, there are an infinite number of variations and nuances in the qualitative research process. For example, some forms of inquiry begin with a firm research question, others without even a clear focus for study. Grounded theorists begin data analysis and interpretation very early in the research process, whereas some case study researchers, for example, may collect data in the field for a period of time before seriously considering the data and its implications. Some ethnographers may be a part of the context (e.g., observing in classrooms), but they may assume more observer-like roles, as opposed to actively participating in the context. Alternatively, action researchers, in studying issues about their own practice, are necessarily situated toward the “participant” end of the participant–observer continuum.

Our focus here is on one integrated part of the qualitative research process, interpretation, the process of collective and individual “meaning making.” As we discuss throughout this chapter, researchers take a variety of approaches to interpretation in qualitative work. Four general questions guide our explorations:

What is interpretation, and why are interpretive strategies important in qualitative research?

How do methodology, data, and the researcher/self impact interpretation in qualitative research?

How do qualitative researchers engage in the process of interpretation?

In what ways can a framework for interpretation strategies support qualitative researchers across multiple methodological and paradigmatic views?

We address each of these guiding questions in our attempt to explicate our interpretation of “interpretation,” and, as educational researchers, we include examples from our own work to illustrate some key concepts.

What Is Interpretation, and Why Are Interpretive Strategies Important in Qualitative Research?

Qualitative researchers and those writing about qualitative methods often intertwine the terms analysis and interpretation . For example, Hubbard and Power (2003) describe data analysis as, “bringing order, structure, and meaning to the data” (p. 88). To us, this description combines analysis with interpretation. Although there is nothing wrong with this construction, our understanding aligns more closely with Mills’s (2007) claim that, “put simply, analysis involves summarizing what’s in the data, whereas interpretation involves making sense of—finding meaning in—that data” (p. 122). For the purpose of this chapter, we’ll adhere to Mills’s distinction, understanding analysis as summarizing and organizing, and interpretation as meaning making. Unavoidably, these closely related processes overlap and interact, but our focus will be primarily on the more complex of these endeavors, interpretation. Interpretation, in this sense, is in part translation, but translation is not an objective act. Instead, translation necessarily involves selectivity and the ascribing of meaning. Qualitative researchers “aim beneath manifest behavior to the meaning events have for those who experience them” ( Eisner, 1991 , p. 35). The presentation of these insider/emic perspectives is a hallmark of qualitative research.

Qualitative researchers have long borrowed from extant models for fieldwork and interpretation. Approaches from anthropology and the arts have become especially prominent. For example, Eisner’s form of qualitative inquiry, “educational criticism” (1991), draws heavily on accepted models of art criticism. Barrett (2000) , an authority on art criticism, describes interpretation as a complex set of processes based on a set of principles. We believe many of these principles apply as readily to qualitative research as they do to critique. The following principles, adapted from Barrett’s principles of interpretation (2000, pp. 113–120), inform our examination:

Qualitative phenomena have “aboutness ”: All social phenomena have meaning, but meanings in this context can be multiple, even contradictory.

Interpretations are persuasive arguments : All interpretations are arguments, and qualitative researchers, like critics, strive to build strong arguments grounded in the information, or data, available.

Some interpretations are better than others : Barrett notes that, “some interpretations are better argued, better grounded with evidence, and therefore more reasonable, more certain, and more acceptable than others” (p. 115). This contradicts the argument that “all interpretations are equal,” heard in the common refrain, “well, that’s just your interpretation.”

There can be different, competing, and contradictory interpretations of the same phenomena : As noted at the beginning of this chapter, we acknowledge that subjectivity matters, and, unavoidably, it impacts one’s interpretations. As Barrett notes (2000) , “Interpretations are often based on a worldview” (p. 116).

Interpretations are not (and can’t be) “right,” but instead, they can be more or less reasonable, convincing, and informative : There is never one “true” interpretation, but some interpretations are more compelling than others.

Interpretations can be judged by coherence, correspondence, and inclusiveness : Does the argument/interpretation make sense (coherence)? Does the interpretation fit the data (correspondence)? Have all data been attended to, including outlier data that don’t necessarily support identified themes (inclusiveness)?

Interpretation is ultimately a communal endeavor : Initial interpretations may be incomplete, nearsighted, and/or narrow, but eventually, these interpretations become richer, broader, and more inclusive. Feminist revisionist history projects are an exemplary case. Over time, the writing, art, and cultural contributions of countless women, previously ignored, diminished, or distorted, have come to be accepted as prominent contributions given serious consideration.

So, meaning is conferred; interpretations are socially constructed arguments; multiple interpretations are to be expected; and some interpretations are better than others. As we discuss later in this chapter, what makes an interpretation “better” often hinges on the purpose/goals of the research in question. Interpretations designed to generate theory, or generalizable rules, will be “better” for responding to research questions aligned with the aims of more traditional quantitative/positivist research, whereas interpretations designed to construct meanings through social interaction, to generate multiple perspectives, and to represent the context-specific perspectives of the research participants are “better” for researchers constructing thick, contextually rich descriptions, stories, or narratives. The former relies on more “atomistic” interpretive strategies, whereas the latter adheres to a more “holistic” approach ( Willis, 2007 ). Both approaches to analysis/interpretation are addressed in more detail later in this chapter.

At this point, readers might ask, why does interpretation matter, anyway? Our response to this question involves the distinctive nature of interpretation and the ability of the interpretive process to put unique fingerprints on an otherwise relatively static set of data. Once interview data are collected and transcribed (and we realize that even the process of transcription is, in part, interpretive), documents are collected, and observations are recorded, qualitative researchers could just, in good faith and with fidelity, represent the data in as straightforward ways as possible, allowing readers to “see for themselves” by sharing as much actual data (e.g., the transcribed words of the research participants) as possible. This approach, however, includes analysis, what we have defined as summarizing and organizing data for presentation, but it falls short of what we actually reference and define as interpretation—attempting to explain the meaning of others’ words and actions. “While early efforts at qualitative research might have stopped at description, it is now more generally accepted that a qualitative researcher adds understanding and interpretation to the description” ( Lichtman, 2006 , p. 8).

As we are fond of the arts and arts-based approaches to qualitative research, an example from the late jazz drummer, Buddy Rich, seems fitting. Rich explains the importance of having the flexibility to interpret: “I don’t think any arranger should ever write a drum part for a drummer, because if a drummer can’t create his own interpretation of the chart, and he plays everything that’s written, he becomes mechanical; he has no freedom.” The same is true for qualitative researchers; without the freedom to interpret, the researcher merely regurgitates, attempting to share with readers/reviewers exactly what the research subjects shared with him or her. It is only through interpretation that the researcher, as collaborator with unavoidable subjectivities, is able to construct unique, contextualized meaning. Interpretation then, in this sense, is knowledge construction.

In closing this section, we’ll illustrate the analysis versus interpretation distinction with the following transcript excerpt. In this study, the authors ( Trent & Zorko, 2006 ) were studying student teaching from the perspective of K–12 students. This quote comes from a high school student in a focus group interview. She is describing a student teacher she had:

The right-hand column contains “codes” or labels applied to parts of the transcript text. Coding will be discussed in more depth later in this chapter, but, for now, note that the codes are mostly summarizing the main ideas of the text, sometimes using the exact words of the research participant. This type of coding is a part of what we’ve called analysis—organizing and summarizing the data. It’s a way of beginning to say, “what is” there. As noted, though, most qualitative researchers go deeper. They want to know more than “what is”; they also ask, “what does it mean?” This is a question of interpretation.

Specific to the transcript excerpt, researchers might next begin to cluster the early codes into like groups. For example, the teacher “felt targeted,” “assumed kids were going to behave inappropriately,” and appeared to be “overwhelmed.” A researcher might cluster this group of codes in a category called “teacher feelings and perceptions” and may then cluster the codes “could not control class,” and “students off task” into a category called “classroom management.” The researcher then, in taking a fresh look at these categories and the included codes, may begin to conclude that what’s going on in this situation is that the student teacher does not have sufficient training in classroom management models and strategies and may also be lacking the skills she needs to build relationships with her students. These then would be interpretations, persuasive arguments connected to the study’s data. In this specific example, the researchers might proceed to write a memo about these emerging interpretations. In this memo, they might more clearly define their early categories and may also look through other data to see if there are other codes or categories that align with or overlap with this initial analysis. They might write further about their emergent interpretations and, in doing so, may inform future data collection in ways that will allow them to either support or refute their early interpretations. These researchers will also likely find that the processes of analysis and interpretation are inextricably intertwined. Good interpretations very often depend on thorough and thoughtful analyses.

How Do Methodology, Data, and the Researcher/Self Impact Interpretation in Qualitative Research?

Methodological conventions guide interpretation and the use of interpretive strategies. For example, in grounded theory and in similar methodological traditions, “formal analysis begins early in the study and is nearly completed by the end of data collection” ( Bogdan & Biklen, 2003 , p. 66). Alternatively, for researchers from other traditions, for example, case study researchers, “Formal analysis and theory development [interpretation] do not occur until after the data collection is near complete” (p. 66).

Researchers subscribing to methodologies that prescribe early data analysis and interpretation may employ methods like analytic induction or the constant comparison method. In using analytic induction, researchers develop a rough definition of the phenomena under study; collect data to compare to this rough definition; modify the definition as needed, based on cases that both fit and don’t fit the definition; and finally, establish a clear, universal definition (theory) of the phenomena (Robinson, 1951, cited in Bogdan & Biklen, 2003 , p. 65). Generally, those using a constant comparison approach begin data collection immediately; identify key issues, events, and activities related to the study that then become categories of focus; collect data that provide incidents of these categories; write about and describe the categories, accounting for specific incidents and seeking others; discover basic processes and relationships; and, finally, code and write about the categories as theory, “grounded” in the data ( Glaser, 1965 ). Although processes like analytic induction and constant comparison can be listed as “steps” to follow, in actuality, these are more typically recursive processes in which the researcher repeatedly goes back and forth between the data and emerging analyses and interpretations.

In addition to methodological conventions that prescribe data analysis early (e.g., grounded theory) or later (e.g., case study) in the inquiry process, methodological approaches also impact the general approach to analysis and interpretation. Ellingson (2011) situates qualitative research methodologies on a continuum spanning “science”-like approaches on one end juxtaposed with “art”-like approaches on the other.

Researchers pursuing a more science-oriented approach seek valid, reliable, generalizable knowledge; believe in neutral, objective researchers; and ultimately claim single, authoritative interpretations. Researchers adhering to these science-focused, post-positivistic approaches may count frequencies, emphasize the validity of the employed coding system, and point to intercoder reliability and random sampling as criteria that bolsters the research credibility. Researchers at or near the science end of the continuum might employ analysis and interpretation strategies that include “paired comparisons,” “pile sorts,” “word counts,” identifying “key words in context,” and “triad tests” ( Ryan & Bernard, 2000 , pp. 770–776). These researchers may ultimately seek to develop taxonomies or other authoritative final products that organize and explain the collected data.

For example, in a study we conducted about preservice teachers’ experiences learning to teach second-language learners, the researchers collected larger datasets and used a statistical analysis package to analyze survey data, and the resultant findings included descriptive statistics. These survey results were supported with open-ended, qualitative data. For example, one of the study’s findings was “a strong majority of candidates (96%) agreed that an immersion approach alone will not guarantee academic or linguistic success for second language learners.” In narrative explanations, one preservice teacher remarked, “there has to be extra instructional efforts to help their students learn English... they won’t learn English by merely sitting in the classrooms” ( Cho, Rios, Trent, & Mayfield, 2012 , p. 75).

Methodologies on the “art” side of Ellingson’s (2011) continuum, alternatively, “value humanistic, openly subjective knowledge, such as that embodied in stories, poetry, photography, and painting” (p. 599). Analysis and interpretation in these (often more contemporary) methodological approaches strive not for “social scientific truth,” but instead are formulated to “enable us to learn about ourselves, each other, and the world through encountering the unique lens of a person’s (or a group’s) passionate rendering of a reality into a moving, aesthetic expression of meaning” (p. 599). For these “artistic/interpretivists, truths are multiple, fluctuating and ambiguous” (p. 599). Methodologies taking more artistic, subjective approaches to analysis and interpretation include autoethnography, testimonio, performance studies, feminist theorists/researchers, and others from related critical methodological forms of qualitative practice.

