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- 1. Basic Classroom Research Dr. Carlo Magno, PhD De La Salle University, Manila Lasallian Institute of Development and Educational Research
- 2. Objectives Consider the basic research paradigm in conceptualizing classroom research. Conceptualize a classroom research anchored on a conceptual or theoretical framework Plan a research following an appropriate deign
- 3. Research Process Problem Identification and Hypothesis Formulation Data Analysis, Interpretation and Drawing Conclusions Design Formulation Coding and data Processing Data Collection
- 4. Phases of a Research Study Idea-generating phase: Identify a topic of interest to study. Problem-definition phase: Refine the vague and general idea that was generated in the previous step. Procedures-design phase: Decide on the specific procedures to be used in the gathering and statistical analysis of the data.
- 5. Phases of a Research Study Observation phase: Using the procedures devised in the previous step, collect your observations from the participants in your study. Data-analysis phase: Analyze the data collected above using appropriate statistical procedures. Interpretation phase: Compare your results with the results predicted on the basis of your theory. Do your results support the theory? Communication phase: Prepare a written or oral report of the study for publication or other presentation to colleagues. The report should include a detailed description of all of the above steps.
- 6. Focus Research Designs that will test specific classroom phenomena Correlational Studies Group Comparison studies Effectiveness of an intervention on a set of measure Limited to quantitative approach in doing research Variables are measured Instruments are limited to obtaining quantitative data Surveys Questionnaires Tests Checklists Structured observations (scores are obtained)
- 7. Correlational Studies Involves two variables where one increases with the other Examples: Grades and motivation: Does student motivation increase with students’ grades? Attitude in Math and Math performance: Does students’ attitude in math increase with their performance in math achievement test? Math anxiety and test in math: Does anxiety decrease math test scores? The choice between the variables should be guided by a theory (theoretical or conceptual framework). Both variables should be quantitatively measured.
- 8. Correlational Studies Linear Regression There is a straight line relationship between variables X and Y When X increases, Y also increases-positive relationship When X increases, Y decreases or vice versa – negative relationship
- 9. Correlational Studies Problem: Is there a significant relationship between achievement and aptitude? Hypothesis: There is a significant relationship between achievement and aptitude
- 10. Relationship between achievement and aptitude Achievement (X) Aptitude (Y) 100 99 95 98 90 94 85 87 82 84 80 81 75 78 70 73 65 68 50 60
- 11. Regression Line between achievement and aptitude S ca tte rp lo t: X vs. Y Y = 1 4 .3 7 9 + .8 5 6 3 3 * X C o rre la tio n : r = .9 8 9 6 6 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 1 1 0 X 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0 9 5 1 0 0 1 0 5 Y 9 5 % co n fid e n ce
- 12. Laziness Perseverance 100 35 95 40 90 45 85 50 75 55 70 60 65 64 60 70 55 76 50 80 Relationship between laziness and perspeverance
- 13. Relationship between Laziness and Perseverance S ca tte rp lo t: Y vs. X X = 1 3 9 .9 4 - 1 .1 3 8 * Y C o rre la tio n : r = -.9 9 5 9 3 0 4 0 5 0 6 0 7 0 8 0 9 0 Y 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 1 1 0 X 9 5 % co n fid e n ce
- 14. Correlational Studies Analysis 2 variables that are interval or ratio: Pearson r 2 variables are ordinal: Spearman rho 2 variables and each is a dichotomy: phi coefficient High Satisfaction in teaching Low satisfaction in teaching High teaching performance 50 21 Low teaching performance 12 48 • A significant relationship occurs if scores are extreme enough to surpass the probability of error. •If p value is < obtained value: reject the null hypothesis •If the obtained value > critical value : reject the null hypothesis
- 15. Group Comparison Studies Involves group formed in categories (2 or more) and these categories are compared on an characteristic. The groups are called as the independent variable The characteristics of where the groups are compared on are called as the dependent variable. Examples: Is there a significant difference between males and females on their math performance? Is there a significant difference between public and private school students in their study habits? Are there a significant differences among the school ability of students from across three years (2010, 2011, 2012)? Are there significant differences among teachers, administrators, and staff on their attitude towards the RH
- 16. Group Comparison Studies Take note that the IV... is categorical can have two or more levels can also be more than one.... Example: Can gender and socio-economic status differentiate students general intelligence? A theoretical or conceptual framework is needed to justify the comparison.
