April 18, 2012 at 3:17 pm (Uncategorized)
March 25, 2012 at 3:47 pm (Uncategorized)
Well, we are finally there – the last stats blog in the semester or should I say in my entire life? 😀
I saw a friend of mine writing her blog on a related topic and I thought it would be a good idea to have a go at it myself. This is something that we did not cover in class and I decided it would be quite useful and informative for everyone.
The word sociometry comes from Latin – “socius” meaning social and “metrum” meaning measure. Knowing this, it is easy to guess what the definition of sociometry is. It is a way of measuring the degree of relatedness among individuals. (http://www.hoopandtree.org/sociometry.htm) The term sociometry and the measure itself was developed by Jacob Moreno (1887 -1974). He noticed a simple thing happening in groups – people were being attracted to or drawing away from others thus creating different patterns of interaction. (http://www.sociometry.co.nz/sociometry.htm) Moreno discovered that when people are allowed to chose who to interact with in a group, higher levels of satisfaction and purpose achievement where noted. Moreno’s first sociometric study produced profound results. It was conducted at the New York State Training School for Girls in Hudson. Morenon was invited there to work with girls and staff in order to try and reduce frequent runaways. So, he used sociometric techniques to assign girls to various residential cottages on their preference. The results showed that assignments based on sociometry greatly reduced the number of runaways from the school. (http://www.hoopandtree.org/sociometry.htm) Since then, a lot more sociometric studies were conducted in different settings.
Sociometry is based on the fact that people make choices on their personal relationships. If you think about your everyday life you will realise that you make that kind of choices very often – when thinking where to sit or stand, when deciding whether someone is friendly or not and when evaluating people’s relationships. So, sociometry is a useful and powerful tool for reducing conflict and improving communication and for assessing the dynamics of groups. the purpose of it is to facilitate group task effectiveness and participant satisfaction.
The most common technique in sociometry is sociometric testing. In this kind of test each member of a group is asked to choose from all the other members those with whom they would prefer to interact with in a specific situation. The situations must be real such as “group study” or “play”. (http://www.vkmaheshwari.com/WP/?p=50) The basic structure of sociometric intervention is as follows:
(1) Identify the group to be studied
(2) Develop the criterion,
(3) Establish rapport / warm-up,
(4) Gather sociometric data,
(5) Analyze and interpret data,
(6) Feed back data, either: (a) to individuals, or (b) in a group setting,
(7) Develop and implement action plans
So, from everything written above, the advantages of sociometry are quite obvious. You also, may have thought of some weaknesses by this point. One major weakness of sociometric tests is that they are a self-report measure, which means they are subjective and may involve social desirability bias. Also, the use of sociometric measures provoked some debates whether it was ethical to use them – mostly because they show people’s opinions of others and this is not something that should be made public.
To conclude, I personally think that sociometry is a good and useful tool. Even though it has it’s weakness – and common is there anything in the world that doesn’t have any? – I think we can make a good use of sociometric techniques, especially in psychology.
Thanks for reading! x
References:
http://www.sociometry.co.nz/sociometry.htm
March 13, 2012 at 12:42 pm (Uncategorized)
March 10, 2012 at 2:21 pm (Uncategorized)
We’ve been studying these two designs for a very long time. However, when it came to the exam, even though I knew everything, I still felt like I confused them. So, I thought it would be a good idea to write a blog on this topic.
Experimental design is a way of allocating participants to different groups in an experiment. In a research study there are usually two or more independent variables and the researcher must decide how they will allocate participants to these IVs. For example, if there is 10 participants, will all of them take part in all conditions or will they be split in half? The most commonly used types of designs are independent measures and repeated measures. With respect to these two designs ANOVA is very similar to t-tests. Independent-measures design (or between-subjects design) means that there is a separate sample for each of the treatments being compared. Repeated-measures design, means that the same sample is tested in all of the different treatments. The only big difference between an analysis of variance and a t-test in this case is that ANOVA can be used to evaluate the results of the study that has more than one factor. (Gravetter and Wallnau)
As in everything else in statistics, there are various pros and cons for both of these designs. Lets look at the independent measures first. The big advantage of this design is that it avoids order effects (e.g. practice, fatigue). Participants only take part in one condition only and therefore there is no real chance that they will get bored or wise to the requirements of the experiment. (http://www.simplypsychology.org/experimental-designs.html) The disadvantages of the between subjects design are: 1. More people are needed and therefore it is much more time-consuming; 2. No two people are the same and therefore participant variables such as age, sex, etc. may interfere.
