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Quantitative Methods Topic 9 Bivariate Relationships.

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Presentation on theme: "Quantitative Methods Topic 9 Bivariate Relationships."— Presentation transcript:

1 Quantitative Methods Topic 9 Bivariate Relationships

2 2 Outline Crosstab (Exploring the relationship between two categorical variable). Correlation (Exploring the relationship between two continuous variables, typically)

3 3 Data file YR12SURV2.SAV YR12SURVEYCODING2.DOC (Questionnaire) Holland2fory12data.doc

4 4 RANKMS and RANKINV RANKMS is a constructed variable ranking informants on the amount of time spent on Maths and Science A high rank (e.g. 3) means the informant spent a lot of time on Maths and Science A low rank (e.g. 1) means the informant spent very little or no time on Maths and Science. RANKINV is a similar rank type variable focused on investigative interests: e.g. informant interested in laboratory work. A high rank (e.g. 4) means the informant was very interested A low rank (.e.g. 1) means the informant was least interested. These are ordinal variables.

5 5 Relationships between two categorical variables Example of research questions Is there a relationship between gender and student investigative interest? Are males more likely to be interested in investigative activities than females? Is the proportion of males in each of the investigative level the same as the proportion of females?

6 6 Variables To answer the research question in the example above we will have to do crosstabulations between two variables Gender RANKINV

7 7 Hypothesis of independence There is no association between the two variables gender and RANKINV There is no difference in the proportion of females and males in each of the categories (levels) of investigative interest

8 8 How to do crosstabulations in SPSS From the DATA menu select ANALYSE then Descriptive Statistics then Crosstabs Move GENDER into the Column(s) window and RANKINV into the Row(s) window Open the Statistics window and tick Chi- square Continue to close Open the Cells window, under Counts tick observed, under Percentages, tick Column, then click Continue Click OK in the Crosstabs window to run

9 9 Screen

10 10 Output There are two tables in the output that are important for us: Table 1: Crosstab Table 2: Chi-square

11 11 Table 1: Crosstab of RankInv by Gender

12 12 Interpreting Association in the Table We can compare the column percentages along the rows and calculate the percentage point difference to see (in this case) whether females differ from males at each level of interest In the rankinvestgtv by Gender crosstabulation, for example, 30.7% of females were in category 1 (Very low investigative interest) compared with 19.6% of males, giving a percentage point difference of Similarly, there is a difference of 10.7 percentage points in the number of males having very high investigative interest compared with females

13 13 Table 2: Chi-square Statistics generated by Crosstabs Pearson Chi-square value, degree of freedom and significant level This helps us to check if expected counts less than 5

14 14 Tests of Statistical Significance for Tables Chi-square used to test the null hypothesis that there is no discrepancy between the observed and expected frequencies or there is no association between row and column variables Chi-square based statistics can be used independently of level of measurement. If chi-square is significant (say Asymp. Sig. <0.05) then we reject the null hypothesis and conclude that the data show some association compared with a (hypothetical) table in which the observed frequencies were determined solely by the separate distributions of the two crosstabulated variables (the marginal distributions) If chi-square is not significant (say Asymp. Sig. >0.05) then we accept the null hypothesis and conclude that the data show no association compared with a (hypothetical) table in which the observed frequencies were determined solely by the separate distributions of the two crosstabulated variables (the marginal distributions)

15 15 Assumptions Random samples Independent observations: the different groups are independent of each other The lowest expected frequency in any cell should be 5 or more

16 16 Chi-square Statistics- limitations Chi-Square measures are sensitive to sample size

17 17 Interpretation of output from chi-square The note under the table shows that you have not violated one of the assumptions of chi-square concerning minimum expected cell frequency Pearson chi-square value: at 18.5 for 3 degrees of freedom Chi-square is highly significant probability of this level of association occurring by chance is less than Degree of freedom=(r-1)(c-1) where r and c are number of categories in each of the two variables. Conclusion: males are more likely than females to be interested in investigative activities.