- 17. Group Comparison Studies Case: Third year high school males and females are tested in their Mathematical Ability Males Females 26 38 24 26 18 24 17 24 18 30 20 22 18
- 18. Group Comparison Studies Males: Mean = 20.14 SD=3.48 Females: Mean = 27.33 SD = 5.89
- 19. Mean of Males and females in Math B ox & W hisker P lo t: V a r2 M e a n ± S D ± 1 .9 6*S D M a le s F em ale s V ar1 1 2 1 4 1 6 1 8 2 0 2 2 2 4 2 6 2 8 3 0 3 2 3 4 3 6 3 8 4 0 Var2
- 20. Group Comparison Studies H0= There is no significant difference between males and females in their math scores H1= There is a significant difference between males and females in their math scores 2. =.05 df = N1 + N2 –2 df = 7 + 6 –2 df = 11 t critical value = 2.201
- 21. Group Comparison Studies 3. Computation t = X1 - X2 x1 2 + x2 2 1 + 1 N1 + N2 – 2 N1 N2 t = - 2.73
- 22. Group Comparison Studies 4. Decision and Interpretation Since the t obtained which is – 2.73 is greater than the t-critical which is 2.201, the null hypothesis is rejected. This means that there is a significant difference between males and females in their math scores. Females (M=27.33) significantly scored higher in math as compared to the males (M=20.14)
- 23. Group Comparison Studies 4. Decision and Interpretation (another way using p values) Since the p value obtained which is 0.0195 is less than the alpha level which is .05, the null hypothesis is rejected. This means that there is a significant difference between males and females in their math scores. Females (M=27.33) significantly scored higher in math as compared to the males (M=20.14)
- 24. Factorial Design Independent Variable B A1 A2 A3 B1 A1 B1 A2 B1 A3 B1 B1 Mean Main Effect for BB2 A1 B2 A2 B2 A3 B2 B2 Mean A1 Mean A2 Mean A3 mean Main Effect for A Main effect of A Main Effect of B Interaction effect of A and B (A X B)
- 25. Talent Achievement Effect of Achievement and Type of school on Talent Low Achievers High Achievers Type of school Public school Private School
- 26. Ho: Achievement does not have a significant main effect on talent (there is no significant difference between high and low achievers on talent) Type of school does not have a significant main effect on talent (there is no significant difference between public and private school students in their talent) There is no significant interaction effect between achievement and type of school (there are no significant differences among high achievers in public, high achievers in private, low achievers in public, and low achievers in private in their talent Effect of Achievement and Type of school on Talent
- 27. H1: Achievement have a significant main effect on talent (there is a significant difference between high and low achievers on talent) Type of school have a significant main effect on talent (there is a significant difference between public and private school students in their talent) There is a significant interaction effect between achievement and type of school (there are significant differences among high achievers in public, high achievers in private, low achievers in public, and low achievers in private in their talent Effect of Achievement and Type of school on Talent
- 28. Group Comparison Studies Analysis If two categories are compared on one DV: t-test for two independent samples If three or more categories (one IV) are compared on one DV: One way Analysis of Variance (ANOVA) If two IV are investigated on one DV: two way ANOVA If two or more IV are investigated on two or more DV: Multivariate Analysis of Variance (MANOVA)
- 29. Effectiveness of an intervention on a set of measure (Experimental Study) The effect of a treatment is tested on a specific change on a characteristic. The treatment that is given to participants are called as the independent variable. The independent variable should be manipulated. Ex. Groups are randomly assigned to listening and watching stimulus. Ex. Groups are randomly assigned to reading a text or watching a news. The characteristic that changes dues to the variation or manipulation of the IV is called as the dependent variable.
- 30. Experimental Study How is the IV manipulated? Presence of absence Amount Type
- 31. Presence vs. absence The effect of adrenocorticotropin (ACTH) on the attention enhancement of schizophrenic patients. 1st group: received the ACTH drug 2nd group: received a placebo drug
- 32. Amount manipulation The effect of ACTH drug on the excessive grooming of rats. 1st group: 0 nanograms of ACTH 2nd group: 20 nanograms of ACTH 3rd group: 50 nanograms of ACTH 4th group: 80 nanograms of ACTH 5th group: 1,000 nanograms of ACTH
- 33. Type manipulation The effect of labeling on the teachers conduct assessment of students Results Trouble makers low conduct Average Average conduct Ideal students High conduct
- 34. Experimental Study In an experiment done by dela Cruz, Cagandahan and Arciaga (2004), the effect of nonbehavioral intervention techniques was investigated on the computational abilities of fourth year high school students. The non-behavioral intervention techniques has three levels, bibliotherapy, small group interaction and games. These techniques were used as a teaching strategy in a lesson in a math class for three sections. Each of the strategy was used for each section. One section did not receive any strategy which served as the control group. After undergoing the strategy, the students were tested where they answered a series of computation items.