Repeated measures uses the same participants in every treatment condition and therefore confounding variables such as participant variables are eliminated. Using the same people in every condition, means less participants needed. Of course, there are also a few problems associated with this design. Nothing is perfect in our world 😛 Order effects can become a problem here. Something that was a n advantage in an IMD becomes a downfall here. Order effects refer to the order of the conditions having an effect on participants’ performance. So, for example, performance in the second condition may become better because the participants already know what to do or it may become worse because they get tired. However, even though order effects interfere, there is a solution. Counterbalancing – a magic tool that helps researchers to combat the order effects. Counterbalancing means alternating the order of the conditions. This helps to make sure that practice effects are distributed equally across the conditions. (http://www.psychmet.com/id16.html)
So, as you can see there are pros and cons in each of the designs. However, I personally think that repeated measures is better in a way that there is fewer problems associated with it and there is solutions for some of them. Which one of the two designs do you, guys prefer? Or are you the type that goes for an alternative ( e.g. matched pairs)? See what you think 🙂
Refference:
Statistics for the bahavioral sciences – Gravetter and Wallnau
February 19, 2012 at 3:06 pm (Uncategorized)
Publication bias or as it is more often referred to – “the file drawer problem” is defined as an influence of the results of a study on whether or not that study is published. There are a number of reasons as to how the results of a study can influence it’s publication. It may, for example, turn out that the results of the experiment are not statistically significant or only partially significant. Another reason for not publishing could be the that the results do not agree with the expectations of the researcher or sponsor, which sometimes can be a major problem that leads to sometimes dangerous outcomes such as result falsification. So as you see the nature of publication bias can vary. However the most common one is failing to reject null hypothesis (i.e. not getting a statistically significant result).
First of all, let me tell you – publication bias is very common and not only in psychology but also in a lot of other areas. In research literature there has been a few studies that found evidence of publication bias. One of the examples of such research is “Publication Decisions Revisited: The Effect of the Outcome of Statistical Tests on the Decision to Publish and Vice Versa” by T. D. Sterling and colleagues. This was a review of the literature through 1995 that indicated the occurrence of the file drawer effect. Another example could be a study by Hopewell et al (Publication Bias in Clinical Trials due to Statistical Significance or Direction of Trial Result) who concluded that “Trials with positive findings are published more often, and more quickly, than trials with negative findings.” By this point some of you may be a little puzzled and thinking – well OK, there is such thing as the file drawer effect, but what wrong does it do, is it really that big of a problem? Well, my opinion is that it is a big problem in research and there are a number of reasons, but I think the most important one is this:
Most of us know (maybe not, but I assume you do:)) that when a significance level of 0.05 is set in a study, about 5% of repeats of research will falsely reject the null hypothesis when it is actually true. Therefore, if only statistically significant results are published, then it is actually a mis-representation of a true situation. So, because of that some false effects may appear to be empirically supported. It is also very misleading for other researchers who may be wasting their time doing research on something that has already been well-researched and just not reported. Quite unfair, don’t you think?
Since the file drawer effect was researched and identified as a threat, there has been a number of attempts to try and detect publication bias. Almost all methods for doing so have their problems and have been criticised a lot. However, they are still used nowadays. The most famous one was proposed by Robert Rosenthal is based on probability calculations. The method is known as the fail-safe file drawer (or FSFD) analysis. It involves calculating a “fail-safe” number, which helps to decide whether or not the results of a study are resistant to publication bias threat.
