what does gap hypothesis state mean

Sorin Adam Matei

Professor of Communication, Associate Dean of Research, Brian Lamb School of Communication, College of Liberal Arts, Purdue University

Knowledge Gap Hypothesis and Digital Divides – A review of the literature and impact on social media research

This is a learning module for the class  Contemporary Social / Mass Media Theory  taught at Purdue University by Sorin Adam Matei

Knowledge gap hypothesis proposes that more information does not always mean a better informed public, or at least that not all members of the public will be be better informed to the same degree. To the contrary, as some members of the public might become better informed, some might in fact lag farther behind in terms of knowledge about important issues of the day. In other words, the slopes of the curves of information gain are more abrupt for some and flatter for other. The angle of the slope seems to be determined by socio-economic status. The final outcome of this process is that as we add more educational and information resources, the ones that have better chances to absorb them will get much more out of them than those that have lesser socio-economic resources. Or, in more vernacular terms, the richer (materially) become even richer (intellectually), while the poor will, although getting something out of this intellectual evolution, do not get nearly as much of it. Thus, the difference is not defined in terms of “some get, while some lose,” but in terms of “some get, while some get even more”… The knowledge gap as been recast more recently as a “digital divide” gap. With the Internet and social media many things can be done faster and better. So much faster and better that those that do not have access to them are literally left out of the game. Two readings highlight the issues involved in the digital divide debate.

Knowledge Gap Assumptions

Mass Media Flow and Differential Growth in Knowledge  P. J. Tichenor, G. A. Donohue and C. N. Olien  The Public Opinion Quarterly   Vol. 34, No. 2 (Summer, 1970), pp. 159-170 Published by: Oxford University Press on behalf of the American Association for Public Opinion Research

Stable URL: http://www.jstor.org/stable/2747414 (please search library database for full citation if the links do not work)

Data from four types of research-news diffusion studies, time trends, a newspaper strike, and a field experiment-are consistent with the general hypothesis that increasing the flow of news on a topic leads to greater acquisition of knowledge about that topic among the more highly educated segments of society. Whether the resulting knowledge gap closes may depend partly on whether the stimulus intensity of mass media publicity is maintained at a high level, or is reduced or eliminated at a point when only the more active persons have gained that knowledge. There are several contributory reasons why the predicted knowledge gap should appear and widen with increasing levels of media input. One factor is communication skills . Persons with more formal education would be expected to have the higher reading and comprehension abilities necessary to acquire public affairs or science knowledge. A second factor is amount of stored information , or existing knowledge resulting from prior exposure to the topic through mass media or from formal education itself. Persons who are already better informed are more likely to be aware of a topic when it appears in the mass media and are better prepared to understand it. A third factor is relevant social contact . Education generally indicates a broader sphere of everyday activity, a greater number of reference groups, and more interpersonal contacts, which increase the likelihood of discussing public affairs topics with others. Studies of diffusion among such groups as doctors and farmers tend to show steeper, more accelerated acceptance rates for more active, socially integrated individuals. A fourth factor includes selective exposure, acceptance, and retention of informatio n. As Sears and Freedman have pointed out, voluntary exposure is often more closely related to education than to any other set of variables. They contend that what appears to be selective exposure according to attitudes might often more appropriately be called “de facto” selectivity resulting from educational differences.’ Selective acceptance and retention, however, might be a joint result of attitude and educational differences. A persistent theme in mass media research is the apparent tendency to interpret and recall information in ways congruent with existing beliefs and values.’ A final factor is the nature of the mass media system that delivers information. Thus far, most science and public affairs news (with the possible recent exceptions of crisis events and space spectaculars) is carried in print media which, traditionally, have been more heavily used by higher-status persons. Print media are geared to the interests and tastes of this higher-status segment and may taper off on reporting many topics when they begin to lose the novel characteristic of “news.” Unlike a great deal of contemporary advertising, science and public affairs news ordinarily lacks the constant repetition which facilitates learning and familiarity among lower-status persons. The knowledge gap hypothesis might be expressed, operationally, in at least two different ways: 1. Over time, acquisition of knowledge of a heavily publicized topic will proceed at a faster rate among better educated persons than among those with less education; and 2. At a given point in time, there should be a higher correlation between acquisition of knowledge and education for topics highly publicized in the media than for topics less highly publicized. One would expect the knowledge gap to be especially prominent when one or more of the contributory factors is operative. Thus, to the extent that communication skills, prior knowledge, social contact, or attitudinal selectivity is engaged, the gap should widen as heavy mass media flow continues.

Mass Media and the Knowledge Gap A Hypothesis Reconsidered G.A. Donohue, P.J. Tichenor, C.N. Olien, University of Minnesota, doi: 10.1177/009365027500200101 Communication Research January 1975 vol. 2 no. 1 3-23 – Try this link while on campus or  GET FROM LIBRARY STACKS (Blame the library…)

A principal consequence of mass media coverage about national public affairs issues, particularly from print media, appears to be an increasing “knowledge gap” between various social strata. Previous data presented by the authors were concerned with issues largely external to the local community. More recent work raises the question whether social conflict about a community issue will tend to open the gap further, or close it. Survey data from fifteen Minnesota communities experiencing conflicts of varying magnitude indicate that as level of conflict about local issues increases, the knowledge gap actually tends to decline. Level of interpersonal communication about the issue appears to be a major intervening variable. Thus, it appears that the knowledge gap hypothesis needs to be modified according to the type of issue involved and the conflict dimensions of the issue within the community.

Citizens, Knowledge, and the Information Environment, Jennifer Jerit1, Jason Barabas2, Toby Bolsen3 Article first published online: 29 MAR 2006, DOI: 10.1111/j.1540-5907.2006.00183.x, American Journal of Political Science Volume 50, Issue 2, pages 266–282, April 2006

In a democracy, knowledge is power. Research explaining the determinants of knowledge focuses on unchanging demographic and socioeconomic characteristics. This study combines data on the public’s knowledge of nearly 50 political issues with media coverage of those topics. In a two-part analysis, we demonstrate how education, the strongest and most consistent predictor of political knowledge, has a more nuanced connection to learning than is commonly recognized. Sometimes education is positively related to knowledge. In other instances its effect is negligible. A substantial part of the variation in the education-knowledge relationship is due to the amount of information available in the mass media. This study is among the first to distinguish the short-term, aggregate-level influences on political knowledge from the largely static individual-level predictors and to empirically demonstrate the importance of the information environment.

REVISITING THE KNOWLEDGE GAP HYPOTHESIS: A META-ANALYSIS OF THIRTY-FIVE YEARS OF RESEARCH Hwang, Yoori; Jeong, Se-Hoon. Journalism and Mass Communication Quarterly86. 3(Autumn 2009): 513-532.

This present study, a meta-analysis providing a systematic summary of previous research on the knowledge gap hypothesis, has three specific goals: (a) to obtain an average size for the knowledge gap, (b) to examine the impact of media publicity on the knowledge gap, and (c) to identify conditions (e.g., topic, knowledge measurement, country, and publication status) under which the gap increases or decreases. Consistent with previous reviews,43 the results show that knowledge disparities exist across social strata. The average effect size of the relationship between education and knowledge acquisition was moderate (r = .28). However, according to this meta-analytic review, the gap in knowledge did not change either over time or with varying levels of media publicity.

Does the Digital Divide Matter More? Comparing the Effects of New Media and Old Media Use on the Education-Based Knowledge Gap DOI:10.1080/15205431003642707 Lu Weia & Douglas Blanks Hindmanb, pages 216-235

As the Internet has become increasingly widespread in the world, some researchers suggested a conceptual shift of the digital divide from material access to actual use. Although this shift has been incorporated into the more broad social inclusion agenda, the social consequences of the digital divide have not yet received adequate attention. Recognizing that political knowledge is a critical social resource associated with power and inclusion, this study empirically examines the relationship between the digital divide and the knowledge gap. Based on the 2008–2009 American National Election Studies panel data, this research found that, supporting the shift of the academic agenda, socioeconomic status is more closely associated with the informational use of the Internet than with access to the Internet. In addition, socioeconomic status is more strongly related to the informational use of the Internet than with that of the traditional media, particularly newspapers and television. More importantly, the differential use of the Internet is associated with a greater knowledge gap than that of the traditional media. These findings suggest that the digital divide, which can be better defined as inequalities in the meaningful use of information and communication technologies, matters more than its traditional counterpart.