As an example, one of us engaged in an artistic inquiry with a group of students in an art class for elementary teachers. We called it “Dreams as Data” and, among the project aims, we wanted to gather participants’ “dreams for education in the future” and display these dreams in an accessible, interactive, artistic display (see Trent, 2002 ). The intent here was not to statistically analyze the dreams/data; instead, it was more universal. We wanted, as Ellingson (2011) noted, to use participant responses in ways that “enable us to learn about ourselves, each other, and the world.” The decision was made to leave responses intact and to share the whole/raw dataset in the artistic display in ways that allowed the viewers to holistically analyze and interpret for themselves. The following text is an excerpt from one response:

Almost a century ago, John Dewey eloquently wrote about the need to imagine and create the education that ALL children deserve, not just the richest, the Whitest, or the easiest to teach. At the dawn of this new century, on some mornings, I wake up fearful that we are further away from this ideal than ever.... Collective action, in a critical, hopeful, joyful, anti-racist and pro-justice spirit, is foremost in my mind as I reflect on and act in my daily work.... Although I realize the constraints on teachers and schools in the current political arena, I do believe in the power of teachers to stand next to, encourage, and believe in the students they teach—in short, to change lives. ( Trent, 2002 , p. 49)

In sum, researchers whom Ellingson (2011) characterizes as being on the science end of the continuum typically use more detailed or “atomistic” strategies to analyze and interpret qualitative data, whereas those toward the artistic end most often employ more holistic strategies. Both of these general approaches to qualitative data analysis and interpretation, atomistic and holistic, will be addressed later in this chapter.

As noted, qualitative researchers attend to data in a wide variety of ways depending on paradigmatic and epistemological beliefs, methodological conventions, and the purpose/aims of the research. These factors impact the kinds of data collected and the ways these data are ultimately analyzed and interpreted. For example, life history or testimonio researchers conduct extensive individual interviews, ethnographers record detailed observational notes, critical theorists may examine documents from pop culture, and ethnomethodologists may collect videotapes of interaction for analysis and interpretation.

In addition to the wide range of data types that are collected by qualitative researchers (and most qualitative researchers collect multiple forms of data), qualitative researchers, again influenced by the factors noted earlier, employ a variety of approaches to analyzing and interpreting data. As mentioned earlier in this article, some advocate for a detailed/atomistic, fine-grained approach to data (see e.g., Miles & Huberman, 1994 ); others, a more broad-based, holistic, “eyeballing” of the data. “Eyeballers reject the more structured approaches to analysis that break down the data into small units and, from the perspective of the eyeballers, destroy the wholeness and some of the meaningfulness of the data” ( Willis, 2007 , p. 298).

Regardless, we assert, as illustrated in Figure 30.1 , that as the process evolves, data collection becomes less prominent later in the process, as interpretation and making sense/meaning of the data becomes more prominent. It is through this emphasis on interpretation that qualitative researchers put their individual imprints on the data, allowing for the emergence of multiple, rich perspectives. This space for interpretation allows researchers the “freedom” Buddy Rich alluded to in his quote about interpreting musical charts. Without this freedom, Rich noted that the process would be simply “mechanical.” Furthermore, allowing space for multiple interpretations nourishes the perspectives of many

As emphasis on data/data collection decreases, emphasis on interpretation increases.

others in the community. Writer and theorist Meg Wheatley explains, “everyone in a complex system has a slightly different interpretation. The more interpretations we gather, the easier it becomes to gain a sense of the whole.”

In addition to the roles methodology and data play in the interpretive process, perhaps the most important is the role of the self/the researcher in the interpretive process. “She is the one who asks the questions. She is the one who conducts the analyses. She is the one who decides who to study and what to study. The researcher is the conduit through which information is gathered and filtered” ( Lichtman, 2006 , p. 16). Eisner (1991) supports the notion of the researcher “self as instrument,” noting that expert researchers don’t simply know what to attend to, but also what to neglect. He describes the researcher’s role in the interpretive process as combining sensibility , the ability to observe and ascertain nuances, with schema , a deep understanding or cognitive framework of the phenomena under study.

Barrett (2007) describes self/researcher roles as “transformations” (p. 418) at multiple points throughout the inquiry process: early in the process, researchers create representations through data generation, conducting observations and interviews and collecting documents and artifacts. Another “transformation occurs when the ‘raw’ data generated in the field are shaped into data records by the researcher. These data records are produced through organizing and reconstructing the researcher’s notes and transcribing audio and video recordings in the form of permanent records that serve as the ‘evidentiary warrants’ of the generated data. The researcher strives to capture aspects of the phenomenal world with fidelity by selecting salient aspects to incorporate into the data record” (p. 418). Transformation continues when the researcher analyzes, codes, categorizes, and explores patterns in the data (the process we call analysis). Transformations also involve interpreting what the data mean and relating these “interpretations to other sources of insight about the phenomena, including findings from related research, conceptual literature, and common experience.... Data analysis and interpretation are often intertwined and rely upon the researcher’s logic, artistry, imagination, clarity, and knowledge of the field under study” ( Barrett, 2007 , p. 418).

We mentioned the often-blended roles of participation and observation earlier in this chapter. The role(s) of the self/researcher are often described as points along a “participant/observer continuum” (see, e.g., Bogdan & Biklen, 2003 ). On the far “observer” end of this continuum, the researcher situates as detached, tries to be inconspicuous (so as not to impact/disrupt the phenomena under study), and approaches the studied context as if viewing it from behind a one-way mirror. On the opposite, “participant” end, the researcher is completely immersed and involved in the context. It would be difficult for an outsider to distinguish between researcher and subjects. For example, “some feminist researchers and some postmodernists take on a political stance as well and have an agenda that places the researcher in an activist posture. These researchers often become quite involved with the individuals they study and try to improve their human condition” ( Lichtman, 2006 , p. 9).

We assert that most researchers fall somewhere between these poles. We believe that complete detachment is both impossible and misguided. In doing so, we, along with many others, acknowledge (and honor) the role of subjectivity, the researcher’s beliefs, opinions, biases, and predispositions. Positivist researchers seeking objective data and accounts either ignore the impact of subjectivity or attempt to drastically diminish/eliminate its impact. Even qualitative researchers have developed methods to avoid researcher subjectivity affecting research data collection, analysis, and interpretation. For example, foundational phenomenologist Husserl (1962/1913) developed the concept of “bracketing,” what Lichtman describes as “trying to identify your own views on the topic and then putting them aside” (2006, p. 13). Like Slotnick and Janesick (2011) , we ultimately claim, “it is impossible to bracket yourself” (p. 1358). Instead, we take a balanced approach, like Eisner, understanding that subjectivity allows researchers to produce the rich, idiosyncratic, insightful, and yet data-based interpretations and accounts of lived experience that accomplish the primary purposes of qualitative inquiry. “Rather than regarding uniformity and standardization as the summum bonum, educational criticism [Eisner’s form of qualitative research] views unique insight as the higher good” ( Eisner, 1991 , p. 35). That said, we also claim that, just because we acknowledge and value the role of researcher subjectivity, researchers are still obligated to ground their findings in reasonable interpretations of the data. Eisner (1991) explains:

This appreciation for personal insight as a source of meaning does not provide a license for freedom. Educational critics must provide evidence and reasons. But they reject the assumption that unique interpretation is a conceptual liability in understanding, and they see the insights secured from multiple views as more attractive than the comforts provided by a single right one. (p. 35)

Connected to this participant/observer continuum is the way the researcher positions him- or herself in relation to the “subjects” of the study. Traditionally, researchers, including early qualitative researchers, anthropologists, and ethnographers, referenced those studied as “subjects.” More recently, qualitative researchers better understand that research should be a reciprocal process in which both researcher and the foci of the research should derive meaningful benefit. Researchers aligned with this thinking frequently use the term “participants” to describe those groups and individuals included in a study. Going a step farther, some researchers view research participants as experts on the studied topic and as equal collaborators in the meaning-making process. In these instances, researchers often use the terms “co-researchers” or “co-investigators.”

The qualitative researcher, then, plays significant roles throughout the inquiry process. These roles include transforming data, collaborating with research participants or co-researchers, determining appropriate points to situate along the participant/observer continuum, and ascribing personal insights, meanings, and interpretations that are both unique and justified with data exemplars. Performing these roles unavoidably impacts and changes the researcher. “Since, in qualitative research the individual is the research instrument through which all data are passed, interpreted, and reported, the scholar’s role is constantly evolving as self evolves” ( Slotnick & Janesick, 2011 , p. 1358).

As we note later, key in all this is for researchers to be transparent about the topics discussed in the preceding section: what methodological conventions have been employed and why? How have data been treated throughout the inquiry to arrive at assertions and findings that may or may not be transferable to other idiosyncratic contexts? And, finally, in what ways has the researcher/self been situated in and impacted the inquiry? Unavoidably, we assert, the self lies at the critical intersection of data and theory, and, as such, two legs of this stool, data and researcher, interact to create the third, theory.

How Do Qualitative Researchers Engage in the Process of Interpretation?

Theorists seem to have a propensity to dichotomize concepts, pulling them apart and placing binary opposites on far ends of conceptual continuums. Qualitative research theorists are no different, and we have already mentioned some of these continua in this chapter. For example, in the last section, we discussed the participant–observer continuum. Earlier, we referenced both Willis’s (2007) conceptualization of “atomistic” versus “holistic” approaches to qualitative analysis and interpretation and Ellingson’s (2011) science–art continuum. Each of these latter two conceptualizations inform “how qualitative researchers engage in the process of interpretation.”

Willis (2007) shares that the purpose of a qualitative project might be explained as “what we expect to gain from research” (p. 288). The purpose, or “what we expect to gain,” then guides and informs the approaches researchers might take to interpretation. Some researchers, typically positivist/postpositivist, conduct studies that aim to test theories about how the world works and/or people behave. These researchers attempt to discover general laws, truths, or relationships that can be generalized. Others, less confident in the ability of research to attain a single, generalizable law or truth, might seek “local theory.” These researchers still seek truths, but “instead of generalizable laws or rules, they search for truths about the local context... to understand what is really happening and then to communicate the essence of this to others” ( Willis, 2007 , p. 291). In both of these purposes, researchers employ atomistic strategies in an inductive process in which researchers “break the data down into small units and then build broader and broader generalizations as the data analysis proceeds” (p. 317). The earlier mentioned processes of analytic induction, constant comparison, and grounded theory fit within this conceptualization of atomistic approaches to interpretation. For example, a line-by-line coding of a transcript might begin an atomistic approach to data analysis.

Alternatively, other researchers pursue distinctly different aims. Researchers with an “objective description” purpose focus on accurately describing the people and context under study. These researchers adhere to standards and practices designed to achieve objectivity, and their approach to interpretation falls between the binary atomistic/holistic distinction.

The purpose of hermeneutic approaches to research is to “understand the perspectives of humans. And because understanding is situational, hermeneutic research tends to look at the details of the context in which the study occurred. The result is generally rich data reports that include multiple perspectives” ( Willis, 2007 , p. 293).

Still other researchers see their purpose as the creation of stories or narratives that utilize “a social process that constructs meaning through interaction... it is an effort to represent in detail the perspectives of participants... whereas description produces one truth about the topic of study, storytelling may generate multiple perspectives, interpretations, and analyses by the researcher and participants” ( Willis, 2007 , p. 295).

In these latter purposes (hermeneutic, storytelling, narrative production), researchers typically employ more holistic strategies. “Holistic approaches tend to leave the data intact and to emphasize that meaning must be derived for a contextual reading of the data rather than the extraction of data segments for detailed analysis” (p. 297). This was the case with the “Dreams as Data” project mentioned earlier.

We understand the propensity to dichotomize, situate concepts as binary opposites, and to create neat continua between these polar descriptors. These sorts of reduction and deconstruction support our understandings and, hopefully, enable us to eventually reconstruct these ideas in meaningful ways. Still, in reality, we realize most of us will, and should, work in the middle of these conceptualizations in fluid ways that allow us to pursue strategies, processes, and theories most appropriate for the research task at hand. As noted, Ellingson (2011) sets up another conceptual continuum, but, like ours, her advice is to “straddle multiple points across the field of qualitative methods” (p. 595). She explains, “I make the case for qualitative methods to be conceptualized as a continuum anchored by art and science, with vast middle spaces that embody infinite possibilities for blending artistic, expository, and social scientific ways of analysis and representation” (p. 595).

We explained at the beginning of this chapter that we view analysis as organizing and summarizing qualitative data, and interpretation as constructing meaning. In this sense, analysis allows us to “describe” the phenomena under study. It enables us to succinctly answer “what” and “how” questions and ensures that our descriptions are grounded in the data collected. Descriptions, however, rarely respond to questions of “why?” Why questions are the domain of interpretation, and, as noted throughout this text, interpretation is complex. “Traditionally, qualitative inquiry has concerned itself with what and how questions... qualitative researchers typically approach why questions cautiously, explanation is tricky business” ( Gubrium & Holstein, 2000 , p. 502). Eisner (1991) describes this distinctive nature of interpretation: “it means that inquirers try to account for [interpretation] what they have given account of ” (p. 35).

