QUANTITATIVE_RESEARCH_METHODS_AND_DESIGN.pptx

post graduate students preparing to write thesis

1. QUANTITATIVE RESEARCH CHARACTERISTICS AND TYPES

2. Before we begin…

3. General Types of Educational Research Descriptive — survey, historical, content analysis, qualitative Associational — correlational, causal-comparative Intervention — experimental, quasi-experimental, action research (sort of) This division is irrespective of the quan / qual divide

4. QUANTITATIVE RESEARCH Quantitative research is the collection and analysis of numerical data to describe, explain, predict, or control phenomena of interest.

5. Quantitative Research This research operates on the philosophical belief or assumption that We inhabit a relatively stable, uniform, and coherent world that we can measure, understand, and generalize about . This view, adopted from the natural sciences, implies that the world and the laws that govern it are somewhat predictable and can be understood by scientific research and examination . In this quantitative perspective, claims about the world are not considered meaningful unless they can be verified through direct observation.

6. THE QUANTITATIVE PROCESS At the outset of a study , quantitative researchers state the hypotheses to be examined and specify the research procedures that will be used to carry out the study. They also maintain control over contextual factors that may interfere with the data collection and identify a sample of participants large enough to provide statistically meaningful data.

7. S ampling The first step in selecting a sample is to define the population to which one wishes to generalize the results of a study The sample is drawn from the population -Data is collected from the sample -Statistics are used to determine how likely the sample results are reflective of the population

8. A number of different strategies can be used to select a sample. Each of the strategies has strengths and weaknesses. There are times when the research results from the sample cannot be applied to the population because threats to external validity exist with the study. The most important aspect of sampling is that the sample represent the population.

9. SIMPLE RANDOM SAMPLING Each subject in the population has an equal chance of being selected regardless of what other subjects have or will be selected. A random number table or computer program is often employed to generate a list of random numbers to use. A simple procedure is to place the names from the population is a hat and draw out the number of names one wishes to use for a sample.

10. STRATIFIED RANDOM SAMPLING – A representative number of subjects from various subgroups is randomly selected . The subgroups are called strata and the sample drawn from each strata is proportionate to the propsrtions of the strata in the sample E.g. if a population has 100 teachers (50 elementary, 30 secondary and 2 tertiary), then in a sample of 10, 5 should be from the elementary stratum, 3 from secondary stratum and 2 from the tertiary stratum

11. CLUSTER SAMPLING In cluster sampling, intact groups, not individuals, are randomly selected. Any location within which we find an intact group of population members with similar characteristics is a cluster. Examples of clusters are classrooms, schools, city blocks, hospitals, and department stores.

12. When is it used? Cluster sampling is done when the researcher is unable to obtain a list of all members of the population. It is also convenient when the population is very large or spread over a wide geographic area. For example, instead of randomly selecting from all fifth graders in a large city, you could randomly select fifth-grade classrooms and include all the students in each classroom. Cluster sampling usually involves less time and expense and is generally more convenient

13. An extension of the Cluster Random Sample is the TWO-STAGE CLUSTERE RANDOM SAMPLE. In this situation, the clusters (classes in our example) are randomly selected and then students within those clusters are randomly selected.

14. CONVENIENCE SAMPLING Subjects are selected because they are easily accessible. This is one of the weakest sampling procedures. An example might be surveying students in one’s class. Generalization to a population can seldom be made with this procedure.

15. PURPOSIVE SAMPLING – Subjects are selected because of some characteristic . Also referred to as judgment sampling and is the process of selecting a sample that is believed to be representative of a given population. Sample selection is based on the researcher’s knowledge and experience of the group to be sampled using clear criteria to guide the process.