Digital divide research, achievements and shortcomings Original Research Article, Poetics, Volume 34, Issues 4–5, August–October 2006, Pages 221-235 Jan A.G.M. van Dijk

From the end of the 1990s onwards the digitaldivide, commonly defined as the gap between those who have and do not have access to computers and the Internet, has been a central issue on the scholarly and political agenda of new media development. This article makes an inventory of 5 years of digitaldivide research (2000–2005). The article focuses on three questions. (1) To what type of inequality does the digitaldivide concept refer? (2) What is new about the inequality of access to and use of ICTs as compared to other scarce material and immaterial resources? (3) Do new types of inequality exist or rise in the information society? The results of digitaldivide research are classified under four successive types of access: motivational, physical, skills and usage. A shift of attention from physical access to skills and usage is observed. In terms of physical access the divide seems to be closing in the most developed countries; concerning digital skills and the use of applications the divide persists or widens. Among the shortcomings of digitaldivide research are its lack of theory, conceptual definition, interdisciplinary approach, qualitative research and longitudinal research.

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Associate Dean of Research and Professor of Communication at Purdue University, Director of the FORCES initiative leads research teams that study the relationship between technological and social systems using big data, simulation, and mapping approaches. He published papers and articles in Journal of Communication, Communication Research, Information Society, National Interest, and Foreign Policy . He is the author or co-editor of several books. The most recent is Structural differentation in social media . He also co-edited Ethical Reasoning in Big Data , Transparency in social media and Roles, Trust, and Reputation in Social Media Knowledge Markets: Theory and Methods (Computational Social Sciences) , all three the product of the NSF funded KredibleNet project . Dr. Matei's teaching portfolio includes technology and strategy, online interaction, and digital media analytics classes. A former BBC World Service journalist, his contributions have been published in Esquire and several leading Romanian newspapers. In Romania, he is known for his books Boierii Mintii (The Mind Boyars) , Idolii forului (Idols of the forum) , and Idei de schimb (Spare ideas) .

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I find the readings are very interesting because the knowledge gap hypothesis (KGH) takes a rather pessimistic view on the difference between the knowledge acquisitions of higher and lower social strata. The KGH explains how a knowledge gap exists between people with high and low social strata. This is not to say that lower social strata does not obtain knowledge but that they obtain knowledge in a lower degree then those with a higher social strata (Tichenor, Donohue & Olien, 1970). How wide the knowledge gap is between social strata can be said to depend on the medium used, as mediums like television and radio actually affords nearly the same acquisition of knowledge to all social strata (Jerit, Barabas & Bolsen, 2006; Wei & Hindman, 2011). This is also the argument put forth by Jerit et. al (2006) when they describe the KGH literature as being too pessimistic. I argue that the pessimistic nature in the body of literature of KGH is not to be discarded when it comes to the concern for democracy and emancipation of the different strata. If we are to believe that we live in an converge culture where content is converging thru the Internet and hardware is becoming more and more situational (Jenkins, n.d.) the findings of Wei and Hindman (2011) does indeed suggest that we are to be pessimistic about the future and the knowledge gap between different strata. Wei and Hindman’s findings suggest that there is a significant difference and gap between the knowledge obtained by higher and lower social strata when the Internet is the medium of knowledge gathering. This can be a problem because more and more content is converging to the Internet and as such “the Internet may function to reinforce inequalities of power and knowledge, producing deeper gaps between the information rich and poor, and between the activists and the disengaged” (Wei & Hindman, 2011: p. 230).

This raises some very serious concerns in regards to democracy and the emancipation of strata not only because more and more content is converging on the Internet but also because the habits of Internet users are used to determining the content that are delivered (Praiser, 2011). As online-algorisms or ‘ the filter bobble’ becomes more and more advanced in determining the preference of Internet users (Paiser, 2011) lower social strata’s use of the Internet to seek non-informational content can become a self-reinforcing element in the knowledge gap between the different strata. Therefore the Internet can be seen as affording a wider knowledge gap between strata, which might be widened further by the ongoing development of targeted content and online-algorisms. This could potentially be a major challenge to democracy as it requires an informed citizenry and as such begs for further investigation by scholars.

Works Cited Jenkins, H. (n.d.). Receiver#12. Retrieved Oct. 4, 2012, from Welcome to convergence culture: http://karactar.tistory.com/attachment/gk050000000003.pdf

Jerit, J., Barabas, J., & Bolsen, T. (2006). Citizens, Knowledge, and the Information Environment. American Journal of Political Science , 50 (2), 266-282.

Pariser, E. (2011). The Filter Bobble. New York: The Penguin Press.

Tiehenor, P. J., Donohue, G., & Olien, C. (1970). Mass Media Flow and Differential Growth in Knowledge. The Public Opinion Quarterly , 34 (2), 169-170.

Wei, L., & Hindman, D. B. (2011). Does the Digital Divide Matter More? Comparing the Effects of New Media and Old Media Use on the Education-Based Knowledge Gap. Mass Communication and Society , 14 (2), 216-235.

As presented and discussed in the readings for this week, the concept of the knowledge gap is concerning. The knowledge gap hypothesis (KGH) states that as information disseminates in a society, individuals possessing higher socioeconomic status acquire new knowledge and information at a faster rate than those with lower socioeconomic status. While everyone benefits from new knowledge, individuals with higher socioeconomic status benefit more overall. This different results in a widening gap between those with high and low socioeconomic status, where high socioeconomic status individuals experience more relative benefits with every new piece of information (Tichenor, Donohue, & Olien, 1970; Wei & Hindman, 2011). In relation to technology, this idea has been examined as the digital divide. Originally, the digital divide addressed access to technology, drawing a line between those individuals who had access to computers or other new media technology, and those that did not (Wei & Hindman, 2011). Recently, research on the digital divide and knowledge gap has switched from focusing on access to technology to how the technology is used (Tichenor, Donohue, & Olien, 1970). Overall, these studies have found patterns similar to research addressing traditional media; individuals of higher socioeconomic status experience more benefits from their Internet use. With new media technology, this benefit seems to stem from the use of technology for information-gathering purposes. Some research has not only found that the traditional knowledge gap exists when considering new media, but that the gap widens at a faster rate (Wei & Hindman, 2011). This discovery is partially attributed to the sheer amount of diverse information available on the Internet; higher socioeconomic status individuals are more likely to seek out new information, and new media technology provide a portal to a vast amount of knowledge. The Internet is not restricted in the same ways that newspapers or TV media are – stories do not fight for page placement or time-slot length, so more information is available for discovery overall (Wei & Hindman, 2011).

I am curious about the role of social media, particularly Facebook, in this context. Facebook primarily functions as a location to establish a personal network among friends, family, and acquaintances by sharing information, pictures, status updates, and other information ( http://facebook.com/facebook ). Users can create their own content or share the content of others, including images, videos, and links to third party websites. Facebook use is not restricted by socioeconomic status or education level (as long as users have access to a computer). Visiting Facebook is not necessarily the result of active political or education information-seeking behavior; generally, users visit to create content, catch up with friends and family, or out of habit. The interesting consideration here is the actual content displayed on a given user’s newsfeed. On a given day, my newsfeed presents the usual pictures and status updates, along with comics, updates on my favorite actors, TV shows, or movies, links to entertaining or enlightening articles posted on third-party websites, or political commentary in a variety of forms. What I select to take a closer look at depends on my mood at the time of log-in, how much time I have available, my relationship with the poster, and my particular interest in a given idea. A given user can only be exposed to the information posted by members of their established Facebook network; arguably, given that Facebook connections are usually based on real-life relationships, users surround themselves with-like minded individuals. Network members may not hold the same viewpoints or opinions, but are connected in some way – affiliation through school or work, family, etc; the resulting information presented on a newsfeed will most likely be a mix of familiar or relatable and novel information. In this particular case, the information is neither as heterogeneous as the content of the Internet as a whole, nor as homogenous as a newscast displayed in multiple neighborhoods. While I find this idea interesting, it raises more questions in my mind than answers:

– How does this network-generated source of information contribute to the digital divide? I would assume that the effects become increasingly individualized, depending on the specific members of an individual’s network, the content posted by those members, and the content engaged with by the user herself.

– What happens to the general effect of increased knowledge for all when Facebook is used as a knowledge source? We can assume that a news broadcast does indeed reach a certain number of people, and we can make educated guesses about the levels of diversity in the audience. On Facebook, different stories and information will disseminate through different networks at varying rates. Perhaps there are demographic factors that relate to the networks through which the information spreads, but I would think that personal interest of users would be a more influential factor here.

Facebook. (2013). http://facebook.com/facebook

Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth in knowledge. The Public Opinion Quarterly, 34(2), 159-170.

Wei, L. & Hindman, D. B. (2011). Does the digital divide matter more? Comparing the effects of new media and old media use on the education-based knowledge gap. Mass Communication and Society, 14(2), 216-235.

The “digital divide” is a term used by scholars to encompass the various inequalities in Internet access and use. The driving force behind this area of research is an argument that differences in access and use mean that some benefit more than others from new technologies like the Internet, with such benefits including gains in knowledge and social capital (Wei & Hindman, 2011). Those who benefit are often seen to be those already privileged within the global and local power systems based on existing demographic variables (i.e. issues of income, education, status, sex, nationality, ethnicity, etc.) (van Dijk, 2006). While such a “divide” is problematic and worth examining, the use of the term and the related concepts of “access” and “use” seems to be vague, making it challenging to study.

