The first variable, Students sex describes the gender of the student participant. Based on gender, the participants were categorized as either male (1) or female (2), a nominal level of measurement. When X1 sex- T1 is examined, it can be seen that students sex classification is based on gender and on how survey participants identified and labeled themselves. According to Frankfort-Nachmias and Leon-Guerrero (2015), the nominal level of measurement involves the assignment of numbers to items as a representation of categories or classes of such items (p. 11).
The second variable of the study, Students Mathematical Identity, was assessed using a scale of student's mathematics identity (X1MTHID), an ordinal level measurement instrument. Using this scale, each participant picked a number which best described their mathematical aptitude on a scale of 1-5, where 1 is the lowest and 5 is the highest. Students Mathematics identity is 5-point Likert response scale from Strongly Agree (5) Agree (4) - Neither Agree or Disagree (3) Disagree (2) Strongly Disagree (1). According to Wagner (2017), a graphic representation of the distribution of a single scale variable can be best displayed by using a histogram as it can be used to examine vvariables in both the interval and the ratio levels of measurement (p. 65).
Statistical procedures and tools can be utilized in examining variables, in establishing whether social differences exist or not, and also in determining relationships between variables. By observing how male and female participants identify their math skills, the differences between the two sexes can be established. Moreover, knowledge of these differences can be used to carry out further research to test the assumption that females believe that their male counterparts have higher mathematics learning ability or boys think that they are better at math than girls.
Some females may accept that they are less skilled than males in mathematics, and start to feel intimidated when competing with male students in this subject area. Can researchers conduct further research to examine whether an individuals sex, race or culture influence how they feel about mathematics? If possible, new programs or interventions can be developed to bridge the gap in math academic performance and eliminate gender-math stereotypes and to help females attain higher mathematics identify and overcome math anxiety.
According to Frankfort-Nachmias and Leon-Guerrero (2015, p. 22), social scientists explain and analyze the link and attributions between relationships and real life when making an observation on variables, when testing theories, when formulating a hypothesis, and when trying to explain causes and effects. Even though this statistics course appears challenging, I have appreciated that positive social changes can be achieved through research, observation, investigation and being conscious of social differences, that require thorough knowledge of statistical tools to understand, explain, and interpret research results. Deitz and Kalof (2009) states that statistical techniques are useful in clarifying that glass, minimizing cloudiness, and in helping researchers separate truth from error (p. 11).
Dietz, T., & Kalof, L. (2009). Introduction to social statistics: the logic of statistical reasoning. Malden, MA: Wiley-Blackwell.
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society. Los Angeles: SAGE.
Wagner, W. E. (2017). Using IBM SPSS statistics for research methods and social science statistics. Los Angeles: SAGE.
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