Our focus here is on interpretation, but interpretation requires analysis, for without having clear understandings of the data and its characteristics, derived through systematic examination and organization (e.g., coding, memoing, categorizing, etc.), “interpretations” resulting from inquiry will likely be incomplete, uninformed, and inconsistent with the constructed perspectives of the study participants. Fortunately for qualitative researchers, we have many sources that lead us through analytic processes. We earlier mentioned the accepted processes of analytic induction and the constant comparison method. These detailed processes (see e.g., Bogdan & Biklen, 2003 ) combine the inextricably linked activities of analysis and interpretation, with “analysis” more typically appearing as earlier steps in the process and meaning construction—“interpretation”—happening later.

A wide variety of resources support researchers engaged in the processes of analysis and interpretation. Saldaña (2011) , for example, provides a detailed description of coding types and processes. He shows researchers how to use process coding (uses gerunds, “-ing” words to capture action), in vivo coding (uses the actual words of the research participants/subjects), descriptive coding (uses nouns to summarize the data topics), versus coding (uses “vs.” to identify conflicts and power issues), and values coding (identifies participants’ values, attitudes, and/or beliefs). To exemplify some of these coding strategies, we include an excerpt from a transcript of a meeting of a school improvement committee. In this study, the collaborators were focused on building “school community.” This excerpt illustrates the application of a variety of codes described by Saldaña to this text:

To connect and elaborate the ideas developed in coding, Saldaña (2011) suggests researchers categorize the applied codes, write memos to deepen understandings and illuminate additional questions, and identify emergent themes. To begin the categorization process, Saldaña recommends all codes be “classified into similar clusters... once the codes have been classified, a category label is applied to them” (p. 97). So, in continuing with the study of school community example coded here, the researcher might create a cluster/category called: “Value of Collaboration,” and in this category might include the codes, “relationships,” “building community,” and “effective strategies.”

Having coded and categorized a study’s various data forms, a typical next step for researchers is to write “memos” or “analytic memos.” Writing analytic memos allows the researcher(s) to “set in words your interpretation of the data... an analytic memo further articulates your... thinking processes on what things may mean... as the study proceeds, however, initial and substantive analytic memos can be revisited and revised for eventual integration into the report itself” ( Saldaña, 2011 , p. 98). In the study of student teaching from K–12 students’ perspectives ( Trent & Zorko, 2006 ), we noticed throughout our analysis a series of focus group interview quotes coded “names.” The following quote from a high school student is representative of many others:

I think that, ah, they [student teachers] should like know your face and your name because, uh, I don’t like it if they don’t and they’ll just like... cause they’ll blow you off a lot easier if they don’t know, like our new principal is here... he is, like, he always, like, tries to make sure to say hi even to the, like, not popular people if you can call it that, you know, and I mean, yah, and the people that don’t usually socialize a lot, I mean he makes an effort to know them and know their name like so they will cooperate better with him.

Although we didn’t ask the focus groups a specific question about whether or not student teachers knew the K–12 students’ names, the topic came up in every focus group interview. We coded the above excerpt and the others, “knowing names,” and these data were grouped with others under the category “relationships.” In an initial analytic memo about this, the researchers wrote:

STUDENT TEACHING STUDY—MEMO #3 “Knowing Names as Relationship Building” Most groups made unsolicited mentions of student teachers knowing, or not knowing, their names. We haven’t asked students about this, but it must be important to them because it always seems to come up. Students expected student teachers to know their names. When they did, students noticed and seemed pleased. When they didn’t, students seemed disappointed, even annoyed. An elementary student told us that early in the semester, “she knew our names... cause when we rose [sic] our hands, she didn’t have to come and look at our name tags... it made me feel very happy.” A high schooler, expressing displeasure that his student teacher didn’t know students’ names, told us, “They should like know your name because it shows they care about you as a person. I mean, we know their names, so they should take the time to learn ours too.” Another high school student said that even after 3 months, she wasn’t sure the student teacher knew her name. Another student echoed, “same here.” Each of these students asserted that this (knowing students’ names) had impacted their relationship with the student teacher. This high school student focus group stressed that a good relationship, built early, directly impacts classroom interaction and student learning. A student explained it like this: “If you get to know each other, you can have fun with them... they seem to understand you more, you’re more relaxed, and learning seems easier.” Open in new tab Meeting Transcript .  Process Coding .  Let’s start talking about what we want to get out of this. What I’d like to hear is each of us sharing what we’re doing relative to this idea of building community. “Here’s what I’m doing. Here’s what worked. Here’s what didn’t work. I’m happy with this. I’m sad with this,” and just hearing each of us reflecting about what we’re doing I think will be interesting. That collaboration will be extremely valuable in terms of not only our relationships with one another, but also understanding the idea of community in more specific and concrete ways. Talking Sharing Building Listening Collaborating Understanding IN VIVO CODING Let’s start talking about what we want to get out of this. What I’d like to hear is each of us sharing what we’re doing relative to this idea of building community. “Here’s what I’m doing. Here’s what worked. Here’s what didn’t work. I’m happy with this. I’m sad with this,” and just hearing each of us reflecting about what we’re doing I think will be interesting. That collaboration will be extremely valuable in terms of not only our relationships with one another, but also understanding the idea of community in more specific and concrete ways. Talking about what we want to get out of this Each of us sharing Hearing each of us reflecting Collaboration will be extremely valuable Relationships DESCRIPTIVE CODING Let’s start talking about what we want to get out of this. What I’d like to hear is each of us sharing what we’re doing relative to this idea of building community. “Here’s what I’m doing. Here’s what worked. Here’s what didn’t work. I’m happy with this. I’m sad with this,” and just hearing each of us reflecting about what we’re doing I think will be interesting. That collaboration will be extremely valuable in terms of not only our relationships with one another, but also understanding the idea of community in more specific and concrete ways. Open, participatory discussion Identification of effective strategies Collaborative, productive relationships Robust Understandings VERSUS CODING Let’s start talking about what we want to get out of this. What I’d like to hear is each of us sharing what we’re doing relative to this idea of building community. “Here’s what I’m doing. Here’s what worked. Here’s what didn’t work. I’m happy with this. I’m sad with this,” and just hearing each of us reflecting about what we’re doing I think will be interesting. That collaboration will be extremely valuable in terms of not only our relationships with one another, but also understanding the idea of community in more specific and concrete ways. Effective vs. Ineffective strategies Positive reflections vs. negative reflections VALUES CODING Let’s start talking about what we want to get out of this. What I’d like to hear is each of us sharing what we’re doing relative to this idea of building community. “Here’s what I’m doing. Here’s what worked. Here’s what didn’t work. I’m happy with this. I’m sad with this,” and just hearing each of us reflecting about what we’re doing I think will be interesting. That collaboration will be extremely valuable in terms of not only our relationships with one another, but also understanding the idea of community in more specific and concrete ways. Sharing Building community Reflection Collaboration Relationships Deeper Understandings Meeting Transcript .  Process Coding .  Let’s start talking about what we want to get out of this. What I’d like to hear is each of us sharing what we’re doing relative to this idea of building community. “Here’s what I’m doing. Here’s what worked. Here’s what didn’t work. I’m happy with this. I’m sad with this,” and just hearing each of us reflecting about what we’re doing I think will be interesting. That collaboration will be extremely valuable in terms of not only our relationships with one another, but also understanding the idea of community in more specific and concrete ways. Talking Sharing Building Listening Collaborating Understanding IN VIVO CODING Let’s start talking about what we want to get out of this. What I’d like to hear is each of us sharing what we’re doing relative to this idea of building community. “Here’s what I’m doing. Here’s what worked. Here’s what didn’t work. I’m happy with this. I’m sad with this,” and just hearing each of us reflecting about what we’re doing I think will be interesting. That collaboration will be extremely valuable in terms of not only our relationships with one another, but also understanding the idea of community in more specific and concrete ways. Talking about what we want to get out of this Each of us sharing Hearing each of us reflecting Collaboration will be extremely valuable Relationships DESCRIPTIVE CODING Let’s start talking about what we want to get out of this. What I’d like to hear is each of us sharing what we’re doing relative to this idea of building community. “Here’s what I’m doing. Here’s what worked. Here’s what didn’t work. I’m happy with this. I’m sad with this,” and just hearing each of us reflecting about what we’re doing I think will be interesting. That collaboration will be extremely valuable in terms of not only our relationships with one another, but also understanding the idea of community in more specific and concrete ways. Open, participatory discussion Identification of effective strategies Collaborative, productive relationships Robust Understandings VERSUS CODING Let’s start talking about what we want to get out of this. What I’d like to hear is each of us sharing what we’re doing relative to this idea of building community. “Here’s what I’m doing. Here’s what worked. Here’s what didn’t work. I’m happy with this. I’m sad with this,” and just hearing each of us reflecting about what we’re doing I think will be interesting. That collaboration will be extremely valuable in terms of not only our relationships with one another, but also understanding the idea of community in more specific and concrete ways. Effective vs. Ineffective strategies Positive reflections vs. negative reflections VALUES CODING Let’s start talking about what we want to get out of this. What I’d like to hear is each of us sharing what we’re doing relative to this idea of building community. “Here’s what I’m doing. Here’s what worked. Here’s what didn’t work. I’m happy with this. I’m sad with this,” and just hearing each of us reflecting about what we’re doing I think will be interesting. That collaboration will be extremely valuable in terms of not only our relationships with one another, but also understanding the idea of community in more specific and concrete ways. Sharing Building community Reflection Collaboration Relationships Deeper Understandings

As noted in these brief examples, coding, categorizing, and writing memos about a study’s data are all accepted processes for data analysis and allow researchers to begin constructing new understandings and forming interpretations of the studied phenomena. We find the qualitative research literature to be particularly strong in offering support and guidance for researchers engaged in these analytic practices. In addition to those already noted in this chapter, we have found the following resources provide practical, yet theoretically grounded approaches to qualitative data analysis. For more detailed, procedural, or atomistic approaches to data analysis, we direct researchers to Miles and Huberman’s classic 1994 text, Qualitative Data Analysis , and Ryan and Bernard’s (2000) chapter on “Data Management and Analysis Methods.” For analysis and interpretation strategies falling somewhere between the atomistic and holistic poles, we suggest Hesse-Biber and Leavy’s (2011) chapter, “Analysis and Interpretation of Qualitative Data,” in their book, The Practice of Qualitative Research (2nd edition); Lichtman’s chapter, “Making Meaning From Your Data,” in her book Qualitative Research in Education: A User’s Guide; and “Processing Fieldnotes: Coding and Memoing” a chapter in Emerson, Fretz, and Shaw’s (1995) book, Writing Ethnographic Fieldnotes . Each of these sources succinctly describes the processes of data preparation, data reduction, coding and categorizing data, and writing memos about emergent ideas and findings. For more holistic approaches, we have found Denzin and Lincoln’s (2007)   Collecting and Interpreting Qualitative Materials , and Ellis and Bochner’s (2000) chapter “Autoethnography, Personal Narrative, Reflexivity,” to both be very informative.

We have not yet mentioned the use of computer software for data analysis. The use of CAQDAS (Computer Assisted Qualitative Data Analysis Software) has become prevalent. That said, it is beyond the scope of this chapter because, generally, the software is very useful for analysis, but only human researchers can interpret in the ways we describe. Multiple sources are readily available for those interested in exploring computer-assisted analysis. We have found the software to be particularly useful when working with large sets of data.

Even after reviewing the multiple resources for treating data included here, qualitative researchers might still be wondering, “but exactly how do we interpret?” In the remainder of this section, and in the concluding section of this chapter, we more concretely provide responses to this question, and, in closing, propose a framework for researchers to utilize as they engage in the complex, ambiguous, and yet exciting process of constructing meanings and new understandings from qualitative sources.

These meanings and understandings are often presented as theory, but theories in this sense should be viewed more as “guides to perception” as opposed to “devices that lead to the tight control or precise prediction of events” ( Eisner, 1991 , p. 95). Perhaps Erickson’s (1986) concept of “assertions” is a more appropriate aim for qualitative researchers. He claimed that assertions are declarative statements; they include a summary of the new understandings, and they are supported by evidence/data. These assertions are open to revision and are revised when disconfirming evidence requires modification. Assertions, theories, or other explanations resulting from interpretation in research are typically presented as “findings” in written research reports. Belgrave and Smith (2002) emphasize the importance of these interpretations (as opposed to descriptions), “the core of the report is not the events reported by the respondent, but rather the subjective meaning of the reported events for the respondent” (p. 248).

Mills (2007) views interpretation as responding to the question, “So what?” He provides researchers a series of concrete strategies for both analysis and interpretation. Specific to interpretation, Mills suggests a variety of techniques, including the following:

“ Extend the Analysis ”: In doing so, researchers ask additional questions about the research. The data appear to say X, but could it be otherwise? In what ways do the data support emergent finding X? And, in what ways do they not?