First, the “digital divide” has become a very broad concept. It has evolved from referring only to a simplistic gap between those who have or do not have physical access to new information and communication technologies to also speak to various gaps in motivation, skills, and usage (van Dijk, 2006). The term itself then is “deeply ambiguous,” as it seemingly implies a singular divide between clearly distinguishable and fixed groups (van Dijk, 2006, p. 222). Research has documented the existence of multiple divides, and some scholars like van Dijk (2006) have argued for seeing them as dynamic in light of the ways technology is constantly changing. The “digital divide” is perhaps better thought of as a number of different divides (van Dijk, 2006) or instead as “digital inequality” (Wei & Hindman, 2011, p. 231).

Second, how to account for issues of “access” and “use” can pose problems for researchers. Wei and Hindman’s (2011) study provides an example of this. This study measured “access” to the Internet based on the yes/no question “Do you have Internet access at home?” (Wei & Hindman, 2011, p. 223). However, the home may not be the place, let alone the only place, people access the Internet. Some may only access the Internet at work, at friends’ or family’s houses, at cyber cafes and other public access points, or some combination of these. Should those individuals not count as having access? This raises questions of what qualifies as having access in terms of location, frequency, and quality of the connection and use. Also, as van Dijk (2006) points out, having access does not necessarily mean people are using it. In the context of access at home, it might be commandeered by particular individuals within the household, seen as inappropriate for others to use (for example, women in certain cultural contexts), or avoided by some based on their perceived lack of technological competence.

These issues present a variety of challenges for scholars interested in the so-called “digital divide.” They speak to the importance of avoiding simplistic measures or definitions of these terms, of not making assumptions about what constitutes meaningful access or use, and of asking people about their varied levels of access and use in ways that speak to their lived experiences with these new technologies.

References Van Dijk, J. a. G. M. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4-5), 221–235. doi:10.1016/j.poetic.2006.05.004

Wei, L., & Hindman, D. B. (2011). Does the digital divide matter more? Comparing the effects of new media and old media use on the education-based knowledge gap. Mass Communication and Society, 14(2), 216–235. doi:10.1080/15205431003642707

With several of the mass media theories that we have discussed in past weeks, it has seemed that the Internet and social media serve to flatten hierarchies and eliminate the middleman. For example, these new technologies may change or eliminate the two-step flow model and allow information to flow straight from the media to the general public, they may weaken the agenda-setting function of traditional mass media, and they may lessen the inherent power distance between the media and the public discussed in media systems dependency theory. However, when it comes to the knowledge gap hypothesis and the digital divide, the opposite seems to be true. Rather than equalizing and eliminating hierarchy, the Internet seems to only reinforce and strengthen disparities among different segments of the population.

The original knowledge gap hypothesis (Tichenor, Donohue, & Olien, 1970) states that as media coverage on an issue increases, the gap in knowledge increases between those with high and low socioeconomic status. Suggested reasons for this gap include the greater likelihood of those with high SES to attend to mass media, understand it in relationship to previously stored knowledge, and discuss it with similarly educated social contacts.

This hypothesis has been supported and refined by a number of subsequent studies. For example, Donohue, Tichenor, and Olien (1975) found that the knowledge gap is wider for national issues and smaller for issues of local importance, especially those that generate conflict within an entire community. In addition, Jerit, Barabas, and Bolsen (2006) found that the knowledge gap widens with increased publicity in newspapers (a medium catered toward more educated segments of the population), but does not significantly increase with increased publicity on television. This focus on different types of media is further complicated by the introduction of the Internet, which leads to what has been called the digital divide. While one might expect the Internet to decrease the knowledge gap by allowing for information to be easily and widely accessed, this has not been the case. In fact, Wei and Hindman (2011) found that the gap is actually wider for consumers of Internet media than for consumers of traditional media. Furthermore, this divide is not necessarily due to access to technology, but rather the different ways that different segments of the population use the Internet.

This is a rather interesting finding that casts doubt on many of the optimistic claims that have been made about the Internet, and suggests that the issue is far more complicated than was once thought. Clearly, the solution to the digital divide is not simply to increase access to technology. Instead, it seems important to educate potential Internet users to use the technology in effective ways, and to learn more about how people of lower socioeconomic status currently use the Internet. This might be an interesting new arena for uses and gratifications research. If we can understand more clearly what resources people are actually using on the Internet and why they are using them, it may be possible to improve these education processes, suggest more beneficial uses, and decrease the digital divide. However, there is no denying that this is a difficult issue with no clear solution.

Donohue, G. A., Tichenor, P. J., & Olien, C. N. (1975). Mass media and the knowledge gap: A hypothesis reconsidered. Communication Research, 2(1), 3-23.

Jerit, J., Barabas, J., & Bolsen, T. (2006). Citizens, knowledge, and the information environment. American Journal of Political Science, 50(2), 266-282.

The knowledge gap theory suggests that one’s socioeconomic status plays a significant role in determining one’s ability to acquire information and knowledge from the mass media. According to this theory, those who are better off in terms of their socio-economic standing in society are at a better position to acquire information and knowledge when information is disseminated by the mass media, than those of lesser socioeconomic standing thus, creating a knowledge gap between the different social strata in society.

Furthermore, it proposes that contrary to popular belief, as more information becomes readily available in society, through the proliferation of information by the mass media, the knowledge gap between higher and lesser socioeconomic groups does not decrease but in fact, increases significantly. Even more, those of higher socioeconomic standing absorb the higher level of distributed information by the mass media at a faster rate than those of a lesser status hence, further increasing the knowledge gap between the two different groups by ensuring that the less educated always benefit relatively less than the higher educated in society.

Therefore, the wider distribution of information by the mass media does not level the playing field for everyone in society but rather, serves to perpetuate the established socioeconomic differences between the different strata within a society. That is especially true, given that those with greater access to information, the higher strata, also possess a greater ability to absorb, understand and put that information to use for their own benefit such as, where to invest their money, or where to move for better job opportunities for example, than the lower strata in society, who apparently will always remain comparatively less informed and thus, always at a comparative disadvantage.

With the emergence of new information technologies like the Internet, smart phones, computers and digital television, a new form of gap emerged, a digital gap or a digital divide. According to Dijk (2006), the digital divide concept was initially used to refer to the gap between those who have access to new digital information technologies and those who do not. Again, under this digital gap theory, the socioeconomic standing acts as a contributing factor to the inequality of access between different groups within a society, or even between different countries, based largely on this factor’s ability to establish who can afford these new technologies and who cannot.

However, Dijk (2006) as well as Wei and Hindman (2011), suggest that the digital divide no longer denotes one’s ability to merely access the new forms of information technology, especially in terms of acquiring physical access to them, but it is now one that is associated with the gap that exists between those who possess the skills and the know-how to utilize this new information technology versus those who do not. Moreover, the socioeconomic status is still a major factor in that it shapes one’s ability possess the necessary skills to adequately use this new technology.

Additionally, Wei and Hindman (2011) suggest that the social economic status affects the type of content acquired on the Internet by different groups, with the higher status group typically gravitating towards information content that might benefit their lives or socioeconomic situation, and or adds to their existing knowledge base. Accordingly, the type of information each socioeconomic group is exposed to on the Internet differs, and is largely under the control of each group. Whereas, under traditional media both socioeconomic groups in general do not have control over the information disseminated by the media to them. As a result, the probability of equal exposure to the same information is higher for both groups under traditional media, than under new information technology yet, this probability of equal exposure to information by traditional media still results in a knowledge gap between the two different groups. As a consequence, Wei and Hindman (2011) propose that the Internet has the ability to increase the knowledge gap between the two different groups within society at a larger scale than traditional media does, specifically because each group is in control of what information to access through the Internet and how to utilize it.

So do these theories have any bearing on social media? Given that social media use is largely a personal choice, and given that different social media platforms allow users, with varying degrees, to largely determine the type of information they have access to through them, as well as, the type of networks they can establish within them, one could suggest that social media will only extend the differences in patterns of selective use between the different socio-economic groups, which according to Wei and Hindman (2011) above, has been exhibited by Internet use in general. As a result, one could presume that those of a higher education and status will also seek more informational content on social media than those of a lower status and education. Consequently, one could suggest that social media will only serve to compound the increase in the knowledge gap that arises between the different types of users based on their socioeconomic status. Also one could presume that much like the digital divide that arises form the different uses of new information technology or the physical access to new information technology, social media can create a digital divide between users and non-users of social media as well as, between different types of users of any one social media.

