Research Methodology


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Research Methodology

  1. 1. HOW?  RESEARCH METHODOLOGY  Research Design  Research Locale  Sources of Data  Sampling  Sample Profile  Sampling Process  Data Collection  Conditions  Instruments  Validity & Reliability  Analysis of Data  Proposed Budget  Project Management
  2. 2. DESIGN & PLANNING PHASE The Research Design  Refers to a scheme or plan of action for meeting the objectives of the study. The appropriateness of the research design depends largely on which method will help the investigator attain research objectives.  The purpose of the research design is to provide a plan of action for answering the research question.  The major concern within the blueprint or plan is to specify control mechanisms to be used in the study so that the answer to the question will be clear and valid.
  3. 3. Reliability & Validity  Reliability refers to the consistency, stability or dependability of data. A research method that will give the same results, even if conducted twice, is reliable; it is unreliable when, used the second time, the research yields different from those of the first time.
  4. 4. Reliability & Validity  Validity refers to data that are not just reliable but also true and accurate. In another sense, it refers to the extent to which an instrument is able to actually assess what it is supposed to measure.
  5. 5. Internal Validity The degree to which changes in the dependent variable (effect) can be attributed to the independent variable (cause) Concerns the validity of inferences that, given the existence of an empirical relationship, it is the independent variable, rather than other factors, that
  6. 6. Threats to Internal Validity  Bias or Selection Bias occurs when the study result is attributed to the experimental treatment when, in fact, the results may be due to pre-treatment differences between the subjects in the experimental and comparison group.
  7. 7. Threats to Internal Validity Example of Selection Bias  Women with fertility problem were more likely to be depressed than women who were mothers, it would be impossible to conclude that the two groups differed in depression because of differences in reproductive status; women in the two groups might have been different in terms of psychological well-being from the start.
  8. 8. Threats to Internal Validity  History occurs when the subjects are exposed to an event. Some events besides the experimental treatment occurs between the pre-treatment and post-treatment measurement of the dependent variable and this event influences the dependent variable.
  9. 9. Threats to Internal Validity Example of History  If the comparison group is different from the treatment group, then the characteristics of the members of the comparison group could lead them to have different intervening experiences.
  10. 10. Threats to Internal Validity  Maturation occurs when changes take place within the study subjects as a result of the passage of treatment and these changes may affect the study results.
  11. 11. Threats to Internal Validity Example of Maturation  Such processes include physical growth, emotional maturity, fatigue, and the like. For instance, if we wanted to evaluate the effects of a special sensori-motor development program for developmentally delayed children, we would have to consider that progress does occur in these children even without special assistance.
  12. 12. Threats to Internal Validity  Testing refers to the effects of taking a pretest on subjects’ performance on a posttest.  It has been documented in several studies, particularly in those dealing with opinions and attitudes, that the mere act of collecting data from people changes them.
  13. 13. Threats to Internal Validity Example of Testing  We administered to a group of students a questionnaire about their attitudes toward assisted suicide. At the end of instruction, we give them the same attitude measure and observe whether their attitudes have changed. The problem is that the first administration of the questionnaire might sensitize students resulting in attitude changes regardless of whether instruction follows.
  14. 14. Threats to Internal Validity  Instrumentation Change occurs when there is changes in measuring instruments or methods of measurement between two points of data collection.  Example: If we used one measure of stress at baseline and a revised measure at follow-up, any differences might reflect changes in the measuring tool rather than the effect of an independent variable.
  15. 15. Threats to Internal Validity  Mortality/Attrition is the drop-out rate because of the boredom syndrome.  Example: The most severely ill patients might drop out of an experimental condition because it is too demanding, or they might drop out of the comparison group because they see no personal advantage to remaining in the study.
  16. 16. External Validity The validity that inferences about observed relationships will hold over variations in persons, setting, time, or measures of the outcomes Concerns the generalizability of causal inferences, and this is a critical concern for research that aims to yield evidence for evidence-based nursing practice
  17. 17. External Validity Threats  Hawthorne Effect occurs when the participants respond in a certain manner because they are aware that they are involved in a research study.
  18. 18. External Validity Threats  Experimental Effect wherein the researcher’s behavior influences the subjects’ behavior in a way that is not intended by the researcher.
  19. 19. External Validity Threats  Pretest Effect occurs when the subjects’ responses to the experimental treatment are influenced by the pretest.
  20. 20. Controlling Extraneous or Confounding Variables Participant characteristics almost always need to be controlled for quantitative findings to be interpretable. This are ways of controlling confounding subject characteristics to rule out rival explanations for cause & effect relationships. 1. Removing the variable 2. Matching cases 2. Balancing cases 4. Analysis of Variance (ANOVA) 5. Randomization
  21. 21. “The researcher must examine all possible threats to validity and eliminate them, recognize their influence when they are inevitable.”