In conclusion, the file drawer effect is a big problem in research. Different attempts to detect publication bias, even the most famous ones have only been partially successful and therefore cannot be heavily relied on even though are still in use. I think, that a solution for reducing publication bias could be letting people know of the results of your experiment even if they were not significant. I know, there aren’t any opportunities to publish such findings, but there are a number of websites where you can post information about your attempts and findings regardless of how significant they were. The most used ones are: www.psychologyreplications.org and www.psychfiledrawer.org
Reference:
R. Rosenthal (1979) The “file drawer problem” and tolerance for null results, Psychological Bulletin, Vol. 86, No. 3, 838-641. Retrieved from http://www.cs.ucl.ac.uk/staff/M.Sewell/faq/publishing-research/Rosenthal1979.pdf
T. D. Sterling, W. L. Rosenbaum and J. J. Weinkam (Publication Decisions Revisited: The Effect of the Outcome of Statistical Tests on the Decision to Publish and Vice Versa, The American Statistician, 1995, vol 49 No. 1, pp. 108 – 112
Hopewell, S. et al (Publication Bias in Clinical Trials due to Statistical Significance or Direction of Trial Result, Cochrane Review 2009, Issue 1; abstract available at www.thecochrane library.com)
February 3, 2012 at 1:59 pm (Uncategorized)
this is my first blog in this semester and I apologise if it is a little bit boring 🙂 Hopefully, you will at least find it informative. Have fun reading, xox
In order to see the differences between a case study and a single case study, we need to first understand what these two designs are and what is involved in them.
Case study is a detailed and intensive study of one person, group, event or community. According to Stake (1995), case study research is concerned with the complexity and particular nature of the case in question. A lot of best-known researchers in psychology and in other areas such as sociology used case studies to investigate different aspects of human nature. For example, most of Freud’s work and theories were developed using this particular design. He conducted very detailed investigations into his patients’ lives in order to understand and help them to overcome their disorders. Most famous case studies of Freud are Little Hans and The Rat Man. In psychology, case studies are often studies of single individual and the information presented in it is mostly biographical. In order to produce such a case study of one person, various techniques may be used. The most common one is an interview, which involves talking to the person of interest as well as to their friends, relatives, employers and others who have a good knowledge of them. other sources of information for a case study are documents, archival records, direct observation and participant observation. So, in a few words case studies provide detailed and rich information, they help researchers to generate new ideas and permit investigation of otherwise impractical/unethical situations.
Single case design or single-subject design is a design that relies on comparison of treatment effects on a single subject or group of single subjects.(http://allpsych.com/researchmethods/singlesubjectdesign.html) Single-subject design are thought to be a direct result of Skinner’s research. This design is sensitive to individual differences. It often involves using large number of participants in a study, however – individuals in the study serve as their own control and therefore the design is called single-subject. Single-subject design has a number of requirements such continuous assessment, baseline assessment and variability in the data. Single-subject designs are quite popular because they are very flexible and highlight individual differences in response to treatment/intervention. Single subject research design is most often used in applied fields of psychology and education.
As you can see, there are a number of obvious differences between a case study and single case design. For one, a case study focuses on one individual whereas single case design studies usually focus on a number of people. For two, case studies look at the history of a person, whereas single-subject design looks at the effects of treatment in one person and then compares it to others. However, the most important thing to be mentioned is that single-subject designs improve on case studies and therefore they are, in my opinion, a more extensive source of knowledge.
References: Mcleod, S. A. (2008). Simply Psychology; . Retrieved 3 February 2012, from http://www.simplypsychology.org/case-study.html
Social research methods by Alan Bryman – 2nd edition.
http://allpsych.com/researchmethods/singlesubjectdesign.html
November 25, 2011 at 3:43 pm (Uncategorized)
November 24, 2011 at 7:01 pm (Uncategorized)
It is impossible to say which of these two methods is better, because they are so very different and both are used in different situations. According to social science research glossary: “Qualitative Research – is a field of social research that is carried out in naturalistic settings and generates data largely through observations and interviews. Compared to quantitative research, which is principally concerned with making inferences from randomly selected samples to a larger population, qualitative research is primarily focused on describing small samples in non-statistical ways”. (http://www.researchconnections.org/childcare/research-glossary#Q)
Qualitative research presents “human” side of an issue. It is a very good methodology for studying sometimes contradictory behaviours, beliefs, and relationships of individuals. This way qualitative research is no less important than quantitative. However, I personally think that in some ways it can be more complicated. This is particularly obvious when you try to compare the two methods. Qualitative method is a time consuming procedure, especially when it comes to the analysis of data. Open-ended questions are used to collect the data, which means that a researcher has to look for common themes emerging. Many of the first attempts of analysis will probably be discarded later and this makes qualitative methods a little chaotic and “all over the place”. Also in qualitative research semi-structured methods are used which makes it more flexible. Quantitative method in turn is more strict. It uses highly structured methods such as questionnaires and surveys. Closed-ended questions are used and the study is always stable from the beginning to an end.