Moreover, according to Donahue and Olien (1970), there are a number of factors that help predict the emergence of a knowledge gap between different socioeconomic groups, and predict the increase of that gap as more information is distributed by the media these include, social contacts, previous information people are exposed to, their communication skills, their ability to self select and retain information and the type media that delivers the information to them. If applied to social media in terms of previously acquired information for example, one could suggest that, depending on the type of information being circulated, the proliferation of information within social media platforms might benefit those who are better educated and those who possess previous knowledge regarding a topic much more than those who are less educated and less familiar with the same topic. For example, if the topic being circulated within the social media platform is time sensitive and was related to a specific new policy, political event or even an economic development, those who are better educated and more knowledgeable are at a greater advantage than those who are less educated in terms of understanding, retaining and using the relevant information being circulated on social media to better their socioeconomic position or their lives.

Of course, depending on the type of social media in question, the type of information received through it will differ and the purpose behind using it will differ by the different socioeconomic groups. Therefore, the contribution of social media to the knowledge gap might increase or decrease depending on the purpose behind using it and the type of social media in question, and the ability of this social media to perpetuate socioeconomic differences will also depend on the type of use and information circulated within it.

To conclude, the knowledge gap theory proposes that there exists a knowledge gap between the socioeconomic groups within society, with those of a higher status and education benefitting more from the greater availability of information in the media than those of a lower status and lower education. This theory is highly pessimistic and even though it suggests, that under some exceptional circumstances the knowledge gap might diminish or even close such as when the media covers an issue that is of importance to a more homogenous community and a source of major conflict within it, it still proposes that generally, the knowledge gap will only keep expanding between the socioeconomic groups in society as more information becomes available through mass media. It also implies that with the emergence of new digital information technologies, that a digital gap between users has emerged and that the accompanying knowledge gap that arises from it will only keep expanding presumably at a faster rate than before with the acceleration of development of the digital information technology.

Very complete answer…

Knowledge gap theory claims there is a “gap” or break in how different sets of people benefit from the information available through various media. The theory generally postulates that people of a higher socioeconomic status (SES) accrue significantly more knowledge/benefits from the information they consume via media—and increase that knowledge at a faster rate—than those from a lower SES.

Tichenor, Donohue and Olien (1970) originally hypothesized that “over time, acquisition of knowledge of a heavily publicized topic will proceed at a faster rate among better educated persons than among those with less education.” In a later study, the same researchers found new contributing factors should be considered to understand the increase or decrease of a knowledge gap (Donohue, Tichenor, & Olien, 1975). The knowledge gap hypothesis should consider more than how the SES of media consumers contributes to a knowledge gap; the type of issue in question and the amount of conflict surrounding the issue can affect the knowledge gap, as well. For example, these researchers found that when an issue is of direct import to a community and there is conflict surrounding the issue, the knowledge gap is likely to decrease.

With the advent of new media, knowledge gap theory has evolved to accommodate the increasing importance of digital media. Now, research has begun to focus on the knowledge gap as a “digital divide.” Wei and Hindman (2011) contributed important findings to adapt knowledge gap theory to new media usage, examining the “digital divide” as observed in access to and use of the Internet. They tested the appropriateness of the conceptual shift from studying new media versus old media and use of media versus access to media. Wei and Hindman found that differing uses of new media create a greater knowledge gap than differing uses of traditional media, confirming the importance of knowledge gap research for new media. Their findings also supported the idea that a digital divide is more evident in the use of digital media, rather than the access to digital media. They found that SES has a stronger positive correlation with informational use of the Internet than with access to the Internet. These findings led Wei and Hindman to define the digital divide as “inequalities in the meaningful use of information and communication technologies” (p. 230).

The definition of digital divide given by Wei and Hindman (2011) provides an interesting twist on knowledge gap research, focusing on the use of the technology rather than the nature and relevance of the message or differing access to the information. This could be further explored in research examining the use of social media. Does an individual’s SES correlate with how he or she uses social media? Are people of a higher SES more likely to use social sites like Facebook and Twitter for informational purposes? Do differing uses of social media contribute to a knowledge gap increase, and do the newsfeed algorithms on social media sites enforce this gap?

Donohue, G. A., Tichenor, P. J., & Olien, C. N. (1975). Mass Media and the knowledge gap: A hypothesis reconsidered. Communication Research, 2(1), 3-23.

Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth in knowledge. Public opinion quarterly, 34(2), 159-170.

Wei, L., & Hindman, D. B. (2011). Does the digital divide matter more? Comparing the effects of new media and old media use on the education-based knowledge gap. Mass Communication and Society, 14(2), 216-235.

The Informational Underclass

I really enjoyed this week’s readings. Without being overtly political, they got to the heart of an issue that I felt existed since the beginning of the course, one that I didn’t quite know how to articulate. Primarily, this feeling had to do with power, politics, and other intangibles surrounding discussions of media, intangibles that can be said to exist before we even get to questions of “media” in the first place. The two main theories – the “digital divide” and “knowledge gap” – are useful for not only thinking through the way that media is disseminated unevenly but also for understanding the previously existing factors and conditions that can contribute to this uneven distribution in media and information. In this sense, I find that this unit speaks to some of the previous units in this course, particularly the one on social capital.

The knowledge gap hypothesis is interesting in that it posits that the mere presence of information or an increase in information does not automatically imply an understanding of what those information convey or entail. The theory posits that “because certain subsystems within any total social system have patterns of behavior and values conducive to change, gaps tend to appear between subgroups already experiencing change and those that are stagnant or slower in initiating change” (Tichenor et al, p. 159). In Tichenor, Donohue, and Olien’s classic 1970 study, the knowledge gap hypothesis was meant to “suggest itself as a fundamental explanation for the apparent failure of mass publicity to inform the public at large” (Tichenor et al, p. 160). While this acknowledged and explained the problem but did not yet produce an answer that was fully formed (in my opinion), their later study from 1975 posited inequality in education as the most salient factor contributing to knowledge gap and the “differential acquisition of information” (Donohue et al, p. 4). Along with education, media characteristics and interpersonal contact also played key roles, however education seemed to be the most important variable. The ties here to social capital theory are clear to me, and this is something I’d like to work on more in the future.

One line that really struck me was the first sentence from Jerit et al (2006): “Is there a permanent information underclass?” (266). Their conclusion was in line with the notion that “higher levels of political knowledge have been associated with an impressive range of outcomes,” including tolerance, participation, and assimilation (p. 278). Dijk (2006) supports the notion that more attention needs to be paid on “skills” and “usage” rather than on mere “access” (p. 229), and it is interesting to read this against Jerit et al (2006) in terms of what an “informed” citizen can do once they have acquired knowledge from the informational environment.

In terms of testing the digital divide and knowledge gap theories in an empirical setting, Wei and Hindman (2011) find that “socioeconomic status is more closely associated with the informational use of the Internet than with access to the Internet” (p. 216-217). These findings suggest that policies such as the “One Laptop per Child” project by Nicholas Negroponte are in a way misguided and not really answering the question of the digital divide. If the digital divide is in some ways predicated on a knowledge gap, simply providing access to computers will not help if there is not an attendant increase in education and instruction. In short, “mass media seem to have a function similar to that of other social institutions: that of reinforcing or increasing existing inequalities” (Tichenor et al, p. 170). In order to combat this, more attention to education and poverty might be a first priority.

Van Dijk, J. a. G. M. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4-5), 221–235. doi:10.1016/j.poetic.2006.05.004

Tichenor, Donohue, and Olien (1970) hypothesized that there is a knowledge gap between certain social subgroups, and “that growth of knowledge is relatively greater among the higher status segments” (p. 160). That is to say that they did not believe that any social subgroup was “completely uninformed” (p. 160), but that on general topics discussed and diffused by mass media were not being consumed by lower socioeconomic (or less educated) subgroups at the same rate as subgroups with higher socioeconomic (or more educated) levels.

While this particular article was published in the 1970s, some of their findings are still pertinent to information diffusion today. People belonging to lower socioeconomic groups by and large have less access to information produced mass communication materials such as newspapers, TV, Internet, and social media – or they choose to spend their time on other activities instead of looking for and consuming news and information (Van Dijk, 2006). I also have witnessed this phenomenon in my current research project with food pantry clients. While the project’s main focus is on their perception of meat, I included a section of questions in the interview aimed at discovering their media usages and information-seeking behaviors. One question is simply, “What media do you use?” I haven’t heard the same answer twice in the 35 interviews I’ve conducted thus far; but on the whole, only 8 subjects have answered that they use the Internet, and half of those have said they do not have Internet at home – they go to the library to access it.

The research I’ve done in this project seems to agree with Wei and Hindman’s (2011) description of the digital divide (modern-day knowledge gap) in its first-level, but contradicts their second-level gap that classifies the gap as one between access and use. My collected data is preliminary and I have not had time to fully analyze it yet; and, the sample subjects who I’m working with may be a part of a lower SES/educational subgroup than the general subgroups they refer to. However, I think the digital divide is still a question of access and use for lower socioeconomic, less educated, or even more rural subgroups in America, as well as countries all over the world.