- 35. Experimental Study Bibliotherapy Small group interaction Games Control Group X1 X2 X3 X4 X1 X2 X3 X4 X1 X2 X3 X4 X1 X2 X3 X4
- 36. Experimental Study 1. H0: The non-behavioral intervention techniques have no significant effect on computational ability H0: There are no significant differences among the groups receiving bibliotherapy, small group interaction, games and control in their computational ability. 2. 2=.05 df between = groups – 1 = (4-1=3) df within = (N – 1) – df between ((209-1)-3)=205 df total = df between + df within (3 + 205) F ratio critical value = 2.65
- 37. ANOVA Hypothesis Testing 3. Computation F ratio computed = 4.62 4. Decision and Interpretation Since the F ratio obtained which is 4.62 is greater than the F ratio critical which is 2.65, the null hypothesis is rejected. The non-behavioral intervention techniques have a significant effect on computational ability.
- 38. ANOVA Hypothesis Testing In te rve n tio n te ch n iq u e s; L S M e a n s C u rre n t e ffe ct: F (3 , 2 0 5 )= 4 .6 8 1 9 , p = .0 0 3 4 7 E ffe ctive h yp o th e sis d e co m p o sitio n V e rtica l b a rs d e n o te 0 .9 5 co n fid e n ce in te rva ls co n tro l G a m e s B ib lio th e ra p y S m a ll g ro u p in te ra ctio n In te rve n tio n te ch n iq u e s 3 .5 4 .0 4 .5 5 .0 5 .5 6 .0 6 .5 7 .0 7 .5 8 .0 8 .5 computation The group who received the small group interaction significantly scored the highest among other intervention techniques.
- 39. Experimental Designs Research Design – Refers to the outline, plan or strategy specifying the procedure to be used in seeking an answer to the research question True Research Designs - Answers the research questions or adequately tests hypothesis. Extraneous variables are controlled Inclusion of a control group External validity - Generalizability
- 40. Experimental Designs 1. After-Only Design Dependent variable is measured only once and this measurement occurs after the experimental conditions have been administered to the experimental group. Treatment Response Measure Experimental Condition X Y Control Condition Y Between Subjects Design – If different subjects are used in each experimental treatment condition. Within Subjects Design – If the same subjects are used in each experimental condition.
- 41. Experimental Designs 1.1 Between-Subjects After Only Design subjects are randomly assigned to the experimental and control group.
- 42. Simple Randomized Subjects Design Includes more than one level of the independent variable
- 43. Experimental Designs Factorial Design Two or more independent variables are simultaneously studied to determine their independent and interactive effects on the dependent variables. Main effect – influence of one independent variable Interaction effect – Influence that one independent has on another
- 44. Experimental Designs Within Subject After-Only Design Same subjects are repeatedly assessed on the dependent variable after participating in all experimental treatment conditions
- 45. Experimental Designs Combined Between- and Within-Subjects Designs Factorial Design Based on a mixed Model Two independent variables have to be varied in two different ways. One independent variable requires a different group of subjects for each level of variation. The other independent variable is constructed in such a way that all subjects have to take each level of variation.
- 46. Experimental Designs
- 47. Experimental Designs 2. Before-After Design The treatment effect is assessed by comparing the difference between the experimental and control groups’ pre- and posttest scores.
- 48. The Solomon Four-Group Design - Designed to deal with a potential testing threat. - Testing threat occurs when the act of taking a test affects how people score on a retest or posttest. - The design has four groups - Two of the groups receive the treatment and two does not. - Two of the groups receive a pretest and two does not. - By explicitly including testing as a factor in the design, we are able to assess experimentally whether a testing threat is operating.
- 49. Experimental Designs Switching Replications Design - There is a need to deny the program to some participants through random assignment. - A two group design with three waves of measurement. - The implementation of the treatment is repeated or replicated. - In the repetition of the treatment, the two groups switch roles: - The original control group becomes the treatment group in phase 2 while the original treatment acts as the control. By the end of the study all participants have received the treatment.
- 50. Experimental Designs Randomized Block Design - Constructed to reduce noise or variance in the data - Requires that the researcher to divide the sample into relatively homogeneous subgroups or blocks. - Then, the experimental design desired is implemented within each block or homogeneous subgroup. - The key idea is that the variability within each block is less than the variability of the entire sample. Thus each estimate of the treatment effect within a block is more efficient than estimates across the entire sample
- 51. Experimental Designs
- 52. Recap What are the three approaches in conducting a study?
- 53. Activity Construct a plan for your classroom research Research Question Hypothesis What conceptual/theoretical framework will be used? (be ready to explain) Why is this research question relevant Method Design Participants (who and how many) Instruments used (how will you measure the DV?) Procedure

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