Classroom research

Basic Classroom Research

Dr. Carlo Magno, PhDDe La Salle University, Manila

Lasallian Institute of Development and Educational Research

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

Research Process

Problem Identification and Hypothesis Formulation

Data Analysis, Interpretation and Drawing Conclusions

Design Formulation

Coding and data Processing

Data Collection

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.

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.

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)

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.

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

Correlational Studies Problem: Is there a significant relationship

between achievement and aptitude? Hypothesis: There is a significant relationship

between achievement and aptitude

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

Regression Line between achievement and aptitude

Scatterplot: X vs. Y

Y = 14.379 + .85633 * XCorrelation: r = .98966

40 50 60 70 80 90 100 110

X

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Y

95% confidence

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

Relationship between Laziness and Perseverance

Scatterplot: Y vs. X

X = 139.94 - 1.138 * YCorrelation: r = -.9959

30 40 50 60 70 80 90

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95% confidence

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

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 bill?

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.

Group Comparison Studies

Case: Third year high school males and females are tested in their Mathematical Ability

Males Females26 3824 2618 2417 2418 3020 2218


Males: Mean = 20.14 SD=3.48 Females: Mean = 27.33 SD = 5.89

Mean of Males and females in Math

Box & Whisker Plot: Var2

Mean ±SD ±1.96*SD Males Females

Var1

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Va

r2


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


3. Computation

t = X1 - X2 x1

2 + x22 1 + 1

N1 + N2 – 2 N1 N2

t = - 2.73


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)


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)

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)

Talent

Achievement

Effect of Achievement and Type of school on Talent

Low Achievers

High Achievers

Type of school

Public school

Private School

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


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


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)

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.

Experimental Study How is the IV manipulated?

Presence of absence Amount Type

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

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

Type manipulation The effect of labeling on the teachers conduct

assessment of students

ResultsTrouble makers low conductAverage Average conductIdeal students High conduct

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.

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

Experimental Study

1. H0: The non-behavioral intervention techniques have no significant effect on computational abilityH0: 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

ANOVA Hypothesis Testing

3. ComputationF ratio computed = 4.62

4. Decision and InterpretationSince 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.

ANOVA Hypothesis TestingIntervention techniques; LS Means

Current effect: F(3, 205)=4.6819, p=.00347Effective hypothesis decomposition

Vertical bars denote 0.95 confidence intervals

controlGames

BibliotherapySmall group interaction

Intervention techniques

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The group who received the small group interaction significantly scored the highest among other intervention techniques.

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

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 YControl 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.

Experimental Designs 1.1 Between-Subjects After Only Design subjects are randomly assigned to the

experimental and control group.

Simple Randomized Subjects Design Includes more than one level of the

independent variable

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

Experimental Designs Within Subject After-Only Design Same subjects are repeatedly assessed on the

dependent variable after participating in all experimental treatment conditions

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.

Experimental Designs

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.

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.

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.

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

Experimental Designs

Recap What are the three approaches in conducting

a study?

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

Technology

Classroom research