Nevertheless, I would agree that in our developed country, no subgroup is completely in the dark about societal, environmental or political issues. But, the ways in which different subgroups gather information about these issues does matter and can contribute to a knowledge gap/digital divide. That’s why Van Dijk’s (2006) article particularly resonated with me this week, especially her Figure 1 on p. 224 that divides access into several parts, including motivational, material, skills and usage access. I concur with her that more research is needed, especially that of a qualitative nature, and that is focused on motivations for accessing and using digital and social media to help explore and explain the digital divide.

References Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth in knowledge. Public opinion quarterly, 34(2), 159-170.

Van Dijk, J. A. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4), 221-235.

Amanda, is this the comment you were talking about?

Yes…I think it was a problem with the browser on my laptop that wasn’t showing it as posted, just awaiting moderation. I’m glad you saw it & graded it – thanks!

The miracles and the wonders of the Internet do need to be called to stop and the Knowledge Gap hypothesis and the Digital Divide can perfectly answer the question concerning the Internet being a good thing or not. While enjoying the benefits and convenience enabled by the Internet, we need to pause for a second and reflect more upon how our own life is made different and how our life is made different from each other due to the use of the Internet.

In Tichenor, Donohue and Olien (1970), researchers have discussed the knowledge gap hypothesis based on the evidences from four different types of researches, the results of which all lends support to this hypothesis that the increasing flow of information on mass media leads to a widening knowledge gap among the social segments characterized by different educational levels. In the study, researchers conclude with the finding that mass media has the function “of reinforcing or increasing existing inequalities” (p. 170). The following study of Donohue, Tichenor and Olien (1975) takes a step further and expands the theory by involving the types of issue and the conflict dimensions of the issue within the community in the discussion loop. Both the two studies focus on the knowledge gap resulting from the information flow in traditional mass media.

To test the knowledge gap hypothesis in the new media environment, the inequalities and disparities between different social segments are even more obvious. The life cycle of information flowing on the Internet is so quick that it can either pass on or fade away very quickly, which in turn creates the information gap very quickly. One other prominent feature of the Internet can also add propelling force to the widening knowledge gap, which is the more diversified and customized information platform. To appeal to the specific needs of the users, the developing trend of the technology has attached great importance to the customized feature which is designed to meet the “unique” needs of the users. The more customized the technology is, the larger the knowledge gap is. With customization, people who have adequate information will keep being better informed while people who lack the information will keep being kept away from the information cycle. Coming from this concern, it is interesting to consider some other factors, apart from educational levels, which can also be closely associated with the Knowledge Gap.

In discussing the Digital Divide, Wei and Hindman (2011) focus more on the so called second-level of Digital Divide which is the actual usage of the Internet, rather than the material access to the Internet. While this second level is undoubtedly becoming a more visible phenomenon, the problem rooted in the first level—the physical access to the digital technologies still cannot be neglected. Part of the whole world is still facing the problems of lacking basic Information and Communication Technologies infrastructures. With this perspective focusing on the global world, the digital divide is incredibly speeding up the disparities between the people from different countries. As Donohue, Tichenor and Olien (1975) have pointed out, “the deprivation of knowledge may lead to relative deprivation of power” (p. 4). What actions can we take to empower the people and nations struggling at the bottom of the digital capabilities and bridging the knowledge gap between them and us?

References Donohue, G.A., Tichenor, P.J., & Olien, C. N. (1975). Mass Media and the Knowledge Gap: A Hypothesis Reconsidered. Communication Research, 2 (3), 3-23

Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass Media Flow and Differential Growth in Knowledge. The Public Opinion Quarterly, 34 (2), 159-179

Wei, L., & Hindman, D. B. (2011). Does the Digital Divide Matter More? Comparing the Effects of New Media and Old Media Use on the Education-Based Knowledge Gap. Mass Communication and Society, 14(2), 216-235

Tichenor, Donohue & Olien (1970) claim that “The knowledge gap hypothesis seems to suggest it self as a fundamental explanation for the apparent failure of mass publicity to inform the public at large” (Tichenor et al, 1970, 161). However, I would argue that the only thing the knowledge gap hypothesis suggests, is how certain classic perspectives, theories and frameworks has been rendered rather irrelevant by rapid changes in the communicative environment.

Central to their argument is five factors that drive the widening of knowledge gaps in correlation with increasing levels of media input. The general level of media input has increased dramatically since 1970, so there should be no doubt, that the knowledge gaps of today are bigger than ever. Except, they are not. In my opinion, this is because most of the five factors are not relevant as arguments in the Internet era.

The first factor is “communication skills”, which implies that people with more formal education have higher reading and comprehension abilities. Although this may be true, I would argue that the Internet lowers the communication skills required by the individual to obtain knowledge. CGP Grey ( https://www.youtube.com/user/CGPGrey ) makes educational videos that explain complex sociological, political and economic concepts in simple layman’s terms. Duolingo ( https://www.duolingo.com/ ) has made it easier than ever to learn foreign languages and Times for Kids ( http://www.timeforkids.com/news ) makes world news accessible for kids. Thus, by making information comprehensible for a wider audience, the Internet narrows the knowledge gap rather than widening it. Granted, Tichenor, Donohue & Olien focuses on public affairs and science knowledge, but even for these subject the above should be true (Tichenor et al, 1970).

Furthermore, I do not buy in on the argument that “stored information” renders people better prepared to understand a given topic. In our modern world, everything is but a Google search away. Therefore, the individual requires less stored information in order to understand a given subject. Context is effortlessly available, which makes it easy for the less educated to understand any topic. Neither “relevant social contact” is a factor that drives a widening of information gaps in the Internet era. Present day social media has made an infinite number of reference groups available for just about anyone. No matter the educational level, individuals have access to an abundance of interpersonal contacts on the Internet, which should make the likelihood of discussing public affairs likely for anyone that has that desire.

Also, “the nature of the mass media that delivers the information” could be questioned. If “science and public affairs news ordinarily lacks the constant repetition which facilitates learning and familiarity among lower status persons” (Tichenor et al, 1970) it would seem that the less educated are in luck in present day. Information is now being pushed through an abundance of different media that an individual might be exposed to throughout the day. Information does no longer come from just interpersonal contacts, radio, newspapers or TV. Newsletters, tweets, podcasts, RSS feeds, push notifications, text messages and social networks provide endless potential for repetition of important information.

However, all might not be lost for Tichenor, Donohue & Oliens hypothesis. The last factor, “selective exposure, acceptance, and retention of information” does seem to hold some relevance. This is because it connects with the findings of Wei & Hindmann (2011) who argue that in the Internet age it is not lack of accessibility, but quantity and quality of Internet use that drives information inequality. Further they conclude that the higher the socioeconomic status of an individual is, the higher is the informational use of the Internet for that person. The underlying argument is, that the more educated the person is, the better they harness the possibilities of the Internet (Wei & Hindmann, 2011).

Nonetheless, I refuse to abandon the idea of a narrowing knowledge gap in the era of the Internet. Yes, more well educated people might “use the internet better”, and there might very well be a knowledge gap, but the overall barriers to information have been, and continue to be, lowered. While there might be a widening gap between the very most and very least informed individuals, the general level of information in society has been heightened. Thus, we are better off in general, and the pessimistic tone of both Tichenor et al. (1970) and Wei & Hindmann (2011) has no right.

References Tiehenor, P. J., Donohue, G., & Olien, C. (1970). Mass Media Flow and Differential Growth in Knowledge. The Public Opinion Quarterly , 34 (2), 169-170.

Is two-step flow theory applicable to aid in understanding the knowledge gap hypothesis? Katz (2000) touches on this issue multiple times, especially in his “Media Multiplication and Social Segmentation” piece. He states that as media have evolved, they become more individualized and simultaneously globalized. Television started out with just a few channels nationwide, and now is considered a waning media with more cable channels than one can count. The internet has only magnified this individualization putting the focus on consumerism instead of on interpersonal and political frameworks. This poses a bit of an issue, considering social influence can be thought of in terms of diffusion among mass media and personal interactions (Katz, 2006)

The knowledge gap hypothesis describes the acquisition rate and breadth of knowledge will be greater for those with higher socioeconomic status than with lower (Tichenor et al., 1970). Indeed, one of the factors contributing to this difference is relevant social contact. Findings show that increased social integration of public issues shows an increased rate of diffusion of information. Similar to Katz, digital divide theory has found that the difference in knowledge is greater among the internet than with other traditional media (Wei & Hindman, 2011). This is in part a result in differences of Internet use, whether informational or accessible. Katz describes the cause of this divide as a disintegration of institutions (such as unions, political parties, and organizations) and replacement with individualism (Katz, 2000). Could it be possible that education could be thought of as an institution that has not dissolved as of yet? Could this be a possible answer for the growing divide of knowledge in socioeconomic groups?