“ Connect Findings with Personal Experience ”: Using this technique, researchers share interpretations based on their intimate knowledge of the context, the observed actions of the individuals in the studied context, and the data points that support emerging interpretations, as well as their awareness of discrepant events or outlier data. In a sense, the researcher is saying, “based on my experiences in conducting this study, this is what I make of it all.”

“ Seek the Advice of ‘Critical’ Friends ”: In doing so, researchers utilize trusted colleagues, fellow researchers, experts in the field of study, and others to offer insights, alternative interpretations, and the application of their own unique lenses to a researcher’s initial findings. We especially like this strategy because we acknowledge that, too often, qualitative interpretation is a “solo” affair.

“ Contextualize the Findings in the Literature ”: This allows researchers to compare their interpretations to others writing about and studying the same/similar phenomena. The results of this contextualization may be that the current study’s findings correspond with the findings of other researchers. The results might, alternatively, differ from the findings of other researchers. In either instance, the researcher can highlight his or her unique contributions to our understanding of the topic under study.

“ Turn to Theory” : Mills defines theory as “an analytical and interpretive framework that helps the researcher make sense of ‘what is going on’ in the social setting being studied.” In turning to theory, researchers search for increasing levels of abstraction and move beyond purely descriptive accounts. Connecting to extant or generating new theory enables researchers to link their work to the broader contemporary issues in the field. (p. 136)

Other theorists offer additional advice for researchers engaged in the act of interpretation. Richardson (1995) reminds us to account for the power dynamics in the researcher–researched relationship and notes that, in doing so, we can allow for oppressed and marginalized voices to be heard in context. Bogdan and Biklen (2003) suggest that researchers engaged in interpretation revisit foundational writing about qualitative research, read studies related to the current research, ask evaluative questions (e.g., is what I’m seeing here good or bad?), ask about implications of particular findings/interpretations, think about the audience for interpretations, look for stories and incidents that illustrate a specific finding/interpretation, and attempt to summarize key interpretations in a succinct paragraph. All of these suggestions can be pertinent in certain situations and with particular methodological approaches. In the next and closing section of this chapter, we present a framework for interpretive strategies we believe will support, guide, and be applicable to qualitative researchers across multiple methodologies and paradigms.

In What Ways Can a Framework for Interpretation Strategies Support Qualitative Researchers Across Multiple Methodological and Paradigmatic Views?

The process of qualitative research is often compared to a journey, one without a detailed itinerary and ending, but instead a journey with general direction and aims and yet an open-endedness that adds excitement and thrives on curiosity. Qualitative researchers are travelers. They travel physically to field sites; they travel mentally through various epistemological, theoretical, and methodological grounds; they travel through a series of problem finding, access, data collection, and data analysis processes; and, finally—the topic of this chapter—they travel through the process of making meaning out of all this physical and cognitive travel via interpretation.

Although travel is an appropriate metaphor to describe the journey of qualitative researchers, we’ll also use “travel” to symbolize a framework for qualitative research interpretation strategies. By design, this is a framework that applies across multiple paradigmatic, epistemological, and methodological traditions. The application of this framework is not formulaic or highly prescriptive, it is also not an “anything goes” approach. It falls, and is applicable, between these poles, giving concrete (suggested) direction to qualitative researchers wanting to make the most out of the interpretations that result from their research, and yet allows the necessary flexibility for researchers to employ the methods, theories, and approaches they deem most appropriate to the research problem(s) under study.

TRAVEL, a Comprehensive Approach to Qualitative Interpretation

In using the word “TRAVEL” as a mnemonic device, our aim is to highlight six essential concepts we argue all qualitative researchers should attend to in the interpretive process: Transparency, Reflexivity, Analysis, Validity, Evidence, and Literature. The importance of each is addressed here.

Transparency , as a research concept seems, well... transparent. But, too often, we read qualitative research reports and are left with many questions: How were research participants and the topic of study selected/excluded? How were the data collected, when, and for how long? Who analyzed and interpreted these data? A single researcher? Multiple? What interpretive strategies were employed? Are there data points that substantiate these interpretations/findings? What analytic procedures were used to organize the data prior to making the presented interpretations? In being transparent about data collection, analysis, and interpretation processes, researchers allow reviewers/readers insight into the research endeavor, and this transparency leads to credibility for both researcher and researcher’s claims. Altheide and Johnson (2011) explain, “There is great diversity of qualitative research.... While these approaches differ, they also share an ethical obligation to make public their claims, to show the reader, audience, or consumer why they should be trusted as faithful accounts of some phenomenon” (p. 584). This includes, they note, articulating “what the different sources of data were, how they were interwoven, and... how subsequent interpretations and conclusions are more or less closely tied to the various data... the main concern is that the connection be apparent, and to the extent possible, transparent” (p. 590).

In the “Dreams as Data” art and research project noted earlier, transparency was addressed in multiple ways. Readers of the project write-up were informed that interpretations resulting from the study, framed as “themes,” were a result of collaborative analysis that included insights from both students and instructor. Viewers of the art installation/data display had the rare opportunity to see all participant responses. In other words, viewers had access to the entire raw dataset (see Trent, 2002 ). More frequently, we encounter only research “findings” already distilled, analyzed, and interpreted in research accounts, often by a single researcher. Allowing research consumers access to the data to interpret for themselves in the “dreams” project was an intentional attempt at transparency.

Reflexivity , the second of our concepts for interpretive researcher consideration, has garnered a great deal of attention in qualitative research literature. Some have called this increased attention the “reflexive turn” (see e.g., Denzin & Lincoln, 2004 :

Although you can find many meanings for the term reflexivity, it is usually associated with a critical reflection on the practice and process of research and the role of the researcher. It concerns itself with the impact of the researcher on the system and the system on the researcher. It acknowledges the mutual relationships between the researcher and who and what is studied... by acknowledging the role of the self in qualitative research, the researcher is able to sort through biases and think about how they affect various aspects of the research, especially interpretation of meanings. ( Lichtman, 2006 , pp. 206–207)

As with transparency, attending to reflexivity allows researchers to attach credibility to presented findings. Providing a reflexive account of researcher subjectivity and the interactions of this subjectivity within the research process is a way for researchers to communicate openly with their audience. Instead of trying to exhume inherent bias from the process, qualitative researchers share with readers the value of having a specific, idiosyncratic positionality. As a result, situated, contextualized interpretations are viewed as an asset, as opposed to a liability.

LaBanca (2011) , acknowledging the often solitary nature of qualitative research, calls for researchers to engage others in the reflexive process. Like many other researchers, LaBanca utilizes a researcher journal to chronicle reflexive thoughts, explorations and understandings, but he takes this a step farther. Realizing the value of others’ input, LaBanca posts his reflexive journal entries on a blog (what he calls an “online reflexivity blog”) and invites critical friends, other researchers, and interested members of the community to audit his reflexive moves, providing insights, questions, and critique that inform his research and study interpretations.

We agree this is a novel approach worth considering. We, too, understand that multiple interpreters will undoubtedly produce multiple interpretations, a richness of qualitative research. So, we suggest researchers consider bringing others in before the production of the report. This could be fruitful in multiple stages of the inquiry process, but especially so in the complex, idiosyncratic processes of reflexivity and interpretation. We are both educators and educational researchers. Historically, each of these roles has tended to be constructed as an isolated endeavor, the solitary teacher, the solo researcher/fieldworker. As noted earlier and in the “analysis” section that follows, introducing collaborative processes to what has often been a solitary activity offers much promise for generating rich interpretations that benefit from multiple perspectives.

Being consciously reflexive throughout our practice as researchers has benefitted us in many ways. In a study of teacher education curricula designed to prepare preservice teachers to support second-language learners, we realized hard truths that caused us to reflect on and adapt our own practices as teacher educators. Reflexivity can inform a researcher at all parts of the inquiry, even in early stages. For example, one of us was beginning a study of instructional practices in an elementary school. The communicated methods of the study indicated that the researcher would be largely an observer. Early fieldwork revealed that the researcher became much more involved as a participant than anticipated. Deep reflection and writing about the classroom interactions allowed the researcher to realize that the initial purpose of the research was not being accomplished, and the researcher believed he was having a negative impact on the classroom culture. Reflexivity in this instance prompted the researcher to leave the field and abandon the project as it was just beginning. Researchers should plan to openly engage in reflexive activities, including writing about their ongoing reflections and subjectivities. Including excerpts of this writing in research account supports our earlier recommendation of transparency.

Early in this chapter, for the purposes of discussion and examination, we defined analysis as “summarizing and organizing” data in a qualitative study, and interpretation as “finding” or “making” meaning. Although our focus has been on interpretation as the primary topic here, the importance of good analysis cannot be underestimated for, without it, resultant interpretations are likely incomplete and potentially uninformed. Comprehensive analysis puts researchers in a position to be deeply familiar with collected data and to organize these data into forms that lead to rich, unique interpretations, and yet to interpretations clearly connected to data exemplars. Although we find it advantageous to examine analysis and interpretation as different but related practices, in reality, the lines blur as qualitative researchers engage in these recursive processes.

We earlier noted our affinity for a variety of approaches to analysis (see e.g., Lichtman, 2006 ; Saldaña, 2011 ; or Hesse-Biber & Leavy 2011 ). Emerson, Fretz, and Shaw (1995) present a grounded approach to qualitative data analysis: in early stages, researchers engage in a close, line-by-line reading of data/collected text and accompany this reading with open coding , a process of categorizing and labeling the inquiry data. Next, researchers write initial memos to describe and organize the data under analysis. These analytic phases allow the researcher(s) to prepare, organize, summarize, and understand the data, in preparation for the more interpretive processes of focused coding and the writing up of interpretations and themes in the form of integrative memos .

Similarly, Mills (2007) provides guidance on the process of analysis for qualitative action researchers. His suggestions for organizing and summarizing data include coding (labeling data and looking for patterns), asking key questions about the study data (who, what, where, when, why, and how), developing concept maps (graphic organizers that show initial organization and relationships in the data), and stating what’s missing by articulating what data are not present (pp. 124–132).

Many theorists, like Emerson, Fretz, and Shaw (1995) and Mills (2007) noted here, provide guidance for individual researchers engaged in individual data collection, analysis, and interpretation; others, however, invite us to consider the benefits of collaboratively engaging in these processes through the use of collaborative research and analysis teams. Paulus, Woodside, and Ziegler (2008) wrote about their experiences in collaborative qualitative research: “Collaborative research often refers to collaboration among the researcher and the participants. Few studies investigate the collaborative process among researchers themselves” (p. 226).

Paulus, Woodside, and Ziegler (2008) claim that the collaborative process “challenged and transformed our assumptions about qualitative research” (p. 226). Engaging in reflexivity, analysis, and interpretation as a collaborative enabled these researchers to reframe their views about the research process, finding that the process was much more recursive, as opposed to following a linear progression. They also found that cooperatively analyzing and interpreting data yielded “collaboratively constructed meanings” as opposed to “individual discoveries.” And finally, instead of the traditional “individual products” resulting from solo research, collaborative interpretation allowed researchers to participate in an “ongoing conversation” (p. 226).

These researchers explain that engaging in collaborative analysis and interpretation of qualitative data challenged their previously held assumptions. They note, “through collaboration, procedures are likely to be transparent to the group and can, therefore, be made public. Data analysis benefits from an iterative, dialogic, and collaborative process because thinking is made explicit in a way that is difficult to replicate as a single researcher” ( Paulus, Woodside, & Ziegler, 2008 , p. 236). They share that during the collaborative process, “we constantly checked our interpretation against the text, the context, prior interpretations, and each other’s interpretations” (p. 234).

We, too, have engaged in analysis similar to these described processes, including working on research teams. We encourage other researchers to find processes that fit with the methodology and data of a particular study, use the techniques and strategies most appropriate, and then cite to the utilized authority to justify the selected path. We urge traditionally solo researchers to consider trying a collaborative approach. Generally, we suggest researchers be familiar with a wide repertoire of practices. In doing so, they’ll be in better positions to select and use strategies most appropriate for their studies and data. Succinctly preparing, organizing, categorizing, and summarizing data sets the researcher(s) up to construct meaningful interpretations in the forms of assertions, findings, themes, and theories.

Researchers want their findings to be sound, backed by evidence, justifiable, and to accurately represent the phenomena under study. In short, researchers seek validity for their work. We assert that qualitative researchers should attend to validity concepts as a part of their interpretive practices. We have previously written and theorized about validity, and, in doing so, we have highlighted and labeled what we consider to be two distinctly different approaches, transactional and transformational ( Cho & Trent, 2006 ). We define transactional validity in qualitative research as an interactive process occurring among the researcher, the researched, and the collected data, one that is aimed at achieving a relatively higher level of accuracy. Techniques, methods, and/or strategies are employed during the conduct of the inquiry. These techniques, such as member checking and triangulation, are seen as a medium with which to ensure an accurate reflection of reality (or, at least, participants’ constructions of reality). Lincoln and Guba’s (1985) widely known notion of trustworthiness in “naturalistic inquiry” is grounded in this approach. In seeking trustworthiness, researchers attend to research credibility, transferability, dependability, and confirmability. Validity approaches described by Maxwell (1992) as “descriptive” and “interpretive” also proceed in the usage of transactional processes.