16. SYSTEMATIC SAMPLING – Systematic sampling is an easier procedure than random sampling when you have a large population and the names of the targeted population are available. Systematic sampling involves selection of every nth (i.e., 5th) subject in the population to be in the sample. Suppose you had a list of 10,000 voters in your area and you wished to sample 400 voters for research We divide the number in the population (10,000) by the size of the sample we wish to use (400) and we get the interval we need to use when selecting subjects (25). In order to select 400 subjects, we need to select every 25th person on the list.

17. Ethics of Research Researchers are bound by a code of ethics that includes the following protections for subjects Protected from physical or psychological harm (including loss of dignity, loss of autonomy, and loss of self-esteem) Protection of privacy and confidentiality Protection against unjustifiable deception The subject must give voluntary informed consent to participate in research. Guardians must give consent for minors to participate. In addition to guardian consent, minors over age 7 (the age may vary) must also give their consent to participate.

18. Informed Consent All research participants must give their permission to be part of a study and they must be given pertinent information to make an “informed” consent to participate. This means you have provided your research participants with everything they need to know about the study to make an “ informed” decision about participating in your research. Researchers must obtain a subject’s (and parents’ if the subject is a minor) permission before interacting with the subject or if the subject is the focus of the study. Generally , this permission is given in writing ; however, there are cases where the research participant’s completion of a task (such as a survey) constitutes giving informed consent.

19. INSTRUMENT Instrument is the generic term that researchers use for a measurement device (survey, test, questionnaire, etc.). To help distinguish between instrument and instrumentation, consider that the instrument is the device and instrumentation is the course of action (the process of developing, testing, and using the device).

20. Instruments fall into two broad categories, researcher-completed and subject-completed , distinguished by those instruments that researchers administer versus those that are completed by participants. Researchers chose which type of instrument, or instruments, to use based on the research question.

21. Usability Usability refers to the ease with which an instrument can be administered, interpreted by the participant, and scored/interpreted by the researcher. Example usability problems include: Students are asked to rate a lesson immediately after class, but there are only a few minutes before the next class begins ( problem with administration). Students are asked to keep self-checklists of their after school activities, but the directions are complicated and the item descriptions confusing ( problem with interpretation ).

22. VALIDITY Validity is the extent to which an instrument measures what it is supposed to measure and performs as it is designed to perform. It is rare, if nearly impossible, that an instrument be 100% valid, so validity is generally measured in degrees . T here are numerous statistical tests and measures to assess the validity of quantitative instruments, which generally involves pilot testing.

23. External validity is the extent to which the results of a study can be generalized from a sample to a population. Establishing eternal validity for an instrument, depends directly on sampling. An instrument that is externally valid helps obtain population generalizability, or the degree to which a sample represents the population. Content validity refers to the appropriateness of the content of an instrument. In other words, do the measures (questions, observation logs, etc.) accurately assess what you want to know ? This is particularly important with achievement tests.

24. RELIABILITY A test is reliable to the extent that whatever it measures, it measures it consistently. Does the instrument consistently measure what it is intended to measure ? There are 4 estimators to gauge reliability: Inter-Rater/Observer Reliability : The degree to which different raters/observers give consistent answers or estimates. Test-Retest Reliability : The consistency of a measure evaluated over time. Parallel-Forms Reliability : The reliability of two tests constructed the same way, from the same content. Internal Consistency Reliability : The consistency of results across items.

25. NOW LET’S STUDY DIFFERENT QUANTITATIVE METHODS

26. Descriptive Research/Survey Research

27. Definition: Descriptive research involves collecting data to test hypotheses or to answer questions about people’s opinions on some topic or issue. Survey is in an instrument to collect data that describes one or more characteristics of a specific population. Survey data are collected by asking members of population a set of questions via a Questionnaire or an Interview. Descriptive research requires attention to the selection of an adequate sample and an appropriate instrument.

28. Rsearch Design

29. Conducting Descriptive Research It requires a collection of standardized quantifiable information from all members of a population or sample .