18 18 Class activity 1: Produce a similar table using GENDER by RANKMS

19 19 Summary of analyses of association RANKINV and RANKMS are 4 and 3 categories (respectively) ordinal variables constructed, respectively, from the total score on Investigative interests and the proportion of curriculum time spent in Maths/Science Gender heads the columns, interest and curriculum participation in Maths/Science form the rows (thus, by convention, gender is the explanatory or independent variable, interest or curriculum participation the response or dependent variables)

20 20 Correlations Strengths of relationships between two variables.

21 21 Correlation Examples of Research questions Is there a relationship between student achievement in mathematics and English language ? Is there a relationship between parents incomes and children VCE results ? Is there a correlation between SES and achievement ? How strong are these relationships?

22 22 Assumptions (1) Scores are obtained using a random sample from populations Independence of observations The distribution of the variable(s) involved is normal Homoscedasticity: the variance of the dependent variable is the same for values of X (residual variance, or conditional variance) Linearity: The relationship between the two variables should be linear. Related pairs: both pieces of information must be from the same subjects

23 23 Data Set Vietnam Data Set vnsample2.sav

24 24 Scatter plot

25 25 Producing a Scatterplot GRAPHS-SCATTER-SIMPLE-DEFINE Select MEASUREMENT score (pmes500) to make this the Y variable Select NUMBER score (pnum500) to make this the X variable. Click OK The scatterplot should appear in the OUTPUT window.

26 26 Scatter plot

27 27 Interpretation of Scatter plot Step 1: Checking for outliers Step 2: INSPECTING THE DISTRIBUTION OF DATA POINT: Are the data points scattered all over. Are the data points neatly arranged in a narrow cigar shape Could we draw a straight line through the main cluster of points or would a curved line better represents the points Is the shape of the cluster even from one end to other. (if it starts off narrow and then gets wider, The data may violate the assumption of homoscedasticity: at different value of X, variability of Y is different) Step 3: Determining the direction of the relationship between the two variables: positive or negative correlations

28 28 When values on two variables tend to go in the same direction, we call this a direct relationship. The correlation between childrens ages and heights is a direct relationship. That is, older children tend to be taller than younger children. This is a direct relationship because children with higher ages tend to have higher heights. Direct Relationship

29 29 When values on two variables tend to go in opposite directions, we call this an inverse relationship. The correlation between students number of absences and level of achievement is an inverse relationship. That is, students who are absent more often tend to have lower achievement. This is an inverse relationship because children with higher numbers of absences tend to have lower achievement scores. Inverse Relationship

30 30 Scatter plot

31 31 How to run correlation Highlight ANALYSE, CORRELATE, BIVARIATE Copy THE TWO VARIABLES INTO VARIABLES box Check that PEARSON box (two continuous variables- see the notes for other variable types) and the 2 tail box Click OK

32 32 OUTPUT AND INTERPRETATION Step 1: Checking information about sample size Step 2: Determining the directions and strengths of the relationships Step 3:Calculating the coefficient of determination (r 2 ) Step 4: Assessing the significance This is correlation coefficient (r) This is the p value Number of cases

33 33 Correlation Coefficient The relationship between two variables may be expressed with a number between and This number is called a correlation coefficient. The closer the correlation coefficient is to 0.00, the lower the relationship between the two variables. The closer the coefficient is to 1.00 or the higher the relationship. According to Cohen (1988) R=.10 to.29 or R=-.10 to -.29: Small R=.30 to 0.49 or R=-.30 to Medium R=.50 to 1.00 or R=-.50 to -1 Large

34 34 Some caveats about correlation and scatter plots - 1 Make a scatter plot of Measurement score against Number score again. This time, double click on the plot to get into Chart Editor. Change both X and Y axis scales to have a minimum of 200 and a maximum of 750. Does the strength of the relationship look weaker in this graph as compared to the one where the min is 0 and max is 1000?

35 35 Some caveats about correlation and scatter plots - 2 Be aware that judging the strength of relationship based on visual perception of scatter plot could be flawed, as the scale of the plots can make a difference.

36 36 Some caveats about correlation and scatter plots - 3 Create a new variable pmes10 using Transform compute new variable such that pmes10=pmes500/ That is, we have transformed the measurement score to have a mean of 10 and a standard deviation of 1. Compute the correlation between pmes10 and pnum500. How does this correlation compare with the correlation between pmes500 and pnum500?