  22. 22. Experimental Design  In an experiment (or randomized, controlled trial, RCT), researchers are active agents, not passive observers.  The controlled experiment is considered by many to be the gold standard for yielding reliable evidence about causes and effects.  A true experimental or RCT design is characterized by the following properties:  Manipulation  Control  Randomization
  23. 23. Experimental Design  Manipulation – the experimenter does something to at least some subjects – that is, there is some type of intervention.  Control – the experimenter introduces controls over the experimental situation, including devising a good approximation of a counterfactual – usually a control group that does not receive the intervention.  Randomization – the experimenter assigns subjects to a control or experimental condition on a random basis.
  24. 24. Experimental Designs Name of Design Pre-intervention Data? Features Posttest-only (after only) No One data collection point after the intervention; not appropriate for measuring change Pretest-posttest (before & aftrer) Yes Data collection before and after the intervention; appropriate for measuring change; can determine differences between groups (experimental) and change within groups (quasi- experimental) Factorial Optional Experimental manipulation of more than one independent variable; permits a test of main effects for each manipulated variable and interaction effects for combinations of manipulated
  25. 25. Pre-Experimental Design  A type of experimental design in which the researcher has little control over the research situation  Types  One-shot Case Study – a single group or subject is observed after a treatment to determine the effect of the treatment (clinical papers or clinical case studies)  One-group Pretest-Posttest Design – compares one group of subjects before and after an experimental treatment.
  26. 26. Quasi-Experimental Design  Quasi-experiments, like true experiments, involve an intervention.  However, quasi-experimental designs lack randomization, the signature of a true experiment  The signature of a quasi-experimental design then, is an intervention in the absence of randomization
  27. 27. Quasi-Experimental Designs  Nonequivalent control group design – the most frequently used quasi-experimental design, which involves an experimental treatment and two groups of subjects observed before and after its implementation.  Example: Gates, Fitzwater, and Succop (2005) evaluated the effectiveness of a violence-prevention intervention for nursing assistants working in long-term care. The intervention was implemented in three nursing homes, and three other nursing homes served as the comparison. Data on anxiety, self-efficacy, and violence- prevention skills were collected before and after the intervention.
  28. 28. Quasi-Experimental Designs  Time series design – the researchers periodically observe subjects and administer an experimental treatment between the observations.  Example: Edwards and Beck (2002) used a powerful time series design to assess the effect of animal-assisted therapy (aquariums) on the nutritional intake of individuals with Alzheimer’s disease. Weight (one of the outcomes) was measured on the first of each months for 3 months before the intervention and for 4 months after it in a sample of residents in specialized units in 3 facilities. The researchers found that, over this 7-month period, weight declined in the 3 months.
  29. 29. Non-Experimental Design  A. Descriptive Research. The purpose is to observe, describe and document aspects of a situation as it naturally occurs.  Exploratory Design – a preliminary research project to ascertain some procedures or possibility for future research.  Survey Design – collecting data from a group, usually be questionnaire or interview, to learn some of the subjects’ characteristics.
  30. 30. Non-Experimental Design  B. Correlational Research  When researchers study the effect of a potential cause that they cannot manipulate, they use designs that examine relationships between variables – often called correlational designs. A correlation is an interrelationship or association between two variables, that is, a tendency for variation in one variable to be related to variation in another.
  31. 31. Non-Experimental Design  B. Correlational Research  Example: In human adults, height and weight are correlated because there is a tendency for taller people to weigh more than shorter people.
  32. 32. DESIGN & PLANNING PHASE The Research Locale  The setting of the Study  Types  Laboratory Studies – designed to be more highly controlled in relation to both the environment in which the study is conducted & the control of extraneous & intervening variables  Field Studies
  33. 33. Laboratory Studies  Example: Physiological laboratory experiments, chemistry & physics experiments, psychological and microbiological experiments are laboratory experiment, designed to control the possibility of extraneous variables influencing the effect of the independent variable on the dependent variable. In the laboratory setting, it is possible to control environmental variables, such as temperature, humidity, light and sound, as well as physiological variables such as nutrition and hydration of the subjects during the experiment in clinical researches.
  34. 34. Field Studies  Simply means they occur somewhere other than in a controlled laboratory setting. They occur in natural settings and use a variety of methods such as field experiments, participant observations in villages or hospital wards, interviews in home or office, questionnaire sent to research subjects and anything at all that does not occur in a controlled laboratory setting.