Taking all of these points into consideration some people from the general public may think that qualitative research is not scientific. However, this isn’t true and scientists, especially psychologists, know better than that. Qualitative method is as scientific as quantitative as it shares the characteristics of scientific research. More precisely qualitative research method seeks answers to a question, uses a predefined set of procedures to answer that question, collects evidence, and produces findings that both weren’t predetermined in advance and are applicable beyond the boundaries of the study. (http://www.fhi.org/nr/rdonlyres/etl7vogszehu5s4stpzb3tyqlpp7rojv4waq37elpbyei3tgmc4ty6dunbccfzxtaj2rvbaubzmz4f/overview1.pdf) Also, whilst thinking about whether the qualitative method of research is scientific or not, I found a very interesting study on this issue. This study investigated which of the two methods was more in favour amongst medical trainees and physicians. The researchers studied 32 participants who had different levels of medical training. The findings were that participants favoured quantitative methodologies over qualitative. However, an interesting point was made by the researchers – participants were actually largely unaware of the principles and paradigms of qualitative methodologies.
In conclusion, I think that both qualitative and quantitative methods are very important and it is impossible to say which one of them is better than another. I personally think that on the one hand qualitative research methods are more interesting because they actually investigate phenomena rather then test hypothesis about it. On the other hand, I think that quantitative methodologies are more reliable as they use numbers (which is probably a bit silly of me). So, as it is so difficult to say which is better, I say -combine both methodologies in your research.
Refferences:
(http://www.researchconnections.org/childcare/research-glossary#Q)
Is it science? A study of the attitudes of medical trainees
and physicians toward qualitative and quantitative
research. (Goguen, J., Knight, M. & Tiberiu, R.) 2007, retrieved from: http://0-www.springerlink.com.unicat.bangor.ac.uk/content/b7ju175612h4m441/fulltext.pdf
October 23, 2011 at 4:54 pm (Uncategorized)
I personally think that the importance of research findings is determined by many factors. However, the first and most important one is the question that particular research addresses, because the hypothesis and the findings are very closely linked together. The question that an experiment addresses should be one that asks about something that other people care about, it should build on what you and others already know and it should allow to learn something new. (http://ed-web3.educ.msu.edu/digitaladvisor/Research/whatimportant.htm) For example, if you are interested in who would make a better astronaut – a dog or a monkey – that won’t do, because it is a question that reflect your own interest and a person from the general public won’t be interested in the findings. The question that you want to address should be one that is developed from an established finding or theory or from a social issue. That way you will make sure that your findings will be interesting and helpful for as many people as it can be.
Another factor that makes your findings important is the issue of reliability and validity, because even if the information you find out is good it should also be established as true. Validity is a tool that shows whether the experiment measures what it claims to measure. There are a few different types of experimental validity, but I think that the most important ones are internal and external validity. Strong internal validity means that conclusions about causal relationships can be made between the two variables. The external validity shows the extent to which the findings can be generalised to real life – other cases, other people and other places.
Reliability is a tool that shows whether the findings are consistent. The question that reliability asks is – will you get the same findings if you repeat the experiment under the same conditions? Reliability can be measured by a variety of ways and one of them is test-retest reliability (repeatability).
It is very important to understand that even if you get reliable results, that doesn’t necessarily mean that they are also valid. Just as an example, take bathroom scale. Imagine that someone who weighs 200 pounds steps on it 10 times and get different readings each time. this means that the scale is not reliable. If this person consistently gets 150 – the scale is reliable, but not valid. However, if it shows 200 each time – then it is both reliable and valid. This is what meant when you hear the phrase: “Reliability doesn’t assume validity”.
So there you go: if you want you research findings to be important -make sure that you address the right question and that the results you get are reliable and valid. But beware, there is a lot more to reliability and validity than I’ve written. For example, you have to take in consideration confounding variables.
P.S. This blog entry is a little dry this week, but hopefully informative 🙂
October 14, 2011 at 12:55 pm (Uncategorized)
https://ira06.wordpress.com/2011/10/05/do-you-need-statistics-to-understand-your-data/#comments
http://statisticsbyrachel.wordpress.com/2011/10/13/correlation-negative-or-positive/#comment-17
http://ishanichakravorty.wordpress.com/2011/10/13/what-makes-research-findings-important/#comment-10
http://ellislee15.wordpress.com/2011/10/11/why-is-reliability-important/#comment-12
ira06 