37 37 Some caveats about correlation and scatter plots - 4 Compute the correlation between pmes500 and pnum500, but only for scores between 300 and 600. You can do this by selecting a sample Data Select cases If condition is satisfied: pnum500 > 300 and pnum and pmes500<600 How does the correlation compare with that from the full sample?

38 38 Some caveats about correlation and scatter plots - 5 This link shows TIMSS and PISA 2003 maths country mean scores for 22 countries. TIMSSandPISA 2003.docTIMSSandPISA 2003.doc Plot a scatter graph between TIMSS and PISA scores Compute the correlation Repeat without Tunisia and Indonesia.

39 39 Coefficient of determination is the squared correlation coefficient, and represents the proportion of common variation in the two variables

40 40 The proportion of variance explained is equal to the square of the correlation coefficient If the correlation between alternate forms of a standardized test is 0.80, then (0.80) 2 or 0.64 of the variance in scores on one form of the test is explained or associated with variance of scores on the other form That is, 64% of the variance one sees in scores on one form is associated with the variance of scores on the other form. Consequently, only 36% (100% – 64%) of the variance of scores on one form is unassociated with variance of scores on the other form. Proportion of variance explained

41 41 Presenting the results for Correlation Purpose of the test Variables involved r values, number of cases, p value R-square Interpretation

42 42 Class activity 1 What are the correlations between three dimensions of mathematics: number, measurement and space? Is it true that students who perform well in number strand also doing well in measurement and space strands? Datafile: VNsample2.sav Variables: student scores in measurement, number and space

43 43 Regression line

44 44 Producing a Scatterplot with regression line GRAPHS-SCATTER-SIMPLE-DEFINE Select MEASUREMENT 500 SCORE IN PUPIL MATH t o make this the Y variable Select NUMBER 500 SCORE IN PUPIL MATH and to make this the X variable. Click OK The scatterplot should appear in the OUTPUT window. Double click anywhere in the scatter plot to open SPSS CHART EDITOR, Click on ELEMENTS< FIT LINE AT TOTAL, make sure that LINEAR is selected.

45 45 Regression line

46 46 Regression line

47 47 Linear Regression Example of research questions To what extent can student achievement in Vietnamese language predict student achievement in mathematics? How well family income (or wealth) can predict student performance? How well university entrance scores can predict student success in University?

48 48 Regression equation y= a+bx+e y and x are the dependent and independent variables respectively a is the intercept (the point at which the line cuts the vertical axis. b is the slope of the line or the regression coefficient. e is error term.

49 49 How to RUN REGRESSION Highlight ANALYSE, REGRESSION, LINEAR Copy THE CONTINOUS DEPENDENT VARIABLE INTO DEPENDENT box Copy THE INDEPENDENT VARIABLE INTO INDEPENDENT box For METHOD, make sure that ENTER is selected. Click STATISTICS and tick on ESTIMATES, MODEL FIT AND DESCRIPTIVES, then CONTINUE Click on OPTIONS, then INCLUDE CONSTANT IN EQUATION, EXCLUDE CASES PAIRWISE, then CONTINUE Click OK

50 50 An example for regression Research question: To what extent that student scores in reading can predict student scores in Mathematics? Datafile: VNsample2.sav Variables: Reading 500 scores, Mathematics 500 scores achievement

51 51 OUTPUT AND INTERPRETATION Step 1: Checking Descriptive

52 52 OUTPUT AND INTERPRETATION (2) Step 2: Evaluating the model

53 53 Output and interpretation Step 3: Evaluating the effect of the independent variable The t-tests with significance levels for the constant (a) and the regression co- efficient (b)

54 54 Presenting the results for regression Purpose of the test Variables involved Number of cases Intercept Un-standardised b and standardised (beta) coefficients, SE, p value R-square Interpretation of the relationship

55 55 Class activity 2 To what extent school resources can predict for student achievement in maths and Vietnamese language Datafile: VNsample2.sav Variables: school resources index, math achievement


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