For example, in the write-up of a study on the facilitation of teacher research, one of us ( Trent, 2012 , p. 44) wrote about the use of transactional processes: “‘Member checking is asking the members of the population being studied for their reaction to the findings’ ( Sagor, 2000 , p. 136). Interpretations and findings of this research, in draft form, were shared with teachers (for member checking) on multiple occasions throughout the study. Additionally, teachers reviewed and provided feedback on the final draft of this article.” This member checking led to changes in some resultant interpretations (called findings in this particular study) and to adaptations of others that shaped these findings in ways that made them both richer and more contextualized.

Alternatively, in transformational approaches, validity is not so much something that can be achieved solely by way of certain techniques. Transformationalists assert that because traditional or positivist inquiry is no longer seen as an absolute means to truth in the realm of human science, alternative notions of validity should be considered to achieve social justice, deeper understandings, broader visions, and other legitimate aims of qualitative research. In this sense, it is the ameliorative aspects of the research that achieve (or don’t achieve) its validity. Validity is determined by the resultant actions prompted by the research endeavor.

Lather (1993) , Richardson (1997) , and others (e.g., Lenzo, 1995 ; Scheurich, 1996 ) propose a transgressive approach to validity that emphasizes a higher degree of self-reflexivity. For example, Lather has proposed a “catalytic validity” described as “the degree to which the research empowers and emancipates the research subjects” ( Scheurich, 1996 , p. 4). Beverley (2000 , p. 556) has proposed “testimonio” as a qualitative research strategy. These first-person narratives find their validity in their ability to raise consciousness and thus provoke political action to remedy problems of oppressed peoples (e.g., poverty, marginality, exploitation).

We, too, have pursued research with transformational aims. In the earlier mentioned study of preservice teachers’ experiences learning to teach second-language learners ( Cho, Rios, Trent, & Mayfield, 2012 ), our aims were to empower faculty members, evolve the curriculum, and, ultimately, better serve preservice teachers so that they might better serve English-language learners in their classrooms. As program curricula and activities have changed as a result, we claim a degree of transformational validity for this research.

Important, then, for qualitative researchers throughout the inquiry, but especially when engaged in the process of interpretation, is to determine the type(s) of validity applicable to the study. What are the aims of the study? Providing an “accurate” account of studied phenomena? Empowering participants to take action for themselves and others? The determination of this purpose will, in turn, inform researchers’ analysis and interpretation of data. Understanding and attending to the appropriate validity criteria will bolster researcher claims to meaningful findings and assertions.

Regardless of purpose or chosen validity considerations, qualitative research depends on evidence . Researchers in different qualitative methodologies rely on different types of evidence to support their claims. Qualitative researchers typically utilize a variety of forms of evidence including texts (written notes, transcripts, images, etc.), audio and video recordings, cultural artifacts, documents related to the inquiry, journal entries, and field notes taken during observations of social contexts and interactions. “Evidence is essential to justification, and justification takes the form of an argument about the merit(s) of a given claim. It is generally accepted that no evidence is conclusive or unassailable (and hence, no argument is foolproof). Thus, evidence must often be judged for its credibility, and that typically means examining its source and the procedures by which it was produced [thus the need for transparency discussed earlier]” ( Schwandt, 2001 , p. 82).

Qualitative researchers distinguish evidence from facts. Evidence and facts are similar but not identical. We can often agree on facts, e.g., there is a rock, it is harder than cotton candy. Evidence involves an assertion that some facts are relevant to an argument or claim about a relationship. Since a position in an argument is likely tied to an ideological or even epistemological position, evidence is not completely bound by facts, but it is more problematic and subject to disagreement. ( Altheide & Johnson, 2011 , p. 586)

Inquirers should make every attempt to link evidence to claims (or findings, interpretations, assertions, conclusions, etc.). There are many strategies for making these connections. Induction involves accumulating multiple data points to infer a general conclusion. Confirmation entails directly linking evidence to resultant interpretations. Testability/falsifiability means illustrating that evidence does not necessarily contradict the claim/interpretation, and so increases the credibility of the claim ( Schwandt, 2001 ). In the “learning to teach second-language learners” study, for example, a study finding ( Cho, Rios, Trent, & Mayfield, 2012 , p. 77) was that “as a moral claim , candidates increasingly [in higher levels of the teacher education program] feel more responsible and committed to ELLs [English language learners].” We supported this finding with a series of data points that included the following preservice teacher response: “It is as much the responsibility of the teacher to help teach second-language learners the English language as it is our responsibility to teach traditional English speakers to read or correctly perform math functions.” Claims supported by evidence allow readers to see for themselves and to both examine researcher assertions in tandem with evidence and to form further interpretations of their own.

Some postmodernists reject the notion that qualitative interpretations are arguments based on evidence. Instead, they argue that qualitative accounts are not intended to faithfully represent that experience, but instead are designed to evoke some feelings or reactions in the reader of the account ( Schwandt, 2001 ). We argue that, even in these instances where transformational validity concerns take priority over transactional processes, evidence still matters. Did the assertions accomplish the evocative aims? What evidence/arguments were used to evoke these reactions? Does the presented claim correspond with the study’s evidence? Is the account inclusive? In other words, does it attend to all evidence or selectively compartmentalize some data while capitalizing on other evidentiary forms?

Researchers, we argue, should be both transparent and reflexive about these questions and, regardless of research methodology or purpose, should share with readers of the account their evidentiary moves and aims. Altheide and Johnson (2011) call this an “evidentiary narrative” and explain:

Ultimately, evidence is bound up with our identity in a situation.... An “evidentiary narrative” emerges from a reconsideration of how knowledge and belief systems in everyday life are tied to epistemic communities that provide perspectives, scenarios, and scripts that reflect symbolic and social moral orders. An “evidentiary narrative” symbolically joins an actor, an audience, a point of view (definition of a situation), assumptions, and a claim about a relationship between two or more phenomena. If any of these factors are not part of the context of meaning for a claim, it will not be honored, and thus, not seen as evidence. (p. 686)

In sum, readers/consumers of a research account deserve to know how evidence was treated and viewed in an inquiry. They want and should be aware of accounts that aim to evoke versus represent, and then they can apply their own criteria (including the potential transferability to their situated context). Renowned ethnographer and qualitative research theorist Harry Wolcott (1990) urges researchers to “let readers ‘see’ for themselves” by providing more detail rather than less and by sharing primary data/evidence to support interpretations. In the end, readers don’t expect perfection. Writer Eric Liu (2010) explains, “we don’t expect flawless interpretation. We expect good faith. We demand honesty.”

Last, in this journey through concepts we assert are pertinent to researchers engaged in interpretive processes, we include attention to the “ literature .” In discussing “literature,” qualitative researchers typically mean publications about the prior research conducted on topics aligned with or related to a study. Most often, this research/literature is reviewed and compiled by researchers in a section of the research report titled, “literature review.” It is here we find others’ studies, methods, and theories related to our topics of study, and it is here we hope the assertions and theories that result from our studies will someday reside.

We acknowledge the value of being familiar with research related to topics of study. This familiarity can inform multiple phases of the inquiry process. Understanding the extant knowledge base can inform research questions and topic selection, data collection and analysis plans, and the interpretive process. In what ways do the interpretations from this study correspond with other research conducted on this topic? Do findings/interpretations corroborate, expand, or contradict other researchers’ interpretations of similar phenomena? In any of these scenarios (correspondence, expansion, contradiction), new findings and interpretations from a study add to and deepen the knowledge base, or literature, on a topic of investigation.

For example, in our literature review for the study of student teaching, we quickly determined that the knowledge base and extant theories related to the student teaching experience was immense, but also quickly realized that few if any studies had examined student teaching from the perspective of the K–12 students who had the student teachers. This focus on the literature related to our topic of student teaching prompted us to embark on a study that would fill a gap in this literature: most of the knowledge base focused on the experiences and learning of the student teachers themselves. Our study then, by focusing on the K–12 students’ perspectives, added literature/theories/assertions to a previously untapped area. The “literature” in this area (at least we’d like to think) is now more robust as a result.

In another example, a research team ( Trent et al., 2003 ) focused on institutional diversity efforts, mined the literature, found an appropriate existing (a priori) set of theories/assertions, and then used this existing theoretical framework from the literature as a framework to analyze data; in this case, a variety of institutional activities related to diversity.

Conducting a literature review to explore extant theories on a topic of study can serve a variety of purposes. As evidenced in these examples, consulting the literature/extant theory can reveal gaps in the literature. A literature review might also lead researchers to existing theoretical frameworks that support analysis and interpretation of their data (as in the use of the a priori framework example). Finally, a review of current theories related to a topic of inquiry might confirm that much theory already exists, but that further study may add to, bolster, and/or elaborate on the current knowledge base.

Guidance for researchers conducting literature reviews is plentiful. Lichtman (2006) suggests researchers conduct a brief literature review, begin research, and then update and modify the literature review as the inquiry unfolds. She suggests reviewing a wide range of related materials (not just scholarly journals) and additionally suggests researchers attend to literature on methodology, not just the topic of study. She also encourages researchers to bracket and write down thoughts on the research topic as they review the literature, and, important for this chapter, she suggests researchers “integrate your literature review throughout your writing rather than using a traditional approach of placing it in a separate chapter [or section]” (p. 105).

We agree that the power of a literature review to provide context for a study can be maximized when this information isn’t compartmentalized apart from a study’s findings. Integrating (or at least revisiting) reviewed literature juxtaposed alongside findings can illustrate how new interpretations add to an evolving story. Eisenhart (1998) expands the traditional conception of the literature review and discusses the concept of an “interpretive review.” By taking this interpretive approach, Eisenhart claims that reviews, alongside related interpretations/findings on a specific topic, have the potential to allow readers to see the studied phenomena in entirely new ways, through new lenses, revealing heretofore unconsidered perspectives. Reviews that offer surprising and enriching perspectives on meanings and circumstances “shake things up, break down boundaries, and cause things (or thinking) to expand” (p. 394). Coupling reviews of this sort with current interpretations will “give us stories that startle us with what we have failed to notice” (p. 395).

In reviews of research studies, it can certainly be important to evaluate the findings in light of established theories and methods [the sorts of things typically included in literature reviews]. However, it also seems important to ask how well the studies disrupt conventional assumptions and help us to reconfigure new, more inclusive, and more promising perspectives on human views and actions. From an interpretivist perspective, it would be most important to review how well methods and findings permit readers to grasp the sense of unfamiliar perspectives and actions. ( Eisenhart, 1998 , p. 397)

And so, our journey through qualitative research interpretation and the selected concepts we’ve treated in this chapter nears an end, an end in the written text, but a hopeful beginning of multiple new conversations among ourselves and in concert with other qualitative researchers. Our aims here have been to circumscribe interpretation in qualitative research; emphasize the importance of interpretation in achieving the aims of the qualitative project; discuss the interactions of methodology, data, and the researcher/self as these concepts and theories intertwine with interpretive processes; describe some concrete ways that qualitative inquirers engage the process of interpretation; and, finally, to provide a framework of interpretive strategies that may serve as a guide for ourselves and other researchers.

In closing, we note that this “travel” framework, construed as a journey to be undertaken by researchers engaged in the interpretive process, is not designed to be rigid or prescriptive, but instead is designed to be a flexible set of concepts that will inform researchers across multiple epistemological, methodological, and theoretical paradigms. We chose the concepts of transparency, reflexivity, analysis, validity, evidence, and literature (TRAVEL) because they are applicable to the infinite journeys undertaken by qualitative researchers who have come before and to those who will come after us. As we journeyed through our interpretations of interpretation, we have discovered new things about ourselves and our work. We hope readers also garner insights that enrich their interpretive excursions. Happy travels to all— Bon Voyage !

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AI Generator

A thesis is a comprehensive research paper that presents a central argument or claim supported by evidence. Typically written by students pursuing advanced degrees, a thesis demonstrates a deep understanding of a subject. It includes a clear research question, literature review, methodology, analysis, and conclusions. The process enhances critical thinking, research skills , and subject expertise, culminating in a significant academic contribution.

Thesis paper . Many students tend to fear this word and there is a good reason as to why they do.  You may already have tried making a thesis before and at some point, you would also realize the trial and error stage of making one. 

What Is a Thesis?

A thesis a research paper writing that is made for a purpose. Thesis papers consists of a research statement , a kind of statement , a theory, a purpose. The thesis is made in order to prove your theory and make it into a fact. There are a lot of kinds of thesis, but the most common thesis kinds are analytical thesis, an argumentative thesis and an explanatory thesis.