Katz, E. (2000). Media multiplication and social segmentation. Departmental Papers (ASC), 161.

Katz, E. (2006). Rediscovering Gabriel Tarde∗. Political Communication, 23(3), 263-270.

Much like Andrew in the previous post, I have often pondered how the dissemination of information through media can affect the way that a user forms opinions about a specific topic or issue. This week’s readings for me were a peek into the world of social media distribution inequality and the knowledge gap that can often arise within differing social groups based on how that information is utilized to make informed decisions. Digital Divide theory is defined as an economic and social inequality that is rooted in the social categories of persons specific to populations, taking into account the access to, use of, and prior knowledge of information technologies (Weia & Hindman, 2011; Norris, 2001). The digital divide can be broken into two categories, the digital divide and the global digital divide.

In Global digital divide the theory states that while parts of the western world has developed the infrastructure to support the internet, much of the under developed world has not been able to put the amount of capital required into an infrastructure network to enable it to keep up in areas of technology, democracy, and labor markets. This is a problem that continues to grow as the economies of under developed nations continue to suffer from the global recession. In digital divide, it is stated that the divide may not be that a social group or individual does not have access to technology, but may have access to a lesser, or antiquated, technology. The focus here is on the ability to gain access to good computers, smart phones, and reliable internet connections. This is a problem that is faced in countries like the US, where the underprivileged and rural populations have a harder time gaining access to reliable internet and cannot afford good computers or technologically advanced mobile devices (Kim, 2003).

We have seen an increase in the use of social media in countries like Egypt and Libya, during the Arab Spring uprisings of 2011. Much of the information that was available to outsiders came through the use of personal digital devices by citizens of those countries. Up until that time, all of the media inside those countries borders was state sponsored and outsiders were only able to get the information that was afforded to them by sponsoring governments.

The digital divide was closed within its own borders, when individuals were given smart phones or able to purchase phones due to lower prices. The fall of the regimes that ruled these countries also allowed the throat hold on internet content to end. The ability of individuals to get out the message through social media and mass media outlets caused the global digital divide begin to close (Duffy, 2011).

Unfortunately, this has had a negative effect on the poorer populations of those countries. As Knowledge Gap Theory states, the more infusion of mass media content into a social system where the higher socioeconomic groups have faster and better access to this information, the larger the gap grows between the lower socioeconomic groups and higher socioeconomic groups (Tichenor, Donohue &Olien 1970). This is the bind that ties the two theories together in my mind and they are not mutually exclusive. While the digital divide and global digital divide may be shortened between social groups, differing social economic gaps can grow, because if you are monetarily challenged, unless the device and services are free, the ability to obtain the information that is placed out there is still very limited.

Duffy, M. J. (2011). “Smart phones in the Arab Spring: a revolution in gathering and reporting news”. http://www.academia.edu .

Kim, Y. J. (2003). “A theory of digital divide: Who gains and loses from technological changes?”. Journal of Economic Development 28 (1): 1-19.

Norris, P. (2001). Digital Divide: Civic Engagement, Information Poverty and the Internet Worldwide. Cambridge University Press.

Tichenor, P.A.; Donohue, G.A. & Olien, C.N. (1970). “Mass media flow and differential growth in knowledge”. Public Opinion Quarterly 34 (2): 159–170.

Wei, Lu, Hindman, D.B. (2011). “Does the Digital Divide Matter More? Comparing the Effects of New Media and Old Media Use on the Education-Based Knowledge Gap”. Mass Communication and Society 14 (2): 216-235.

Are knowledge gap and the digital divide theoretical framework the same thing, or are they two different things? If the later, what makes them different?

The knowledge gap hypothesis seems complementary to two-step flow. It helps to explain the formation of opinion leaders and followers and opens up avenues of research into how those formations could change in the future. Tichenor, Donohue, and Olien (1970) define the knowledge gap as the tendency for there to be a greater acquisition of knowledge among the more highly educated compared to the lower educated. They identify five factors that explain why this knowledge gap exists. They say of the third factor, relevant social contact, that “studies of diffusion among such groups as doctors and farmers tend to show steeper, more accelerated acceptance rates for more active, socially integrated individuals” (p.162). This reminded me of the drug study discussed by Katz (1957) where doctors who were more active socially were found to be the opinion leaders on the adoption of the new drug technology. The knowledge gap hypothesis goes further than stating who the opinion leaders are but also shows how they came to be the opinion leaders. They receive the information first thus giving them the opportunity to lead the discussion on a particular item and influence the followers. Jerit, Barabas, and Bolsen (2006) take this idea and see if it can be manipulated. They look specifically at political knowledge saying “political knowledge helps citizens…translate their opinions into meaningful forms of political participation” (p.266). They want to know if the knowledge gap can be reduced thus allowing more non-traditional opinion leaders to come forward. They found that print media increased the knowledge gap but television did not. This opens the way for other researchers to specifically target television as communication medium for the lower educated allowing them to gain more knowledge and thus more power within their political system. The potential is there for a more diverse range of opinion leaders to be heard in mainstream media than just those of high socioeconomic status.

References Jerit, J., Barabas, J., & Bolsen, T. (2006) Citizens, Knowledge, and the Information Environment. American Journal of Political Science, 50 (2), p. 266-282. Retrieved from http://onlinelibrary.wiley.com.ezproxy.lib.purdue.edu/doi/10.1111/j.1540-5907.2006.00183.x/pdf . Katz, E. (1957) The Two-Step Flow of Communication: An Up-To-Date Report on an Hypothesis. The Public Opinion Quarterly, 21, p.61-78. Retrieved from http://www.jstor.org.ezproxy.lib.purdue.edu/stable/2746790 . Tichenor, P.J., Donohue, G.A., & Olien, C.N. (1970) Mass Media Flow and Differential Growth in Knowledge. The Public Opinion Quarterly, 34 (2), p. 159-170. Retrieved from http://www.jstor.org/stable/2747414 .

This is the type of “connect the dots” thinking that makes this class worth teaching…

Information inequality from digital divide is generally used to describe the discrepancy between people who have access to the Internet, a new communication tool, and people who do not have the access to the technology. The general concept of information inequality mainly deals with the problem whether people physically access to the information or not. However, the inequality from digital divide has a multi-faceted concept of access. Van Dijk (2006) suggests four types of barriers to access information through digital media; (a) lack of physical access, (b) lack of mental or motivational access, (c) lack of digital skills, and (d) lack of actual usage of digital media. According to Van Dijk (2006), access problems of digital technology gradually flow from the first two kinds of access to the last two kinds. The general concept of digital divide only refers to the first of four kinds of access. As more and more people have gone online, many researchers have reconsidered digital divide from ‘the first-level digital divide’ associated with lack of physical and motivational access to ‘the second level digital divide’ caused by lack of digital skills and actual use. When the problems of physical and motivational approach have been explained, the problems of structurally different skills and uses become more operative.

Next, I would like to talk about new questions of digital divide in the new ear of social media. With emerge of new technology of the Internet, which makes information flow go faster and better to the public, new problems from the second-level of digital divide have been focused. In the same way, with popular use of socially connected communications in social media, a new question about digital divide should be discussed. As mentioned early, digital inequality researchers have expanded from a divide based simply on computer ownership to a range of inequalities in access and actual use of various digital technologies. Researchers of the Internet digital divide has dealt with understanding how digital skills, social networks and other resources mediate use of digital information (Hargittai, 2007; Mossberger et al., 2003). Recently, some studies have dealt with the socioeconomic participation gap (Correa, 2010; Hargittai & Walejko, 2008) for content sharing among youth with social media sites (Hargittai, 2007). However, many studies mainly focused on the ‘consumption’ of digital contents, rather than the ‘creating’ of digital contents. Creating contents is one of most important features in social media use. Schradie (2011) suggests that as the number of user-generated contents increases through social media sites, new empirical questions about digital inequality emerge from different types of consumers of social media such as contents consumers and contents generators. This is possible because social network sites, like Facebook, YouTube and Twitter, enable users to participate in creating online contents, which leads to increasing digital divide between those who are able to interact more fully with the technology and those who are passively consuming the contents. The study found that a digital production gap exists among different socio economic status (SES) groups, resulting that digital inequality happens more importantly for contents production than contents consumption.