30. Constructing The Q uestionnaire A Questionnaire should be attractive ,brief and easy to respond . No item should be included that does not directly relate to the objective of the study. Structured or closed ended should be used. Common structured items used in questionnaire are scaled items , ranked items and checklists. In an Unstructured item format respondents have complete freedom of response but often are difficult to analyze and interpret.

31. Each question should focus on :

32. Pilot Testing The questionnaire should be tested by respondent who are similar to those in the sample of the study. Pilot testing provides information about deficiencies as well as suggestions for improvement. Omission or unclear or irrelevant items should be revised. Pilot testing or review by colleagues can provide a measure of content validity.

33. Cover Letter Every mailed or Emailed questionnaire must be accompanied by cover letter that explains What is being asked and why is being asked. The cover letter should be brief, neat and addressed to the specific individual.

34. Selecting Participants Participants should be selected using an appropriate sampling technique. The researcher should ensure that the identified participants should have the desired information and must be willing to share it .

35. Distributing the Questionnaire Questionnaires are usually distributed via one of five approaches:

36. Tabulating Questionnaire responses The simplest way to present the result is to indicate the percentage of respondents who selected each alternative for each item. However analyzing summed items clusters-groups items focused on the same issues is more useful , meaningful and reliable. Comparison can be investigated in the collected data by examining the responses of different sub-groups in the sample (male/female).

37. Results

38. CORRELATIONAL RESEARCH

39. Correlational Research Correlational research involves collecting data to determine whether and to what degree a relation exists between two or more quantitative variables. The degree of relation is expressed as a correlation coefficient.

40. Purpose The purpose of a correlational research may be to determine relations among variables or to use these relations to make predictions.

41. Types of correlations Positive correlation Positive correlation bet ween two variables is when an increase in one variable leads to an increase in the other and a decrease in one leads to a decrease in the other. For example, the amount of money that a person possesses might correlate positively with the number of cars he owns.

42. Negative correlation Negative correlation is when an increase in one variable leads to a decrease in another and vice versa. For example, the level of education might correlate negatively with crime. This means if by some way the education level is improved in a country, it can lead to lower crime. Note that this doesn't mean that a lack of education causes crime. It could be, for example, that both lack of education and crime have a common reason: poverty.

43. No correlation Two variables are uncorrelated when a change in one doesn't lead to a change in the other and vice versa. For example, among millionaires, happiness is found to be uncorrelated to money. This means an increase in money doesn't lead to happiness

44. Correlation Coefficient Measure showing the degree of relation between two variables.

45. Methods of Studying Correlation Scatter Diagram Method Karl Pearson’s Coefficient of Correlation Spearman rank correlation

46. Scatter Diagram Method Scatter Diagram is a graph of observed plotted points where each points represents the values of X & Y as a coordinate. It portrays the relationship between these two variables graphically

47. A perfect positive correlation Height Weight Height of A Weight of A Height of B Weight of B A linear relationship

48. High Degree of positive correlation Positive relationship Height Weight r = +.80

49. Degree of correlation Perfect Negative Correlation Exam score TV watching per week r = -1.0

50. Degree of correlation No Correlation (horizontal line) Height IQ r = 0.0

51. Advantages of Scatter Diagram Simple & Non Mathematical method Not influenced by the size of extreme item First step in investing the relationship between two variables

52. Disadvantage of scatter diagram Can not adopt the an exact degree of correlation

53. Karl Pearson’s Coefficient of Correlation It is also called simple correlation coefficient. It measures the nature and strength between two variables of the quantitative type.

54. The sign of r denotes the nature of association while the value of r denotes the strength of association.

55. If the sign is + ve this means the relation is direct (an increase in one variable is associated with an increase in the other variable and a decrease in one variable is associated with a decrease in the other variable). While if the sign is - ve this means an inverse or indirect relationship (which means an increase in one variable is associated with a decrease in the other).

