Third Installment is here, enjoy!

Psychology, being a science, is based on research and experimentation. It is important to note that our own intuition cannot be relied on completely, because although efficient, it can lead us away from the truth. One example of this is seen in hindsight bias, or the tendency to believe that one knew the result all along after hearing the results. As a science Psychology will be used to predict what will happen in the future. Understanding research methods is essential in my sciences, as is the case with Psychology. Two key types of research present themselves, applied research and basic research. Applied research has practical applications while basic research is for the knowledge gained to answer questions rather than immediately satisfying the needs of people. Even a connection with math can be seen within Psychology in Statistics, where one must learn to use the data from research to form a mathematical conclusion. Because this is the case there will be several statistics terms in the future that will be seen within the lessons as well.

Most psychological research is guided by an hypothesis (an “if… then” statement). Although it does not link causality between two things directly a hypothesis is used to express the relationship between two variables. Variables are factors within an experiment than can possibly be changed. There are two key variables, dependent variables and independent variables. Just like the name suggests, the dependent variable is directly changed by the independent variable (a change in the independent will result in a change in the dependent). For research an independent variable is manipulated. To see how this works, here is an example:

If he stays awake for a long period of time, then he will have less balance while walking.

In this case, we see an if then statement which clearly shows that this is an hypothesis. Furthermore, the independent variable is the length of time that the person in question is awake and the dependent variable is the amount of balance as the statement clearly shows that the balance is dependent on how much sleep the person had. Generally, hypotheses are created to run experiments to prove theories. Theories are used to explain phenomena, but need to be backed up by evidence in the form of experiments. Thus, the theory must be tested with hypotheses.

Operational definitions are key in running an experiment. The variables to be studied are often operationalized. What this means is that the measurement procedure of the variable is explained. By having reliable operational definitions the research is considered to be more valid and more reliable as the researcher is using correct methods to measure the key variables.

Research needs to be both valid and reliable. Yes, there is a key difference between the two of them. If research is valid, then the results and the experiment itself measures what the researcher set out to measure. If the data is reliable then it can be replicated by other scientists using the same procedure.

In order to test or even create a hypothesis there needs to be subjects that the study can use. If living, the individuals are termed participants. Otherwise, it is more common to use subjects. However, it is not wrong to call living individuals subjects as well. These subjects have to be selected which is done through sampling (literally the choosing of participants). A population is first selected. This is just common sense, if you want to test the effects of a certain type of collar on different dogs then your population will be from all the dogs that you are randomly selecting from (E.g. All Dogs at the Nashville Petsmart or All Dogs in the Memphis region). However, the sample needs to be representative of the entire population. For example, if you were to set your population as all the dogs in the world, yet only choose dogs from your home state, then you would not be getting a sample representative of your entire population and your research is not applicable to the entire population. Usually the best way is to randomly select individuals from the population. This increases the likelihood of choosing a sample that is representative of the population and thus giving you data that can be used to make accurate conclusions on the entire population. Randomization is a little more difficult than one might assume. It requires getting a population without bias, meaning that human selection is not viable. Instead the common practice is to label each individual within a population with a number then using a computer program or a random number table to choose. I personally use my Ti-89 for this task. This also shows why it may be more practical to do an experiment with results on a smaller population as to avoid having to label an enormous population. However with the advent of telephone books and the like, the ability to label entire states with only a few mistakes has become possible. This also shows why one must be wary of polls claiming to have results that depict the view of an entire nation as the actual poll data might not have been based on a random sample, but by whoever CHOSE to respond, an important example of bias that will be discussed at a later date. There are other simple ways of using randomization which are seen in our daily lives: tossing a die, tossing a coin, and pulling names out of a hat.

You might ask, why sample rather than just use the entire population, since that would procure the most accurate answer. However, the expenditure and physical restraints of accomplishing such a feat would be infeasible. It is much more practical to use a sample. A census on the other hand is the term used when all of the subjects in a population are tested.

An example why we use samples is if I were to try to find all the heights of males in Tennessee, if I were to gather them up, by the time I got through 1/3 of them, the heights have all changed because of the time taken. Using samples is quicker and thus a more accurate depiction of the true mean.

Samples can also be stratified, randomly selecting after deciding groups. In case that was not clear, if I were to be studying the effects of a certain medicine on dogs. I might pick a random number of each type of dog in order to get applicable results as different dogs might have different reactions. This is used in matters of race, ethnicity, religion, and many other divisions that could cause differences based on what was being tested.

Experiments are usually divided into one of two categories, laboratory experiments and field experiments. Laboratory experiments are those that take place within an actual scientific lab while field experiments are conducted elsewhere. Laboratory experiments are mainly used because of the fact that they are able to be controlled more since the scientist can prevent most variables from interfering with the results of the experiments and just let the independent variable in question be the cause for the change in the dependent. Field experiments are useful for the fact that they are more realistic when applying results to actual human beings as we are continuously facing many unknown variables every second.

Experiments are primarily used for research within the science of Psychology because of the fact that they directly relate the independent variable to the direct and account for any variables that may be of influence. One key variable that may change the outcome is termed a confounding variable. This is any variable that is not picked up by the control that has an effect on the dependent. Thus, with confounding variables we might think that the independent is the only cause when in actually there are other variables.

The control of the experiment is the base of comparison of results. It follows through the same procedure as the experiment however even the independent variable is kept constant. This allows to compare whether a change would have occurred even without the presence of the independent. It is essential in field studies as it is the most accurate way to account for all but one variable.

Assignment is the actual putting of subjects into their respective groups. As stated before, this is done through randomization, the key term here being random assignment. In random assignment the groups, whether they be experimental or control get subjects who have an equal chance of going into any of the groups. This just serves to limit the amount of variables that might affect subjects based on what group they are in by making it random.

Experimenter bias is a key topic that must be taken seriously within every experiment. When the experimenter knows which group is the control and which is the experimental, then the experimenter might unknowingly treat different groups differently and therefore invalidate the data. For example, if there were two patients and one of the them was taking an actual medicine that had been prescribed for them and the other was just taking a sugar pill, anything from body movements to types of questions that the experimenter asks might increase the chance of the hypothesis proving right as the experimenter expects one to be different and might take the smallest signals to mean something more significant. The same deal works with the patient too. If the patient knows that they are on a sugar pill, then rarely will it be the case that the pill actually works. What we expect to happen has an influence on how we perceive the results occurring. In order to prevent the type of errors stated before, the double-blind procedure is used. This is an experiment in which the experimenter and the subject do not know whether they have the actual treatment or fake. This stops the bias and thus gives more valid data.

There is a technical term for the actual fake, the placebo. The placebo usually takes the form of a sugar pill if used in medicine and is debated on whether there are actual effects upon people using the placebo because of the psychology of believing that they are receiving the actual medicine itself. It is often used on patients which the doctor believes to be affected by something for which there is no cure. This is not to say that the actual sickness itself is psychosomatic, but rather there might be effects on actual sickness based on thought, which is interesting. However, the placebo is used as a control, so from a scientific standpoint it does not influence, meaning that the numerical results of the placebo are subtracted from the results of the experimental to find how much the experimental truly affects.

I hope you enjoyed another Psychology thread.

The continuation of this one… #4 will come out soon.