Correa, T. (2010). The participation divide among “online experts”. Journal of Computer‐Mediated Communication, 16(1), 71-92. Hargittai, E. (2007). Whose space? Differences among users and non‐users of social network sites. Journal of Computer‐Mediated Communication, 13(1), 276-297. Hargittai, E., & Walejko, G. (2008). The Participation Divide: Content creation and sharing in the digital age 1. Information, Community and Society, 11(2), 239-256. Mossberger, K., Mary, K. M. C. J. T., Tolbert, C. J., & Stansbury, M. (2003). Virtual inequality: Beyond the digital divide. Georgetown University Press. Schradie, J. (2011). The digital production gap: The digital divide and Web 2.0 collide. Poetics, 39(2), 145-168. Van Dijk, J. A. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4), 221-235.

According to knowledge gap assumption, more highly educated people can obtain greater knowledge from news media. Because highly educated people would have higher reading and comprehension abilities as well as existing knowledge. Thus, demographic and socioeconomic status (SES) have been prove as strongest predictor of knowledge gap (Tichenor, Donohue, & Olien, 1970). However, as Internet has become most influential media in the world, researchers proposed the assumption of digital divide should focus on actual use than material access (Wei & Hindman, 2011). And research results indicates SES is more related to Internet use than traditional media (newspaper and television) use as well as Internet access. Moreover, different internet use is correlated with a greater knowledge gap than different traditional media use (Wei & Hindman, 2011). In this sense, the emphasis of digital divide should not only be on how to measure that people access to communication technology, but on how to use information of internet of the digital age. However, in social media area, whether communication technology access or information use can be predictors of knowledge gap? According to The Pew Charitable Trusts, the research results demonstrate that African-Americans and Latinos tend to use their phones to access the internet more than white people. The implication of this result is that it shows that access to the internet, or use information of internet is not enough to measure people’s knowledge gap. Social media and communication technology enable people access internet and search information easily than ever. This change in focus raises several questions for knowledge gap generation. I think in social media area, existing knowledge, relevant social contact and selective exposure would be main predictors of people’s knowledge gap. Because existing knowledge and relevant social contact are associated with people’s SES, which makes people exposed to various information in their social network no matter online or offline. And selective exposure is correlated with how people personalize the internet and social media use, which would influence how people choose and gain knowledge. I think these 3 factors are still very important predictors of knowledge gap in social media age.

Wei, L., & Hindman, D. B. (2011). Does the digital divide matter more? Comparing the effects of new media and old media use on the education-based knowledge gap. Mass Communication and Society, 14(2), 216-235. Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth in knowledge. Public opinion quarterly, 34(2), 159-170.

Good day sir! I am Violy Romano, a third year student from Mindanao University of Science and Technology in Cagayan de Oro city, Philippines. I am currently taking Bachelor of Science in Technology Communication Management.

In this semester sir, we have a subject theories of communication and each one of us has to report the assigned theory. Moreover, I am assigned to report the Knowledge Gap theory and I searched it through google about the theory but there is a part of it which I did not understand. I want to know more about the knowledge gap theory graph that is being shown in your blog sir. I hope I may get to understand more about this theory sir so that I can discuss it to the class very well and my classmates would understand.

I am hoping for your favorable response.

Sincerely Yours, Violy Romano

The old graph was there for illustration purposes alone. I added a new one, which is more descriptive. See the caption. If you have questions, feel free to post here again…

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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15 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

How to find and fill gaps in the literature [Research Gaps Made Easy]

As we dive deeper into the realm of research, one term repeatedly echoes in the corridors of academia: “gap in literature.”

But what does it mean to find a gap in the literature, and why is it so crucial for your research project?

A gap in the literature refers to an area that hasn’t been studied or lacks substantial inquiry in your field of study. Identifying such gaps allows you to contribute fresh insights and innovation, thereby extending the existing body of knowledge.

It’s the cornerstone for every dissertation or research paper, setting the stage for an introduction that explicitly outlines the scope and aim of your investigation.

This gap review isn’t limited to what has been published in peer-reviewed journals; it may also include conference papers, dissertations, or technical reports, i.e., types of papers that provide an overview of ongoing research. 

This step is where your detective work comes in—by spotting trends, common methodologies, and unanswered questions, you can unearth an opportunity to explore an unexplored domain, thereby finding a research gap. 

Why Looking for Research Gaps is Essential

Looking for research gaps is essential as it enables the discovery of novel and unique contributions to a particular field.

By identifying these gaps, found through methods such as analyzing concluding remarks of recent papers, literature reviews, examining research groups’ non-peer-reviewed outputs, and utilizing specific search terms on Google Scholar, one can discern the trajectory of ongoing research and unearth opportunities for original inquiry.

These gaps highlight areas of potential innovation, unexplored paths, and disputed concepts, serving as the catalyst for valuable contributions and progression in the field. Hence, finding research gaps forms the basis of substantial and impactful scientific exploration.

Then your research can contribute by finding and filling the gap in knowledge. 

Method 1: Utilizing Concluding Remarks of Recent Research

When embarking on a quest to find research gaps, the concluding remarks of recent research papers can serve as an unexpected treasure map.

This section of a paper often contains insightful comments on the limitations of the work and speculates on future research directions.

These comments, although not directly pointing to a research gap, can hint at where the research is heading and what areas require further exploration.

Consider these remarks as signposts, pointing you towards uncharted territories in your field of interest.

For example, you may come across a conclusion in a recent paper on artificial intelligence that indicates a need for more research on ethical considerations. This gives you a direction to explore – the ethical implications of AI. 

However, it’s important to bear in mind that while these statements provide valuable leads, they aren’t definitive indicators of research gaps. They provide a starting point, a clue to the vast research puzzle.

Your task is to take these hints, explore further, and discern the most promising areas for your investigation. It’s a bit like being a detective, except your clues come from scholarly papers instead of crime scenes!

Method 2: Examining Research Groups and Non-peer Reviewed Outputs

If concluding remarks are signposts to potential research gaps, non-peer reviewed outputs such as preprints, conference presentations, and dissertations are detailed maps guiding you towards the frontier of research.

These resources reflect the real-time development in the field, giving you a sense of the “buzz” that surrounds hot topics.

These materials, presented but not formally published, offer a sneak peek into ongoing studies, providing you with a rich source of information to identify emerging trends and potential research gaps.

For instance, a presentation on the impact of climate change on mental health might reveal a new line of research that’s in its early stages.

One word of caution: while these resources can be enlightening, they have not undergone the rigorous peer review process that published articles have.

This means the quality of research may vary and the findings should be interpreted with a critical eye. Remember, the key is to pinpoint where the research is heading and then carve out your niche within that sphere.

Exploring non-peer reviewed outputs allows you to stay ahead of the curve, harnessing the opportunity to investigate and contribute to a burgeoning area of study before it becomes mainstream.

Method 3: Searching for ‘Promising’ and ‘Preliminary’ Results on Google Scholar

With a plethora of research at your fingertips, Google Scholar can serve as a remarkable tool in your quest to discover research gaps. The magic lies in a simple trick: search for the phrases “promising results” or “preliminary results” within your research area. Why these specific phrases? Scientists often use them when they have encouraging but not yet fully verified findings.

To illustrate, consider an example. Type “promising results and solar cell” into Google Scholar, and filter by recent publications.

The search results will show you recent studies where researchers have achieved promising outcomes but may not have fully developed their ideas or resolved all challenges.

These “promising” or “preliminary” results often represent areas ripe for further exploration.

They hint at a research question that has been opened but not fully answered. However, tread carefully.

While these findings can indeed point to potential research gaps, they can also lead to dead ends. It’s crucial to examine these leads with a critical eye and further corroborate them with a comprehensive review of related research.

Nevertheless, this approach provides a simple, effective starting point for identifying research gaps, serving as a launchpad for your explorations.

Method 4: Reading Around the Subject

Comprehensive reading forms the bedrock of effective research. When hunting for research gaps, you need to move beyond just the preliminary findings and delve deeper into the context surrounding these results.

This involves broadening your view and reading extensively around your topic of interest.

In the course of your reading, you will start identifying common themes, reoccurring questions, and shared challenges in the research.

Over time, patterns will emerge, helping you recognize areas where research is thin or missing.

For instance, in studying autonomous vehicles, you might find recurring questions about regulatory frameworks, pointing to a potential gap in the legal aspects of this technology.

However, this method is not about scanning through a huge volume of literature aimlessly. It involves strategic and critical reading, looking for patterns, inconsistencies, and areas where the existing literature falls short.

It’s akin to painting a picture where some parts are vividly detailed while others remain sketchy. Your goal is to identify these sketchy areas and fill in the details.

So grab your academic reading list, and start diving into the ocean of knowledge. Remember, it’s not just about the depth, but also the breadth of your reading, that will lead you to a meaningful research gap.

Method 5: Consulting with Current Researchers

Few methods are as effective in uncovering research gaps as engaging in conversations with active researchers in your field of interest.

Current researchers, whether they are PhD students, postdoctoral researchers, or supervisors, are often deeply engaged in ongoing studies and understand the current challenges in their respective fields.