  35. 35. Timing of Data Collection  Prospective or Longitudinal Studies – looking at events that are underway or expected to occur in the future. This is designed to follow the subjects for a long period of time.  Retrospective, Ex Post Facto or Historical Studies – focusing on events that have occurred in the past.
  36. 36. Timing of Data Collection  Retrospective – for cause-effect study in which the effect is known.  Example: Lung Cancer studies  Ex Post Facto – a phenomenon that occurs in the present is thought to have a cause that can be found in the past.  Example: Alcoholism, Obesity and Diabetes  Historical – descriptive studies that ask people to recall events, other people and memories of the past, or refer to written documents and artifacts to reconstruct past events.
  37. 37. DESIGN & PLANNING PHASE The Sampling Design  Sampling – is the process of selecting a portion of the population to represent the entire population so that inferences about the population can be made.  A sample is a subset of population elements  A stratum is a mutually exclusive segment of a population  An element is the most basic unit about which information is collected  In nursing research, the elements are usually humans
  38. 38. Basic Sampling Concepts  Population – the entire aggregation of cases in which a researcher is interested  If you want to study American nurses with doctoral degrees, the population could be defined as all U.S. citizens who are RNs and who have acquired a doctoral- level degree.  The criteria that specify population characteristics are referred to as eligibility criteria or inclusion criteria. Sometimes, a population is defined in terms of characteristics that people must not possess (stipulating the exclusion criteria).  For example, the population may be defined to exclude
  39. 39. Study Groups  Experimental Group  First group that receives treatment  Control Group  Second group without treatment that will serve as the comparison group
  40. 40. To whom do you wish to generalize the findings? The Target Population To which population do you have access? The Accessible Population Through what resource can you access them? The Sampling Frame Who is participating in your study? The Sample
  41. 41. Why do sampling? Reduce cost Greater speed Greater scope Greater accuracy
  42. 42. Types of Sampling  Probability Sampling – the use of random sampling procedures to select a sample from elements of a population.  Non-Probability Sampling – a sampling process in which a sample is selected from elements or members of a population through non-random methods.
  43. 43. Methods of Probability Sampling  Simple Random Sampling – the most basic probability sampling design wherein there is the establishment of a sampling frame (the technical name for the list of elements from which the sample will be chosen).  Example: Boyington, Jones, & Wilson (2006) explored the presence of nursing on hospital websites. The sampling frame was the US News and World Report list of the top 203 American hospitals in 2003. Fifty hospitals were selected: all 17 from the magazine’s “Honor Roll” and 33 randomly selected from the remaining hospitals via an electronic number generator.
  44. 44. Methods of Probability Sampling  Stratified Random Sampling – wherein the population is first divided into two or more strata. The aim of this sampling is to enhance representativeness. This design subdivides the population into homogeneous subsets from which an appropriate number of elements are selected at random  Example: Stewart & colleagues (2005) studied nursing practice issues in rural & remote areas of Canada using a mailed survey. Questionnaires were sent to a stratified random sample of rural nurses throughout Canada, using province as the stratifying variable.
  45. 45. Methods of Probability Sampling  Cluster Sampling – there is a successive random sampling units. Because of the successive stages in cluster sampling, this approach is often called multistage sampling. The resulting design can be described in terms of the number of stages (e.g. three- stage cluster sampling)  Example: Thato & colleagues (2003) studied predictors of condom use among adolescent Thai vocational students. In the first stage, the researchers randomly selected 8 private vocational schools in Bangkok, and then randomly selected students from the schools. A total of 425 student aged 18-22 were sampled.
  46. 46. Methods of Probability Sampling  Systematic Sampling – involves the selection of every kth case from a list, such as every 10th person on a patient list or every 100th person in a directory of ANA members.  Application: The desired sample size is established at some number (n). The size of the population must be known or estimated (N). By dividing N by n, the sampling interval width (k) is established. The sampling interval is the standard distance between elements chosen for the sample.
  47. 47. Methods of Non-Probability Sampling  Convenience Sampling – it is sometimes called an accidental sampling, entails using the most conveniently available people as study participants.  A faculty member who distributes questionnaires to nursing students in a class is using a convenience sample. The nurse who conducts an observational study of women delivering twins at the local hospital is also relying on a convenience sample.
  48. 48. Methods of Non-Probability Sampling  Snowball Sampling – also called network sampling or chain sampling is a variant of convenience sampling. With this approach, early sample members (called seeds) are asked to refer other people who meet the eligibility criteria. This sampling method is often used when the population is people with characteristics who might otherwise be difficult to identify.  Snowballing begins with a few eligible study participants and then continues on the basis of participant referrals.