56. The value of r ranges between ( -1) and ( +1) The value of r denotes the strength of the association as illustrated by the following diagram. -1 1 -0.25 -0.75 0.75 0.25 strong strong intermediate intermediate weak weak no relation perfect negative correlation perfect positive correlation Direct indirect

57. Continue… If r = Zero this means no association or correlation between the two variables. If 0 < r < 0.25 = weak correlation. If 0.25 ≤ r < 0.75 = intermediate correlation. If 0.75 ≤ r < 1 = strong correlation. If r = l = perfect correlation. How to compute simple correlation co-efficient

58. Advantages of Pearson’s Coefficient It summarizes in one value, the degree of correlation & direction of correlation also.

59. Limitation of Pearson’s Coefficient Always assume linear relationship Interpreting the value of r is difficult. Value of Correlation Coefficient is affected by the extreme values. Time consuming methods

60. Spearman’s Rank Coefficient of Correlation When statistical series in which the variables under study are not capable of quantitative measurement but can be arranged in serial order, in such situation pearson’s correlation coefficient can not be used in such case Spearman Rank correlation can be used. R = 1- (6 ∑ D 2 ) / N (N 2 – 1) R = Rank correlation coefficient D = Difference of rank between paired item in two series. N = Total number of observation.

61. Merits Spearman’s Rank Correlation This method is simpler to understand and easier to apply compared to karl pearson’s correlation method. This method is useful where we can give the ranks and not the actual data. (qualitative term) This method is to use where the initial data in the form of ranks.

62. Limitation Spearman’s Correlation Cannot be used for finding out correlation in a grouped frequency distribution. This method should be applied where N exceeds 30.

63. Where and Why Correlation are used 1. Prediction If two variables are known to be related in a systematic way, then it is possible to use one variables to make accurate prediction about to other. For Example Carrots cause good eyesight .But sometime the prediction is not perfectly accurate . For Example College admissions officers can make a prediction about the potential success of each applicant.

64. CONTINUE,,,,,,,,,,,, 2.VALIDITY. Suppose that a psychologist develops a new test for measuring intelligence. One common techniques for demonstrating Validity is to use a correlation. If the test actually measures of intelligence then the score on the test should be related t o other measures of intelligence . FOR EXAMPLE standardized IQ Test, performance on learning tasks and so on…

65. Continue,,,, 3. Reliability . In addition to evaluating the validity of a measurement procedure, correlations are used to determine reliability. FOR EXAMPLE If your IQ was measured as 113 last week ,you would expect to obtain nearly the same score if your IQ was measured again this week.

66. Continue,,,, 4.THEORY VERIFICATION. Many psychological theories make a specific predictions about the relationship between two variables. FOR EXAMPLE. a developmental theory predict a relationship between the parent’s IQS and the child’s IQS, a social psychologist may have a theory predicting a relationship between personality type and behaviours .

67. Advantages of Correlation studies Show the amount (strength) of relationship present Can be used to make predictions about the variables under study. Can be used in many places, including natural settings, libraries, etc. Easier to collect co relational data

68. DISADVANTAGES Can’t assume that a cause-effect relationship exists Little or no control (experimental manipulation) of the variables is possible Relationships may be accidental or due to a third, unmeasured factor common to the 2 variables that are measured

69. SINGLE SUBJECT RESEARCH

70. What is it? Single-subject research usually involves collecting data on one subject at a time. Single-subject researchers generally use line graphs to illustrate the effect of their intervention.

71. “ Single subject research (also known as single case experiments) is popular in the fields of special education and counseling . This research design is useful when the researcher is attempting to change the behavior of an individual or a small group of individuals and wishes to document that change.

72. Unlike true experiments where the researcher randomly assigns participants to a control and treatment group, in single subject research the participant serves as both the control and treatment group. The researcher uses line graphs to show the effects of a particular intervention or treatment. An important factor of single subject research is that only one variable is changed at a time. Single subject research designs are “weak when it comes to external validity….Studies involving single-subject designs that show a particular treatment to be effective in changing behavior must rely on replication–across individuals rather than groups–if such results are be found worthy of generalization” (Fraenkel & Wallen , 2006, p. 318).

73. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior Frequency of disruptions 8 7 6 5 4 3 2 1 Baseline Praise

74. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days . Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

75. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred . Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline

76. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

77. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

78. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

79. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

80. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

81. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day for several days. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

82. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day for several days. In the example below, the target student was disruptive seven times on the first day Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

83. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day for several days. In the example below, the target student was disruptive seven times on the first day, six times on the second day Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

84. Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day for several days. In the example below, the target student was disruptive seven times on the first day, six times on the second day, and seven times on the third day. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

85. Once a baseline of behavior has been established (when a consistent pattern emerges with at least three data points), the intervention begins. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

86. Once a baseline of behavior has been established (when a consistent pattern emerges with at least three data points), the intervention begins. The researcher continues to plot the frequency of behavior Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

87. Once a baseline of behavior has been established (when a consistent pattern emerges with at least three data points), the intervention begins. The researcher continues to plot the frequency of behavior while implementing the intervention of praise. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

88. In this example, we can see that the frequency of disruptions decreased once praise began. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

89. In this example, we can see that the frequency of disruptions decreased once praise began. The design in this example is known as an A-B design. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

90. In this example, we can see that the frequency of disruptions decreased once praise began. The design in this example is known as an A-B design. The baseline period is referred to as A Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

91. In this example, we can see that the frequency of disruptions decreased once praise began. The design in this example is known as an A-B design. The baseline period is referred to as A and the intervention period is identified as B. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise

92. In this example, we can see that the frequency of disruptions decreased once praise began. The design in this example is known as an A-B design. The baseline period is referred to as A and the intervention period is identified as B. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise A B

93. Another design is the A-B-A design. An A-B-A design (also known as a reversal design) involves discontinuing the intervention and returning to a baseline. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise Baseline

94. Another design is the A-B-A design. An A-B-A design (also known as a reversal design) involves discontinuing the intervention and returning to a baseline. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise Baseline A B A

95. Sometimes an individual’s behavior is so severe that the researcher cannot wait to establish a baseline and must begin with an intervention. In this case, a B-A-B design is used. The intervention Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Praise

96. Sometimes an individual’s behavior is so severe that the researcher cannot wait to establish a baseline and must begin with an intervention. In this case, a B-A-B design is used. The intervention is followed by a baseline Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Praise Baseline

97. Sometimes an individual’s behavior is so severe that the researcher cannot wait to establish a baseline and must begin with an intervention. In this case, a B-A-B design is used. The intervention is followed by a baseline followed by the intervention. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Praise Baseline Praise

98. Sometimes an individual’s behavior is so severe that the researcher cannot wait to establish a baseline and must begin with an intervention. In this case, a B-A-B design is used. The intervention is followed by a baseline followed by the intervention. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Praise Baseline Praise B A B

99. Frequency of disruptions Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8 7 6 5 4 3 2 1 Baseline Praise Ordinate Condition change line Data points Data path Abscissa Measure of time Unit of time Dependent measure Condition identifications Independent variable Regardless of the research design, the line graphs used to illustrate the data contain a set of common elements.

100. Causal Comparative Research In causal comparative research the researcher attempts to determine the cause or reason, for existing differences in the behaviour or status of groups or individuals.

101. pg2 Definition and purpose: Like co relational research causal comparative research is sometimes treated as a type of descriptive research because it too describe conditions that already exist. Causal comparative is thus a unique type of research with its own research procedures. Also known as “ex post facto” research. (Latin for “after the fact”).

102. pg3 Although causal comparative research produces limited cause –effect information. It is an important form of educational research. True cause effect relation can be determined only through experimental research.

