Research Methodology Hamad Yaseen, PhD Student s Project 470 MLS Department, FAHS
Key Concepts and Issues Time in research Variables Types of relationships Hypotheses Types of data Structure or research Deduction and induction Ethics Validity and Reliability
Time in Research cross-sectional vs. longitudinal repeated measures time series
Variables Variable any observation that can take on different values Attribute a specific value on a variable
Examples Variable Attribute age
Examples Variable age Attribute 18, 19, 20, etc...
Examples Variable Attribute Gender or sex
Examples Variable Gender or sex Attribute Male, female
Examples Variable Attribute satisfaction
Examples Variable satisfaction Attribute 1 = very satisfied 2 = satisfied 3= somewhat satisfied 4 = not satisfied 5 = not satisfied at all
Types of Variables Independent variable (IV) what you (or nature) manipulates in some way Dependent variable (DV) what you presume to be influenced by the IV
Examples IV DV health status attitude social support exercise participation intervention
The purpose of the study was to Test whether the Fair Play for Sport curriculum is effective in promoting moral development in youth Examine the relationship between age and VO2max. Test whether there are gender differences the value placed on sport participation IV, DV?
Types of Relationships Correlation vs. Causal relationships variables perform in a synchronized manner one variable causes the other variable correlation does not imply causation! (it s necessary but not sufficient)
Types of Relationships Patterns of relationships: no relationship positive relationship negative relationship curvilinear relationship
+ + fitness exercise intensity fitness - - - resting HR + + + - performance - - HR + - vocabulary + - alertness +
Hypotheses Hypothesis a specific statement of prediction Types of hypotheses Alternative vs. Null One-tailed vs. Two-tailed
Hypotheses Alternative hypothesis (HA) The alternative hypothesis, denoted by H 1 or H A, is the hypothesis that sample observations are influenced by some non-random cause. Null hypothesis (HO) The null hypothesis, denoted by H 0, is usually the hypothesis that sample observations result purely from chance.
Hypotheses Hypothesis H A There is a relationship between age and exercise participation there is a relationship H O there is not a relationship This is a two-tailed hypothesis as no direction is predicted
Hypotheses Hypothesis H A An incentive program will increase exercise participation Participation will increase H O Participation will not increase or will decrease this is a One-tailed hypothesis as a specific direction is predicted
Types of Data Quantitative vs. Qualitative Quantitative : Data that can be measured and written down with numbers. Qualitative : information that can't actually be measured. A color for example
Qualitative data: blue/green color, gold frame smells old and musty texture shows brush strokes of oil paint peaceful scene of the country masterful brush strokes Quantitative data: picture is 10" by 14" with frame 14" by 18" weighs 8.5 pounds surface area of painting is 140 sq. in. cost $300
Structure of Research The "hourglass" notion of research begin with broad questions narrow down, focus in operationalize OBSERVE analyze data reach conclusions generalize back to questions
Deduction and Induction Deduction Induction
Ethics in Research Balance between Protecting Participants vs. Quest for knowledge Informed consent/assent confidentiality and anonymity justification of procedures right to services
Validity and reliability They are fundamental cornerstones of the scientific method. Together, they are at the core of what is accepted as scientific proof.
Reliability Any significant results must be more than a one-off finding and be inherently repeatable. Other researchers must be able to perform exactly the same experiment, under the same conditions and generate the same results. This will reinforce the findings and ensure that the wider scientific community will accept the hypothesis.
Validity Validity encompasses the entire experimental concept and establishes whether the results obtained meet all of the requirements of the scientific research method. For example, there must have been randomization of the sample groups and appropriate care and diligence shown in the allocation of controls.
Types of Validity Internal validity: dictates how an experimental design is structured and encompasses all of the steps of the scientific research method. External validity: is the process of examining the results and questioning whether there are any other possible causal relationships.
Validity and Reliability One of my favorite metaphors for the relationship between reliability is that of the target. Think of the center of the target as the concept that you are trying to measure. Imagine that for each person you are measuring, you are taking a shot at the target. If you measure the concept perfectly for a person, you are hitting the center of the target. If you don't, you are missing the center. The more you are off for that person, the further you are from the center.
In the first one, you are hitting the target consistently, but you are missing the center of the target. That is, you are consistently and systematically measuring the wrong value for all respondents. This measure is reliable, but no valid (that is, it's consistent but wrong).
The second, shows hits that are randomly spread across the target. You seldom hit the center of the target but, on average, you are getting the right answer for the group (but not very well for individuals). In this case, you get a valid group estimate, but you are inconsistent. Here, you can clearly see that reliability is directly related to the variability of your measure.
The third scenario shows a case where your hits are spread across the target and you are consistently missing the center. Your measure in this case is neither reliable nor valid.
Finally, we see the "Robin Hood" scenario -- you consistently hit the center of the target. Your measure is both reliable and valid (I bet you never thought of Robin Hood in those terms before).
For next lecture Eliminating other potential causal relationships, by using controls and duplicate samples, is the best way to ensure that your results stand up to rigorous questioning and are statistically significant.