Types of Thesis

Analytical thesis.

An analytical thesis breaks down an issue or idea into its component parts, evaluates the topic, and presents this breakdown and evaluation to the audience. It is often used in literature, history, and social sciences.

Expository Thesis

An expository thesis explains a topic to the audience. It provides a comprehensive overview of a subject, presenting facts and analysis without personal opinion. This type is common in science and technical writing.

Argumentative Thesis

An argumentative thesis makes a claim about a topic and justifies this claim with specific evidence. The goal is to persuade the reader of a particular viewpoint. This type is prevalent in fields like philosophy, political science, and law.

Narrative Thesis

A narrative thesis tells a story or recounts an event. It includes personal experiences or detailed descriptions of events to support the main argument. This type is often used in creative writing and autobiographies.

Comparative Thesis

A comparative thesis compares and contrasts two or more subjects, evaluating their similarities and differences. It is commonly used in literature, history, and social sciences to draw meaningful conclusions.

Descriptive Thesis

A descriptive thesis provides a detailed description of a topic without arguing a specific point. It paints a vivid picture of the subject, often used in fields like anthropology and sociology to explore cultural phenomena.

Empirical Thesis

An empirical thesis is based on original research and data collection. It involves experiments, surveys, or observations to answer a specific research question. This type is typical in natural and social sciences.

Examples of Thesis

Thesis examples in literature, 1: analysis of a single work.

Title: “The Use of Symbolism in ‘The Great Gatsby’ by F. Scott Fitzgerald”

Thesis Statement: In F. Scott Fitzgerald’s ‘The Great Gatsby,’ the use of symbolism, particularly through the green light at the end of Daisy’s dock, the eyes of Doctor T. J. Eckleburg, and the Valley of Ashes, serves to illustrate the overarching themes of the American Dream, moral decay, and the quest for identity.

2: Comparative Analysis

Title: “The Role of Women in ‘Pride and Prejudice’ by Jane Austen and ‘Jane Eyre’ by Charlotte Brontë”

Thesis Statement: While both Jane Austen’s ‘Pride and Prejudice’ and Charlotte Brontë’s ‘Jane Eyre’ critique the limited roles and expectations of women in 19th-century British society, Austen’s Elizabeth Bennet and Brontë’s Jane Eyre embody different forms of rebellion against societal norms, highlighting the evolving perception of women’s independence and self-worth.

3: Thematic Analysis

Title: “Exploring the Theme of Isolation in ‘Frankenstein’ by Mary Shelley”

Thesis Statement: Mary Shelley’s ‘Frankenstein’ explores the theme of isolation through the experiences of Victor Frankenstein and his creation, the monster, demonstrating how isolation leads to destructive consequences for both individuals and society.

4: Character Analysis

Title: “The Evolution of Hamlet’s Character in William Shakespeare’s ‘Hamlet'”

Thesis Statement: In William Shakespeare’s ‘Hamlet,’ the protagonist undergoes a significant transformation from a grief-stricken and indecisive prince to a determined and introspective avenger, reflecting the complexities of human nature and the impact of existential contemplation.

5: Genre Analysis

Title: “Gothic Elements in ‘Wuthering Heights’ by Emily Brontë”

Thesis Statement: Emily Brontë’s ‘Wuthering Heights’ employs key elements of Gothic literature, including a brooding atmosphere, supernatural occurrences, and the exploration of human psychology, to create a haunting and timeless tale of passion and revenge.

6: Symbolic Analysis

Title: “The Symbolism of the Green Light in ‘The Great Gatsby’ by F. Scott Fitzgerald”

Thesis Statement: The green light in F. Scott Fitzgerald’s ‘The Great Gatsby’ symbolizes Gatsby’s unattainable dreams and the elusive nature of the American Dream, reflecting the broader themes of hope, disillusionment, and the pursuit of an idealized future.

7: Historical Context

Title: “Historical Influences on George Orwell’s ‘1984’”

Thesis Statement: George Orwell’s ‘1984’ draws heavily on the political climate of the early 20th century, particularly the rise of totalitarian regimes and the impact of World War II, to present a dystopian vision of a future where government surveillance and propaganda control every aspect of life.

8: Feminist Critique

Title: “Feminist Perspectives in ‘The Handmaid’s Tale’ by Margaret Atwood”

Thesis Statement: Margaret Atwood’s ‘The Handmaid’s Tale’ critiques the patriarchal structures of contemporary society by depicting a dystopian world where women’s rights are stripped away, illustrating the extreme consequences of gender oppression and the resilience of female solidarity.

9: Psychoanalytic Criticism

Title: “Freudian Elements in ‘The Turn of the Screw’ by Henry James”

Thesis Statement: Henry James’s ‘The Turn of the Screw’ can be interpreted through a Freudian lens, where the governess’s experiences and the ambiguous nature of the ghosts reflect deep-seated psychological conflicts and repressed desires, highlighting the novella’s exploration of the human psyche.

10: Postcolonial Analysis

Title: “Postcolonial Themes in ‘Things Fall Apart’ by Chinua Achebe”

Thesis Statement: Chinua Achebe’s ‘Things Fall Apart’ addresses postcolonial themes by portraying the clash between traditional Igbo society and British colonial forces, illustrating the devastating effects of colonialism on indigenous cultures and the struggle for cultural identity and autonomy.

Thesis Examples for Essays

1: persuasive essay.

Topic: “The Importance of Renewable Energy”

Thesis Statement: Governments around the world should invest heavily in renewable energy sources like solar and wind power to reduce dependency on fossil fuels, combat climate change, and create sustainable job opportunities.

2: Analytical Essay

Topic: “The Symbolism in ‘The Great Gatsby’ by F. Scott Fitzgerald”

Thesis Statement: In ‘The Great Gatsby,’ F. Scott Fitzgerald uses the symbols of the green light, the eyes of Doctor T. J. Eckleburg, and the Valley of Ashes to illustrate the moral and social decay of America during the Roaring Twenties.

3: Expository Essay

Topic: “The Impact of Social Media on Teenagers”

Thesis Statement: Social media has significantly impacted teenagers’ mental health, social skills, and academic performance, both positively and negatively, necessitating a balanced approach to its usage.

4: Compare and Contrast Essay

Topic: “Public vs. Private School Education”

Thesis Statement: While public schools offer a more diverse social environment and extracurricular opportunities, private schools provide smaller class sizes and specialized curriculums, making the choice dependent on individual student needs and family priorities.

5: Cause and Effect Essay

Topic: “The Causes and Effects of the Rise in Obesity Rates”

Thesis Statement: The rise in obesity rates can be attributed to poor dietary habits, sedentary lifestyles, and genetic factors, leading to serious health issues such as diabetes, heart disease, and decreased life expectancy.

6: Narrative Essay

Topic: “A Life-Changing Experience”

Thesis Statement: My trip to volunteer at a rural school in Kenya was a life-changing experience that taught me the value of education, the importance of cultural exchange, and the power of empathy and compassion.

7: Argumentative Essay

Topic: “The Necessity of Free College Education”

Thesis Statement: Free college education is essential for ensuring equal opportunities for all, reducing student debt burdens, and fostering a more educated and productive workforce.

8: Descriptive Essay

Topic: “The Beauty of a Sunset”

Thesis Statement: A sunset, with its vibrant hues and serene ambiance, evokes a sense of peace and reflection, illustrating nature’s ability to inspire awe and tranquility in our daily lives.

9: Definition Essay

Topic: “What is Happiness?”

Thesis Statement: Happiness is a complex and multifaceted emotion characterized by feelings of contentment, fulfillment, and joy, influenced by both internal factors like mindset and external factors such as relationships and achievements.

10: Process Essay

Topic: “How to Bake the Perfect Chocolate Cake”

Thesis Statement: Baking the perfect chocolate cake involves selecting high-quality ingredients, precisely following the recipe, and understanding the nuances of baking techniques, from mixing to temperature control.

Thesis Examples for Argumentative Essay

1: gun control.

Topic: “Stricter Gun Control Laws”

Thesis Statement: Stricter gun control laws are necessary to reduce gun violence in the United States, as evidenced by lower rates of gun-related deaths in countries with stringent regulations.

2: Climate Change

Topic: “Addressing Climate Change”

Thesis Statement: To effectively combat climate change, governments worldwide must implement aggressive policies to reduce carbon emissions, invest in renewable energy, and promote sustainable practices.

3: Animal Testing

Topic: “Ban on Animal Testing”

Thesis Statement: Animal testing for cosmetics should be banned globally due to its ethical implications, the availability of alternative testing methods, and the questionable reliability of animal-based results for human safety.

4: Education Reform

Topic: “Standardized Testing in Schools”

Thesis Statement: Standardized testing should be eliminated in schools as it narrows the curriculum, causes undue stress to students, and fails to accurately measure a student’s potential and abilities.

5: Universal Basic Income

Topic: “Implementing Universal Basic Income”

Thesis Statement: Implementing a universal basic income would help alleviate poverty, reduce income inequality, and provide financial stability in an increasingly automated and unpredictable job market.

6: Health Care

Topic: “Universal Health Care”

Thesis Statement: Universal health care should be adopted in the United States to ensure that all citizens have access to essential medical services, reduce overall healthcare costs, and improve public health outcomes.

7: Immigration Policy

Topic: “Reforming Immigration Policies”

Thesis Statement: Comprehensive immigration reform is essential to address undocumented immigration, protect human rights, and contribute to economic growth by recognizing the contributions of immigrants to society.

8: Death Penalty

Topic: “Abolishing the Death Penalty”

Thesis Statement: The death penalty should be abolished as it is an inhumane practice, prone to judicial errors, and has not been proven to deter crime more effectively than life imprisonment.

9: Social Media Regulation

Topic: “Regulating Social Media Platforms”

Thesis Statement: Social media platforms should be regulated to prevent the spread of misinformation, protect user privacy, and reduce the negative impact on mental health, particularly among adolescents.

10: College Tuition

Topic: “Free College Tuition”

Thesis Statement: Providing free college tuition at public universities would increase access to higher education, reduce student debt, and help create a more educated and skilled workforce to meet future economic demands.

Thesis Examples for Research Papers

1: environmental science.

Topic: “Impact of Plastic Pollution on Marine Life”

Thesis Statement: Plastic pollution in the oceans is causing significant harm to marine life, leading to ingestion and entanglement of plastic debris, disruption of ecosystems, and bioaccumulation of toxic substances in the food chain.

2: Psychology

Topic: “Effects of Social Media on Adolescent Mental Health”

Thesis Statement: Excessive use of social media negatively impacts adolescent mental health by increasing the risk of anxiety, depression, and poor sleep quality, while also contributing to body image issues and cyberbullying.

3: Education

Topic: “Benefits of Bilingual Education Programs”

Thesis Statement: Bilingual education programs enhance cognitive abilities, improve academic performance, and promote cultural awareness, making them a valuable approach in the increasingly globalized and multicultural society.

4: Public Health

Topic: “Addressing the Obesity Epidemic”

Thesis Statement: Addressing the obesity epidemic requires a multifaceted approach that includes implementing public health campaigns, promoting healthy eating habits, increasing physical activity, and regulating food advertising targeted at children.

5: Economics

Topic: “Universal Basic Income and Economic Stability”

Thesis Statement: Implementing a universal basic income can provide economic stability by reducing poverty, ensuring a safety net during economic downturns, and stimulating consumer spending, thereby supporting overall economic growth.

6: Political Science

Topic: “Impact of Voter ID Laws on Voter Turnout”

Thesis Statement: Voter ID laws disproportionately reduce voter turnout among minority and low-income populations, undermining the democratic process and exacerbating existing inequalities in political participation.

7: Sociology

Topic: “Gender Stereotypes in Media Representation”

Thesis Statement: Media representation perpetuates gender stereotypes by consistently portraying men and women in traditional roles, which reinforces societal norms and limits the opportunities for gender equality.

8: Technology

Topic: “Artificial Intelligence in Healthcare”

Thesis Statement: The integration of artificial intelligence in healthcare can improve patient outcomes, enhance diagnostic accuracy, and streamline administrative processes, but it also raises ethical concerns regarding data privacy and the potential for job displacement.

Topic: “Causes and Consequences of the American Civil War”

Thesis Statement: The American Civil War was primarily caused by deep-seated economic, social, and political differences between the North and South, particularly over the issue of slavery, and it resulted in significant social and political changes, including the abolition of slavery and the reconstruction of the South.

10: Environmental Policy

Topic: “Renewable Energy Policies and Their Effectiveness”

Thesis Statement: Renewable energy policies, such as subsidies for solar and wind power and carbon pricing, are effective in reducing greenhouse gas emissions and promoting sustainable energy sources, but their success depends on comprehensive implementation and international cooperation.