103. pg4 Example: the participation in pre school education is the major factor contributing to differences in social adjustment of first graders. To examine the hypothesis, the researcher would select the example of first graders who had participated in the pre school education and the sample of first graders who had not, then compare the social adjustment of two groups.

104. pg5 If the children who participated in the pre school education exhibited the higher level of social adjustment . Thus the basic causal comparative approach involves starting with an effect ( i.e social adjustment) and seeking possible causes ( i.e did preschool effect it)

105. pg6 Design and procedure: The basic causal comparative involves selecting two groups differing on some variables of interest and comparing them on some dependent variable. One group may possess a characteristic different than the other. Samples must be representatives of their respective population and similar with respect to critical variable other then the grouping variables.

106. pg7 Control procedure: Lack of randomization, manipulation and control all are sources of weakness in a causal comparative design.

107. pg8 Data analysis and interpretation: The inferential statistics most commonly used in causal comparatives studies are the t test which is used to determine whether the scores of two groups are significantly different from one another, analysis of variance, used to test for significant differences among the scores for three or more groups. Interpreting the findings requires considerable caution.

108. The Three Types There are 3 types of causal-comparative research: Exploration of Effects Exploration of Causes Exploration of Consequences

109. Similarities to correlational research Both types of research are examples of associational research: Researchers seek to explore relationships among variables. Both attempt to explain phenomena of interest. Both seek to identify variables that are worthy of later exploration Often provide guidance for later experimental studies.

110. Differences Causal-Comparative Typically compare 2 or more groups of subjects Involves at least 1 categorical variable. Analyzes data by comparing averages or uses crossbreak tables. Correlational Requires a score on each variable for each subject. Investigate 2 or more quantitative variables. Analyzes data by using scatterplots and/or correlation coefficients.

111. Differences Causal-comparative No manipulation of the variables. Provide weaker evidence for causation. The groups are already formed, the researcher must find them. Experimental The independent variable is manipulated. Provide stronger evidence for causation. The researcher can sometimes assign subjects to treatment groups.

112. The steps… Problem Formulation Select the sample of individuals to be studied. Instrumentation- achievement tests, questionnaires, interviews, observational devices, attitudinal measures…there are no limits…

113. The design The basic design is to select a group that has the independent variable and select another group of subjects that does not have the independent variable. The 2 groups are then compared on the dependent variable.

114. Internal Validity Usually 2 weaknesses in the research: Lack of randomization Inability to manipulate an independent variable Threats Oftentimes subject bias occurs Location Instrumentation Loss of subjects

115. Data Analysis Construct frequency polygons. Means and standard deviations (only if variables are quantitative) T-test for differences between means. Analysis of covariance

116. Proceed with caution!!! The researcher must remember that demonstrating a relationship between 2 variables (even a very strong relationship) does not “prove” that one variable actually causes the other to change in a causal-comparative study.

117. Limitations of Use There must be a “pre-existing” independent variable Years of study, gender, age, etc. There must be active variables- variables which the research can manipulate The length and number of study sessions, instructional techniques, etc.

118. Examples Exploration of causes of group membership. Question: What causes individuals to join a gang? Hypothesis: Individuals who are members of gangs have more aggressive personalities than individuals who are not members of gangs.

119. References Fraenkel, J. R., & Wallen , N. E. (2006). How to design and evaluate research in education (6th ed.). Boston, MA: McGraw Hill . Geisler, J. L., Hessler , T., Gardner, R., III, & Lovelace, T. S. (2009). Differentiated writing interventions for high-achieving urban African American elementary students. Journal of Advanced Academics, 20, 214–247. McKinney , S. (2004). A comparison of urban teacher characteristics for student interns placed in different urban school settings. The professional educator, 26(2 ). Wasson, B. (2001). Classroom behavior of good and poor readers. The professional educator, 23(3). www.mnstate.edu/wasson/ed603/ed603lesson12.htm www.faculty-staff.ou.edu/B/Nancy.H.Barry-1/cause.html