Start by expressing genuine interest in their work. Rather than directly asking for research gaps, inquire about the challenges they are currently facing in their projects.

You can ask, “What are the current challenges in your research?”

Their responses can highlight potential areas of exploration, setting you on the path to identifying meaningful research gaps.

Moreover, supervisors, particularly those overseeing PhD and Master’s students, often have ideas for potential research topics. By asking the right questions, you can tap into their wealth of knowledge and identify fruitful areas of study.

While the act of discovering research gaps can feel like a solitary journey, it doesn’t have to be.

Engaging with others who are grappling with similar challenges can provide valuable insights and guide your path. After all, the world of research thrives on collaboration and shared intellectual curiosity.

Method 6: Utilizing Online Tools

The digital age has made uncovering research gaps easier, thanks to a plethora of online tools that help visualize the interconnectedness of research literature.

Platforms such as:

• Connected Papers,
• ResearchRabbit, and

allow you to see how different papers in your field relate to one another, thereby creating a web of knowledge.

Upon creating this visual web, you may notice that many papers point towards a certain area, but then abruptly stop. This could indicate a potential research gap, suggesting that the topic hasn’t been adequately addressed or has been sidelined for some reason.

By further reading around this apparent gap, you can understand if it’s a genuine knowledge deficit or merely a research path that was abandoned due to inherent challenges or a dead end.

These online tools provide a bird’s eye view of the literature, helping you understand the broader landscape of research in your area of interest.

By examining patterns and relationships among studies, you can effectively zero in on unexplored areas, making these tools a valuable asset in your quest for research gaps.

Method 7: Seeking Conflicting Ideas in the Literature

In scientific research, areas of conflict can often be fertile ground for finding research gaps. These are areas where there’s a considerable amount of disagreement or ongoing debate among researchers.

If you can bring a fresh perspective, a new technique, or a novel hypothesis to such a contentious issue, you may well be on your way to uncovering a significant research gap.

Take, for instance, an area in psychology where there is a heated debate about the influence of nature versus nurture.

If you can introduce a new dimension to the debate or a method to test a novel hypothesis, you could potentially fill a significant gap in the literature.

Investigating areas of conflict not only opens avenues for exploring research gaps, but it also provides opportunities for you to make substantial contributions to your field. The key is to be able to see the potential for a new angle and to muster the courage to dive into contentious waters.

However, engaging with conflicts in research requires careful navigation.

Striking the right balance between acknowledging existing research and championing new ideas is crucial.

In the end, resolving these conflicts or adding significant depth to the debate can be incredibly rewarding and contribute greatly to your field.

The Right Perspective Towards Research Gaps

The traditional understanding of research gaps often involves seeking out a ‘bubble’ of missing knowledge in the sea of existing research, a niche yet to be explored. However, in today’s fast-paced research environment, these bubbles are becoming increasingly rare.

The paradigm of finding research gaps is shifting. It’s no longer just about seeking out holes in existing knowledge, but about understanding the leading edge of research and the directions it could take. It involves not just filling in the gaps but extending the boundaries of knowledge.

To identify such opportunities, develop a comprehensive understanding of the research landscape, identify emerging trends, and keep a close eye on recent advancements.

Look for the tendrils of knowledge extending out into the unknown and think about how you can push them further. It might be a challenging task, but it offers the potential for making substantial, impactful contributions to your field. 

Remember, every great innovation begins at the edge of what is known. That’s where your research gap might be hiding.

Wrapping up – Literature and research gaps

Finding and filling a gap in the literature is a task crucial to every research project. It begins with a systematic review of existing literature – a quest to identify what has been studied and more importantly, what hasn’t.

You must delve into the rich terrain of literature in their field, from the seminal, citation-heavy research articles to the fresh perspective of conference papers. Identifying the gap in the literature necessitates a thorough evaluation of existing studies to refine your area of interest and map the scope and aim of your future research.

The purpose is to explicitly identify the gap that exists, so you can contribute to the body of knowledge by providing fresh insights. The process involves a series of steps, from consulting with faculty and experts in the field to identify potential trends and outdated methodologies, to being methodological in your approach to identify gaps that have emerged.

Upon finding a gap in the literature, we’ll ideally have a clearer picture of the research need and an opportunity to explore this unexplored domain.

It is important to remember that the task does not end with identifying the gap. The real challenge lies in drafting a research proposal that’s objective, answerable, and can quantify the impact of filling this gap. 

It’s important to consult with your advisor, and also look at commonly used parameters and preliminary evidence. Only then can we complete the task of turning an identified gap in the literature into a valuable contribution to your field, a contribution that’s peer-reviewed and adds to the body of knowledge.

To find a research gap is to stand on the shoulders of giants, looking beyond the existing research to further expand our understanding of the world.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk wisdom that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis.   In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. How would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when it would be impossible or difficult to  conduct an experiment . These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

A Word From Verywell

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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• Knowledge Base

Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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5.2 - writing hypotheses.

The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis (\(H_0\)) and an alternative hypothesis (\(H_a\)).

When writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the direction of the test (non-directional, right-tailed or left-tailed), and (3) the value of the hypothesized parameter.

• At this point we can write hypotheses for a single mean (\(\mu\)), paired means(\(\mu_d\)), a single proportion (\(p\)), the difference between two independent means (\(\mu_1-\mu_2\)), the difference between two proportions (\(p_1-p_2\)), a simple linear regression slope (\(\beta\)), and a correlation (\(\rho\)). 
• The research question will give us the information necessary to determine if the test is two-tailed (e.g., "different from," "not equal to"), right-tailed (e.g., "greater than," "more than"), or left-tailed (e.g., "less than," "fewer than").
• The research question will also give us the hypothesized parameter value. This is the number that goes in the hypothesis statements (i.e., \(\mu_0\) and \(p_0\)). For the difference between two groups, regression, and correlation, this value is typically 0.

Hypotheses are always written in terms of population parameters (e.g., \(p\) and \(\mu\)).  The tables below display all of the possible hypotheses for the parameters that we have learned thus far. Note that the null hypothesis always includes the equality (i.e., =).

Watch CBS News

How to travel around the Francis Scott Key Bridge collapse in Baltimore: A look at the traffic impact and alternate routes

By Rohan Mattu

Updated on: April 1, 2024 / 8:13 AM EDT / CBS Baltimore

BALTIMORE -- The collapse of the Francis Scott Key Bridge in Baltimore early on the morning of March 26  led to a major traffic impact for the region and cut off a major artery into and out of the port city. 

Drivers are told to prepare for extra commuting time until further notice.

Alternate routes after Francis Scott Key Bridge collapse

Maryland transit authorities quickly put detours in place for those traveling through Dundalk or the Curtis Bay/Hawkins Point side of the bridge. The estimated 31,000 who travel the bridge every day will need to find a new route for the foreseeable future. 

The outer loop I-695 closure shifted to exit 1/Quarantine Road (past the Curtis Creek Drawbridge) to allow for enhanced local traffic access. 

The inner loop of I-695 remains closed at MD 157 (Peninsula Expressway). Additionally, the ramp from MD 157 to the inner loop of I-695 will be closed. 

Alternate routes are I-95 (Fort McHenry Tunnel) or I-895 (Baltimore Harbor Tunnel) for north/south routes. 

Commercial vehicles carrying materials that are prohibited in the tunnel crossings, including recreation vehicles carrying propane, should plan on using I-695 (Baltimore Beltway) between Essex and Glen Burnie. This will add significant driving time.    

Where is the Francis Scott Key Bridge? 

The Key Bridge crosses the Patapsco River, a key waterway that along with the Port of Baltimore serves as a hub for East Coast shipping. 

The bridge is the outermost of three toll crossings of Baltimore's Harbor and the final link in Interstate 695, known in the region as the Baltimore Beltway, which links Baltimore and Washington, D.C. 

The bridge was built after the Baltimore Harbor Tunnel reached capacity and experienced heavy congestion almost daily, according to the MDTA. 

Tractor-trailer inspections

Tractor-trailers that now have clearance to use the tunnels will need to be checked for hazardous materials, which are not permitted in tunnels, and that could further hold up traffic. 

The MDTA says vehicles carrying bottled propane gas over 10 pounds per container (maximum of 10 containers), bulk gasoline, explosives, significant amounts of radioactive materials, and other hazardous materials are prohibited from using the Fort McHenry Tunnel (I-95) or the Baltimore Harbor Tunnel (I-895).  

Any vehicles transporting hazardous materials should use the western section of I-695 around the tunnels, officials said. 

• Francis Scott Key Bridge
• Bridge Collapse
• Patapsco River

Rohan Mattu is a digital producer at CBS News Baltimore. Rohan graduated from Towson University in 2020 with a degree in journalism and previously wrote for WDVM-TV in Hagerstown. He maintains WJZ's website and social media, which includes breaking news in everything from politics to sports.

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