Thesis Examples for Informative Essay

Topic: “The Water Cycle”

Thesis Statement: The water cycle, which includes processes such as evaporation, condensation, precipitation, and infiltration, is essential for distributing water across the Earth’s surface and maintaining ecological balance.

2: Health and Wellness

Topic: “The Benefits of Regular Exercise”

Thesis Statement: Regular exercise is crucial for maintaining physical health, improving mental well-being, and reducing the risk of chronic diseases such as obesity, diabetes, and cardiovascular conditions.

3: Technology

Topic: “The Development of Artificial Intelligence”

Thesis Statement: The development of artificial intelligence has progressed from simple machine learning algorithms to complex neural networks capable of performing tasks such as natural language processing, image recognition, and autonomous driving.

Topic: “The Causes and Effects of the American Civil Rights Movement”

Thesis Statement: The American Civil Rights Movement was driven by factors such as racial segregation, economic disparity, and political disenfranchisement, leading to significant legislative and social changes that improved the rights and freedoms of African Americans.

5: Education

Topic: “The Montessori Method of Education”

Thesis Statement: The Montessori method of education, developed by Dr. Maria Montessori, emphasizes self-directed learning, hands-on activities, and collaborative play, fostering independence and critical thinking skills in young children.

6: Sociology

Topic: “The Impact of Urbanization on Community Life”

Thesis Statement: Urbanization significantly impacts community life by altering social structures, increasing economic opportunities, and presenting challenges such as overcrowding, pollution, and loss of green spaces.

7: Environmental Policy

Topic: “The Role of Renewable Energy in Combating Climate Change”

Thesis Statement: Renewable energy sources, such as solar, wind, and hydroelectric power, play a critical role in combating climate change by reducing greenhouse gas emissions and providing sustainable alternatives to fossil fuels.

8: Business

Topic: “The Rise of Gig Economy”

Thesis Statement: The rise of the gig economy has transformed the labor market by offering flexible work opportunities, fostering entrepreneurship, and posing challenges such as job insecurity and lack of benefits for workers.

9: Psychology

Topic: “The Importance of Sleep for Cognitive Function”

Thesis Statement: Adequate sleep is essential for cognitive function, memory consolidation, and emotional regulation, with chronic sleep deprivation leading to impaired mental performance and increased risk of mental health disorders.

10: Cultural Studies

Topic: “The Influence of Japanese Anime on Global Pop Culture”

Thesis Statement: Japanese anime has significantly influenced global pop culture by shaping trends in fashion, art, and storytelling, and fostering a dedicated international fanbase that celebrates its unique aesthetic and thematic elements.

Thesis Examples for Synthesis Essay

1: climate change.

Topic: “Combating Climate Change through Policy and Innovation”

Thesis Statement: Combating climate change requires a multifaceted approach that includes stringent environmental policies, investment in renewable energy technologies, and community-based initiatives to reduce carbon footprints, integrating efforts from government, industry, and society.

2: Education

Topic: “Balancing Technology and Traditional Teaching Methods in Education”

Thesis Statement: A balanced approach to education that combines the benefits of technology, such as interactive learning tools and online resources, with traditional teaching methods, like face-to-face instruction and hands-on activities, can enhance student engagement and academic achievement.

Topic: “Addressing the Opioid Crisis through Comprehensive Strategies”

Thesis Statement: Addressing the opioid crisis requires comprehensive strategies that include better access to addiction treatment programs, stricter regulations on prescription opioids, and increased public awareness campaigns to educate communities about the risks of opioid misuse.

4: Technology

Topic: “The Impact of Social Media on Political Mobilization”

Thesis Statement: Social media has revolutionized political mobilization by providing platforms for grassroots campaigns, enabling real-time communication, and fostering civic engagement, but it also poses challenges such as the spread of misinformation and echo chambers.

5: Business

Topic: “Corporate Social Responsibility and Its Impact on Brand Loyalty”

Thesis Statement: Corporate social responsibility (CSR) initiatives, when genuinely implemented, can significantly enhance brand loyalty by aligning company values with consumer expectations, fostering trust, and contributing positively to societal well-being.

Topic: “The Role of Gender Stereotypes in Media Representation”

Thesis Statement: Media representation perpetuates gender stereotypes by consistently depicting men and women in traditional roles, which influences societal perceptions and expectations, but progressive portrayals are gradually challenging these norms and promoting gender equality.

Topic: “Sustainable Urban Development and Green Infrastructure”

Thesis Statement: Sustainable urban development that incorporates green infrastructure, such as green roofs, urban gardens, and eco-friendly public transportation, is essential for mitigating environmental impacts, improving public health, and enhancing the quality of urban life.

8: Psychology

Topic: “The Effects of Mindfulness Practices on Mental Health”

Thesis Statement: Mindfulness practices, including meditation, yoga, and mindful breathing, have been shown to significantly improve mental health by reducing stress, enhancing emotional regulation, and promoting overall well-being, supported by a growing body of scientific research.

9: Economics

Topic: “Universal Basic Income as a Solution to Economic Inequality”

Thesis Statement: Universal Basic Income (UBI) presents a viable solution to economic inequality by providing financial security, reducing poverty, and supporting economic stability, though it requires careful consideration of funding mechanisms and potential societal impacts.

10: Public Health

Topic: “The Importance of Vaccination Programs in Preventing Epidemics”

Thesis Statement: Vaccination programs are crucial for preventing epidemics, protecting public health, and achieving herd immunity, as evidenced by the successful eradication of diseases like smallpox and the control of outbreaks such as measles and influenza.

Thesis Examples for Persuasive Essays

Thesis Statement: Stricter gun control laws are essential to reduce gun violence in the United States, as they will help prevent firearms from falling into the wrong hands, decrease the number of mass shootings, and enhance public safety.

Topic: “Urgent Action on Climate Change”

Thesis Statement: Immediate and robust action is needed to combat climate change, including reducing carbon emissions, transitioning to renewable energy sources, and implementing sustainable practices to mitigate the devastating effects on our planet.

3: Animal Rights

Topic: “Ban on Animal Testing for Cosmetics”

Thesis Statement: Animal testing for cosmetics should be banned worldwide due to its ethical implications, the availability of alternative testing methods, and the questionable reliability of animal-based results for human safety.

Topic: “Abolishing Standardized Testing in Schools”

Thesis Statement: Standardized testing should be abolished in schools as it narrows the curriculum, places undue stress on students, and fails to accurately measure a student’s potential and abilities, thereby hindering educational growth.

5: Universal Health Care

Topic: “Adopting Universal Health Care in the United States”

Thesis Statement: The United States should adopt a universal health care system to ensure that all citizens have access to essential medical services, reduce overall healthcare costs, and improve public health outcomes.

6: Immigration Policy

Thesis Statement: Comprehensive immigration reform is essential to address undocumented immigration, protect human rights, and contribute to economic growth by recognizing the contributions of immigrants to society and ensuring a fair, efficient legal process.

7: Death Penalty

Thesis Statement: The death penalty should be abolished as it is an inhumane practice, prone to judicial errors, and has not been proven to deter crime more effectively than life imprisonment, while also being more costly to taxpayers.

8: Social Media Regulation

Thesis Statement: Social media platforms should be regulated to prevent the spread of misinformation, protect user privacy, and reduce the negative impact on mental health, particularly among adolescents, to create a safer online environment.

9: College Tuition

Topic: “Providing Free College Tuition”

10: Renewable Energy

Topic: “Investing in Renewable Energy Sources”

Thesis Statement: Governments should invest heavily in renewable energy sources like solar and wind power to reduce dependency on fossil fuels, combat climate change, and create sustainable job opportunities, ensuring a cleaner and healthier future.

Thesis Examples for Analysis Essays

1: literary analysis.

Topic: “Symbolism in ‘The Great Gatsby’ by F. Scott Fitzgerald”

Thesis Statement: In ‘The Great Gatsby,’ F. Scott Fitzgerald uses symbols such as the green light, the Valley of Ashes, and the eyes of Doctor T. J. Eckleburg to critique the American Dream and explore themes of ambition, disillusionment, and moral decay.

2: Film Analysis

Topic: “Themes of Redemption in ‘The Shawshank Redemption'”

Thesis Statement: ‘The Shawshank Redemption’ explores themes of hope, friendship, and the human spirit’s resilience, using the character arcs of Andy Dufresne and Red to highlight the transformative power of hope and redemption within the confines of a corrupt prison system.

3: Rhetorical Analysis

Topic: “Martin Luther King Jr.’s ‘I Have a Dream’ Speech”

Thesis Statement: In his ‘I Have a Dream’ speech, Martin Luther King Jr. employs rhetorical strategies such as repetition, parallelism, and powerful imagery to effectively convey his vision of racial equality and galvanize the civil rights movement.

4: Historical Analysis

Topic: “Causes of the Fall of the Roman Empire”

Thesis Statement: The fall of the Roman Empire was the result of a complex interplay of factors, including political corruption, economic instability, military defeats, and the gradual erosion of civic virtue, which collectively undermined the empire’s ability to sustain itself.

5: Character Analysis

Topic: “The Complexity of Hamlet in William Shakespeare’s ‘Hamlet'”

Thesis Statement: In William Shakespeare’s ‘Hamlet,’ the titular character’s complexity is revealed through his introspective nature, moral ambiguity, and fluctuating resolve, which collectively illustrate the play’s exploration of existential themes and the human condition.

6: Social Analysis

Topic: “The Impact of Social Media on Modern Communication”

Thesis Statement: Social media has significantly altered modern communication by enabling instantaneous sharing of information and fostering global connectivity, while also contributing to issues such as reduced face-to-face interactions, cyberbullying, and the spread of misinformation.

7: Cultural Analysis

Topic: “Cultural Significance of Traditional Festivals”

Thesis Statement: Traditional festivals play a crucial role in preserving cultural heritage, fostering community identity, and promoting social cohesion, as they provide a platform for the transmission of customs, values, and shared history across generations.

8: Economic Analysis

Topic: “The Effects of Globalization on Local Economies”

Thesis Statement: Globalization has profoundly impacted local economies by enhancing market access, fostering economic growth, and encouraging cultural exchange, but it has also led to job displacement, wage suppression, and the erosion of local industries in some regions.

9: Psychological Analysis

Topic: “Freudian Themes in ‘The Turn of the Screw’ by Henry James”

Thesis Statement: Henry James’s ‘The Turn of the Screw’ can be analyzed through a Freudian lens, where the governess’s experiences and the ambiguous nature of the ghosts reflect deep-seated psychological conflicts, repressed desires, and the complexities of the human psyche.

10: Political Analysis

Topic: “The Effectiveness of the New Deal Programs”

Thesis Statement: The New Deal programs implemented by President Franklin D. Roosevelt were effective in providing immediate relief during the Great Depression, spurring economic recovery, and implementing long-term reforms that reshaped the American social and economic landscape.

Thesis Examples for Compare and Contrast Essay

1: literature.

Topic: “Comparing ‘1984’ by George Orwell and ‘Brave New World’ by Aldous Huxley”

Thesis Statement: While George Orwell’s ‘1984’ presents a dystopian future of totalitarian control through fear and oppression, Aldous Huxley’s ‘Brave New World’ explores a similar theme through a society controlled by pleasure and conditioning, highlighting different methods of societal control and their implications.

Topic: “Public School vs. Private School Education”

Thesis Statement: Public schools offer a diverse social environment and a broad curriculum, whereas private schools provide smaller class sizes and specialized programs, making the choice between the two dependent on individual educational goals and personal preferences.

Topic: “E-books vs. Printed Books”

Thesis Statement: While e-books offer convenience, portability, and interactive features, printed books provide a tactile experience, lack of screen strain, and a sense of nostalgia, demonstrating how each format caters to different reader preferences and needs.

Topic: “Traditional Medicine vs. Modern Medicine”

Thesis Statement: Traditional medicine emphasizes holistic and natural treatments based on centuries-old practices, while modern medicine focuses on scientific research and technological advancements, highlighting the strengths and limitations of each approach in addressing health issues.

5: Social Media

Topic: “Facebook vs. Instagram”

Thesis Statement: Facebook facilitates in-depth social interaction and a wide range of features for communication and information sharing, whereas Instagram focuses on visual content and a streamlined user experience, catering to different user preferences and social engagement styles.

Topic: “Traveling by Plane vs. Traveling by Train”

Thesis Statement: Traveling by plane offers speed and efficiency for long distances, while traveling by train provides scenic views and a more relaxed experience, highlighting the trade-offs between convenience and leisure in different modes of transportation.

7: Economics

Topic: “Capitalism vs. Socialism”

Thesis Statement: Capitalism promotes economic growth and individual entrepreneurship through market competition, whereas socialism emphasizes social welfare and equitable distribution of resources, reflecting contrasting ideologies on economic management and social equity.

