16.1 Reading Reports of Sociological Research

By now, you should have a good idea about the basic components of sociological research projects. You know how sociological research is designed, and you are familiar with how to frame a review of sociological literature. In Chapter 5 “Literature Review” and Chapter 14 “Research Proposals,” we discussed the various components of a literature review for research projects, and presented some tips on how to review literature as you design your own research project. We hope that you will find the sociological literature to be of interest and relevance to you beyond figuring out how to summarize and critique it in relation to your research plans. Sociologists like to think their research matters, but it cannot matter if our research reports go unread or are not understandable. In this section we will review some previous material regarding sociological literature, and consider some additional tips for reading and understanding reports of sociological research.

As mentioned previously, reading the abstract that appears in most reports of scholarly research will provide you with an excellent, easily digestible review of a study’s major findings and the framework the author is using to position her findings. Abstracts typically contain just a few hundred words, so reading them is a nice way to quickly familiarize yourself with a study. Another thing to look for as you set out to read and comprehend a research report is the author’s acknowledgments. Who supported the work by providing feedback or other assistance? If relevant, are you familiar with the research of those who provided feedback on the report you are about to read? Are any organizations mentioned as having supported the research in some way, either through funding or by providing other resources to the researcher? Familiarizing yourself with an author’s acknowledgments will give you additional contextual information within which to frame and understand what you are about to read.

Once you have read the abstract and acknowledgments, you could next peruse the discussion section near the end of the report. You might also look at any tables that are included in the article. A table provides a quick, condensed summary of the report’s key findings. The use of tables is not limited to one form or type of data, though they are used most commonly in quantitative research. Tables are a concise way to report large amounts of data. Some tables present descriptive information about a researcher’s sample. These tables will likely contain frequencies (N) and percentages (%). For example, if gender happened to be an important variable for the researcher’s analysis, a descriptive table would show how many and what percent of all study participants are women, and how many/what percent are men. Frequencies, or “how many,” will probably be listed as N, while the percent symbol (%) might be used to indicate percentages.

In a table presenting a causal relationship, independent variable attributes are typically presented in the table’s columns, while dependent variable attributes are presented in rows. This allows the reader to scan across a table’s rows to see how values on the dependent variable attributes change as the independent variable attribute values change. Tables displaying results of quantitative analysis will also likely include some information about the strength and statistical significance of the relationships presented in the table. These details tell the reader how likely it is that the relationships presented will have occurred simply by chance.

Of course, we cannot assume that these patterns did not simply occur by chance. How confident can we be that the findings presented in the table did not occur by chance? This is where tests of statistical significance come in handy. Statistical significance tells us the likelihood that the relationships we observe could be caused by something other than chance. While your statistics class will give you more specific details on tests of statistical significance and reading quantitative tables, the important thing to be aware of as a non-expert reader of tables is that some of the relationships presented will be statistically significant and others may not be. Tables should provide information about the statistical significance of the relationships presented. When reading a researcher’s conclusions, be sure to pay attention to which relationships are statistically significant and which are not.

In Table 16.1 “Percentage Reporting Harassing Behaviours at Work” from Saylor Academy´s gender research, you will see that a p value is noted in the last very column of the table. A is a statistical measure of the probability that there is no relationship between the variables under study. Another way of putting this is that the p value provides guidance on whether or not we should reject the null hypothesis. The null hypothesis is simply the assumption that no relationship exists between the variables in question. In Table 16.1 “Percentage Reporting Harassing Behaviours at Work”, we see that for the first behaviour listed, the p value is 0.623. This means that there is a 62.3% chance that the null hypothesis is correct in this case. In other words, it seems likely that any relationship between observed gender and experiencing threats to safety at work in this sample is simply due to chance.

In the final row of the table, however, we see that the p value is 0.039. In other words, there is a 3.9% chance that the null hypothesis is correct. Thus, we can be somewhat more confident than in the preceding example that there may be some relationship between a person’s gender and his experiencing the behaviour noted in this row. We might say that this finding is significant at the .05 level. This means that the probability that the relationship between gender and experiencing staring or invasion of personal space at work is due to sampling error alone is less than 5 in 100.

When testing hypotheses, social scientists generally state their findings in terms of rejecting the null hypothesis rather than making bold statements about the relationships observed in their tables. You can learn more about creating tables, reading tables, and tests of statistical significance in a class focused exclusively on statistical analysis.

Note: Sample size was 138 for women and 43 for men

Having read the tables in a research report, along with the abstract, acknowledgments, and discussion in the report, you are finally ready to read the report in its entirety. As you read a research report, there are several questions you can ask yourself about each section, from abstract to conclusion. Those questions are summarized in Table 16.2 “Questions Worth Asking While Reading Research Reports”. Keep in mind that the questions covered here are designed to help you, the reader, to think critically about the research you come across and to get a general understanding of the strengths, weaknesses, and key takeaways from a given study. We hope that by considering how you might respond to the following questions while reading research reports, you will feel confident that you could describe the report to others and discuss its meaning and impact with them.