  49. 49. Methods of Non-Probability Sampling  Quota Sampling – is one in which the researcher identifies population strata and determines how many participants are needed from each stratum. By using information about population characteristics, researchers can ensure that diverse segments are represented in the sample, preferably in the proportion in which they occur in the population.  For example: Pieper (2006) used quota sampling in their study of chronic venous insufficiency (CVI) in HIV- positive persons and the extent to which neuropathy increased the risk for CVI. They stratified their sample on the basis of whether or not the person had a history
  50. 50. Methods of Non-Probability Sampling  Purposive Sampling – or judgmental sampling, is based on the belief that researchers’ knowledge about the population can be used to hand-pick sample members. Researchers might decide purposely to select subjects who are judged to be typical of the population or particularly knowledgeable about the issues under study.  For example: Gagnon & Grenier (2004) conducted a study to identify, validate, and rank-order quality care indicators relating to empowerment for patients with a chronic complex illness. One phase of their study involved gathering quantitative information from a
  51. 51. Data Collection Methods  Interview – a process of asking questions to subjects verbally in order to collect data.  Approaches:  1. Self-report – questioning people directly  2. Unstructured interviews – typically conversational in nature  3. Focused Interviews or semi-structured interview – used when you want to be sure that a given set of topics is covered in interviews with respondents (FGD)
  52. 52. Data Collection Methods  Interview – a process of asking questions to subjects verbally in order to collect data.  Approaches:  4. Life histories – narrative self-disclosure about a person’s life experiences  5. Critical Incidents Technique – method of gathering information about people’s behaviors by examining specific incidents relating to the behavior under investigation
  53. 53. Data Collection Methods  Questionnaire – a written question-and-answer sheet which provides data about subject’s attitudes, beliefs, habits, and socioeconomic background  Question forms:  Open-ended questions  Close-ended questions or fixed alternative
  54. 54. Data Collection Methods  Observation – is a method of collecting descriptive behavioral data and is extremely useful in health researches because one can observe behavior as it occurs. It is systematically planned and recorded.  Types:  Participant observer  Non-participant observer
  55. 55. How to Write Questionnaire Items  1. Define or qualify terms that could easily be misinterpreted.  What is the value of your house? X  What is the present market value of your house?  How much does your house cost?  2. Be careful in using descriptive adjectives & adverbs that have no agreed upon meaning.  Frequently, occasionally, rarely  Times per week or times per month
  56. 56. How to Write Questionnaire Items  3. Beware of double negatives. Underline negatives for clarity.  Are you opposed to not requiring students to take shower after gym class?  Federal aid should not be granted to those states in which education is not equal regardless of race, creed or color.  4. Be careful with inadequate alternatives.  Married? YES NO
  57. 57. How to Write Questionnaire Items  5. Avoid double-barreled question.  Do you believe that gifted students should be placed in separate groups for instructional purposes and assigned to special schools?  6. Underline a word if you wish to indicate special emphasis.  Should all schools offer a modern foreign language.  7. When asking for ratings/comparisons, a point of reference is necessary.  How would you rate the clinical instructor’s teaching? Superior _____ Average _____ Below Average _____
  58. 58. How to Write Questionnaire Items  8. Avoid unwanted assumptions.  Are you satisfied with the salary raise you receive last year?  Do you feel that you benefited from the spankings that you received as a child?  9. Phrase questions so that they are appropriate for all respondents.  What is your monthly teaching salary?  10. Design questions that will give complete response.  Do you read the Philippine Star? YES NO
  59. 59. How to Write Questionnaire Items  11. Provide for systematic quantification of responses. - solutions -  Ask respondents to rank  Arrange in order of preference  Allow specific number of responses  12. Consider the possibility of classifying the responses yourself, rather than having the respondent choose categories.
  60. 60. EMPIRICAL & ANALYTIC PHASE TRIANGULATION  A compatibility procedure designed to reconcile the two major methodologies by eclectically using elements from each of the major methodologies as these contribute to the solution of the major problem. Qualitative Research (Data: principally verbal) Quantitative Research (Data: principally numerical) Descriptive Studies Historical Studies Case Studies Ethnographic Studies Survey Studies Experimental Studies Quasi-experimental Studies Statistical Analytical Studies
  61. 61. Kinds of Triangulation  Methodological Triangulation  The use of two or more methods of data collection procedures within a single study.  Data Triangulation  Attempts to gather observations through the use of a variety of sampling strategies to ensure that a theory is tested in more than one way.
  62. 62. Kinds of Triangulation  Theoretical Triangulation  The use of several frames of reference or perspectives in the analysis of the same set of data.  Investigator Triangulation  The use of multiple observers, coders, interviewers, and/or analysis in a particular study.