8: Literature

Topic: “Shakespeare’s ‘Hamlet’ vs. Sophocles’ ‘Oedipus Rex'”

Thesis Statement: While Shakespeare’s ‘Hamlet’ delves into themes of indecision, revenge, and existential angst, Sophocles’ ‘Oedipus Rex’ explores fate, self-discovery, and the inevitability of destiny, illustrating different approaches to tragedy in Western literature.

9: Lifestyle

Topic: “Urban Living vs. Rural Living”

Thesis Statement: Urban living offers convenience, diverse cultural experiences, and numerous job opportunities, while rural living provides a peaceful environment, close-knit communities, and a connection to nature, demonstrating the contrasting lifestyles and priorities of each setting.

10: History

Topic: “The American Revolution vs. The French Revolution”

Thesis Statement: The American Revolution focused on independence from colonial rule and the establishment of a democratic republic, whereas the French Revolution aimed to overthrow the monarchy and address social inequalities, highlighting different motivations, outcomes, and impacts on world history.

More Thesis Samples & Examples:

1. thesis statements.

2. University Thesis Research

3. Working Thesis

4. Master Thesis

5. Basics About Thesis Statements

6. Thesis Sample

7. Thesis Format

8. Thesis PDF

9. Graduate Students Thesis

10. Thesis Example

Tips for Writing Your Thesis

Start Early

• Begin your thesis process early to allow ample time for research , writing , and revisions.

Choose a Relevant Topic

• Select a topic that interests you and has sufficient research material available. Ensure it is specific enough to be manageable but broad enough to find sources.

Develop a Strong Thesis Statement

• Craft a clear, concise thesis statement that outlines the main argument or focus of your paper. This will guide your research and writing.

Create an Outline

• Plan your thesis structure with a detailed outline. Include sections for the introduction, literature review, methodology, results, discussion, and conclusion.

Conduct Thorough Research

• Use a variety of sources, such as books, journal articles, and credible websites. Take detailed notes and organize your research to support your thesis statement.

Write in Stages

• Break down the writing process into manageable stages. Start with the introduction, move to the literature review, then the methodology, and so on.

Maintain Consistent Formatting

• Follow the required formatting style (e.g., APA, MLA, Chicago) consistently throughout your thesis. Pay attention to citation rules and references.

Seek Feedback

• Regularly consult with your advisor and seek feedback from peers. Incorporate their suggestions to improve your work.

Edit and Revise

• Set aside time for multiple rounds of editing and revising. Check for clarity, coherence, grammar, and spelling errors.

Stay Organized

• Keep all your research materials, notes, and drafts well-organized. Use tools like folders, labels, and reference management software.

Stay Motivated

• Set small, achievable goals and reward yourself for meeting them. Stay positive and remember that writing a thesis is a marathon, not a sprint.

Proofread Thoroughly

• Conduct a final proofread to catch any remaining errors. Consider using grammar checking tools or hiring a professional proofreader.

What to include in a Thesis

Writing a thesis involves several critical sections that contribute to the overall structure and argumentation of the research. Here’s a guide on what to include in a thesis:

1. Title Page

• Title: Clear, concise, and descriptive.
• Author’s Name
• Institutional Affiliation
• Date of Submission
• Advisor’s Name

2. Abstract

• Summary: Brief overview of the research.
• Key Points: Main objectives, methods, results, and conclusions.
• Word Limit: Typically 150-300 words.

3. Table of Contents

• Sections and Subsections: With corresponding page numbers.

4. List of Figures and Tables

• Figures/Tables: Numbered and titled with page numbers.

5. Introduction

• Background: Context of the study.
• Problem Statement: The issue being addressed.
• Objectives: What the research aims to achieve.
• Research Questions/Hypotheses: Specific questions or hypotheses the study will test.
• Significance: Importance of the study.

6. Literature Review

• Overview of Existing Research: Summarize previous studies.
• Theoretical Framework: The theories guiding the research.
• Gaps in Literature: Identify what has not been addressed.

7. Methodology

• Research Design: Type of study (e.g., qualitative, quantitative).
• Participants: Who was involved in the study.
• Data Collection: How data was gathered (e.g., surveys, experiments).
• Data Analysis: Methods used to analyze the data.
• Ethical Considerations: How ethical issues were handled.
• Findings: Present data and key results.
• Visuals: Use tables, graphs, and charts for clarity.
• Statistical Analysis: Include relevant statistical tests.

9. Discussion

• Interpretation of Results: What the findings mean.
• Comparison with Existing Literature: How results align or contrast with previous research.
• Implications: Practical or theoretical implications.
• Limitations: Discuss limitations of the study.
• Future Research: Suggestions for future studies.

10. Conclusion

• Summary of Findings: Recap main findings.
• Restate Importance: Reiterate the study’s significance.
• Final Thoughts: Concluding remarks.

11. References

• Citations: Complete list of all sources cited in the thesis.
• Formatting: Follow a specific citation style (e.g., APA, MLA).

12. Appendices

• Supplementary Material: Additional data, questionnaires, or detailed descriptions.

Thesis vs. Dissertation

How do i know if my thesis is strong, clear and specific thesis statement.

• Precision : Your thesis statement should be clear, specific, and concise. It should articulate the main argument or focus of your thesis.
• Focus : Ensure it directly addresses the research question without being too broad or vague.

Well-Defined Research Question

• Relevance : The research question should be significant to your field of study.
• Feasibility : Make sure it is practical and manageable within the scope of your resources and time frame.

Comprehensive Literature Review

• Depth : Your literature review should cover relevant research and show an understanding of key theories and findings.
• Gaps Identification : Highlight gaps in the existing literature that your thesis aims to fill.

Solid Methodology

• Appropriateness : The chosen methodology should be suitable for answering your research question.
• Detail : Clearly describe your research design, data collection methods, and data analysis procedures.
• Justification : Explain why these methods are the best fit for your study.

Strong Evidence and Analysis

• Support : Provide ample evidence to support your thesis statement and arguments.
• Critical Analysis : Critically analyze the data, showing how it supports or contradicts your hypothesis.
• Consistency : Ensure that all evidence is consistently interpreted and integrated into your argument.

Coherent Structure

• Organization : The thesis should be well-organized with a logical flow of ideas.
• Clarity : Each section should clearly contribute to the overall argument.
• Transitions : Use smooth transitions between sections to maintain coherence.

Original Contribution

• Innovation : Your thesis should offer new insights or findings in your field.
• Significance : Highlight the importance and impact of your research.

Proper Formatting and Style

• Formatting : Follow the required formatting guidelines (APA, MLA, Chicago, etc.) consistently.
• Grammar and Spelling : Proofread your work to ensure it is free from grammatical and spelling errors.
• Citations : Properly cite all sources and provide a comprehensive reference list.

Feedback and Revision

• Advisor Feedback : Regularly seek feedback from your advisor and incorporate their suggestions.
• Peer Review : Get input from peers to identify areas for improvement.
• Multiple Revisions : Be prepared to revise your thesis multiple times to enhance its quality.

Self-Assessment

• Alignment : Ensure that all parts of the thesis align with the thesis statement.
• Completeness : Check that all required sections are included and thoroughly addressed.
• Confidence : Be confident in your arguments and the quality of your research.

How to Make a Thesis

Where do you often begin when you want to make a thesis? Many may say to begin by drafting, to begin by making an outline or to start at the introduction. A lot of these answers may even confuse you and may make you think that making a thesis is difficult or confusing. Stop right there, there are answers to every question, and to show you the  thesis statement writing tips .

Step 1: Make an Outline for the Thesis

Start out by making a  thesis outline . The outline will help you as it acts as the backbone of your entire thesis. Making outlines also help you by giving you a good view of what comes first, what should be added here and what should not be added. Outlining your thesis is often the best way to begin.

Step 2: Start with a Thesis Proposal for Your Thesis Paper

Once you have a blank outline for your thesis, which you will be filling out in order to know what goes first, the next thing to do is to pick a topic or pick a thesis proposal . This is an important part of making your thesis paper. Start with thinking about what kind of thesis proposal you want to talk about.

Step 3: Write Down the Introduction of Your Thesis

Thesis introduction has an important role to play. Its role in your thesis is to give a short summary of what can be expected in your thesis. The introduction of your thesis is all about the topic or the proposal of your thesis. When you write your thesis, make sure that the introduction should be clear and concise. After the introduction, the heart of your thesis will follow.

Step 4: Finalize Your Thesis Paper

Finalizing your thesis paper may take a lot of time and effort. But not to worry. It is always necessary and understandable that finalizing your thesis paper is important. As long as you are making sure that everything that is necessary, the introduction, the proposal, the thesis problem, solution and conclusion are present.

How do I choose a thesis topic?

Choose a topic that interests you, has ample research material, is specific enough to be manageable, and aligns with your academic goals.

How long should my thesis be?

Thesis length varies by discipline and degree level; Master’s theses are usually 50-100 pages, while PhD dissertations can be 100-300+ pages.

What is a thesis statement?

A thesis statement is a concise summary of the main point or claim of your thesis, guiding your research and writing.

How do I structure my thesis?

A typical thesis structure includes a title page, abstract, table of contents, introduction, literature review, methodology, results, discussion, conclusion, and references.

How important is the literature review?

The literature review is crucial as it contextualizes your research, highlights gaps, and demonstrates your understanding of existing scholarship.

What is the difference between a thesis and a dissertation?

A thesis is usually for a Master’s degree and demonstrates mastery of a topic, while a dissertation for a PhD contributes new knowledge to the field.

How do I manage my time effectively while writing my thesis?

Create a detailed timeline, break the process into manageable tasks, set deadlines, and regularly consult with your advisor.

How do I ensure my thesis is original?

Conduct thorough research, properly cite sources, use plagiarism detection tools, and contribute unique insights or findings to your field.

What should I do if I encounter writer’s block?

Take breaks, set small writing goals, change your environment, seek feedback, and stay connected with your advisor for guidance and support.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

2024 Theses Doctoral

Statistically Efficient Methods for Computation-Aware Uncertainty Quantification and Rare-Event Optimization

He, Shengyi

The thesis covers two fundamental topics that are important across the disciplines of operations research, statistics and even more broadly, namely stochastic optimization and uncertainty quantification, with the common theme to address both statistical accuracy and computational constraints. Here, statistical accuracy encompasses the precision of estimated solutions in stochastic optimization, as well as the tightness or reliability of confidence intervals. Computational concerns arise from rare events or expensive models, necessitating efficient sampling methods or computation procedures. In the first half of this thesis, we study stochastic optimization that involves rare events, which arises in various contexts including risk-averse decision-making and training of machine learning models. Because of the presence of rare events, crude Monte Carlo methods can be prohibitively inefficient, as it takes a sample size reciprocal to the rare-event probability to obtain valid statistical information about the rare-event. To address this issue, we investigate the use of importance sampling (IS) to reduce the required sample size. IS is commonly used to handle rare events, and the idea is to sample from an alternative distribution that hits the rare event more frequently and adjusts the estimator with a likelihood ratio to retain unbiasedness. While IS has been long studied, most of its literature focuses on estimation problems and methodologies to obtain good IS in these contexts. Contrary to these studies, the first half of this thesis provides a systematic study on the efficient use of IS in stochastic optimization. In Chapter 2, we propose an adaptive procedure that converts an efficient IS for gradient estimation to an efficient IS procedure for stochastic optimization. Then, in Chapter 3, we provide an efficient IS for gradient estimation, which serves as the input for the procedure in Chapter 2. In the second half of this thesis, we study uncertainty quantification in the sense of constructing a confidence interval (CI) for target model quantities or prediction. We are interested in the setting of expensive black-box models, which means that we are confined to using a low number of model runs, and we also lack the ability to obtain auxiliary model information such as gradients. In this case, a classical method is batching, which divides data into a few batches and then constructs a CI based on the batched estimates. Another method is the recently proposed cheap bootstrap that is constructed on a few resamples in a similar manner as batching. These methods could save computation since they do not need an accurate variability estimator which requires sufficient model evaluations to obtain. Instead, they cancel out the variability when constructing pivotal statistics, and thus obtain asymptotically valid t-distribution-based CIs with only few batches or resamples. The second half of this thesis studies several theoretical aspects of these computation-aware CI construction methods. In Chapter 4, we study the statistical optimality on CI tightness among various computation-aware CIs. Then, in Chapter 5, we study the higher-order coverage errors of batching methods. Finally, Chapter 6 is a related investigation on the higher-order coverage and correction of distributionally robust optimization (DRO) as another CI construction tool, which assumes an amount of analytical information on the model but bears similarity to Chapter 5 in terms of analysis techniques.

• Operations research
• Stochastic processes--Mathematical models
• Mathematical optimization
• Bootstrap (Statistics)
• Sampling (Statistics)

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