Scope and Methods

Scope of Methods

  1. Research hypothesis

Boyles’ Law states that pressure is inversely proportional to volume. Another factor for this statement is temperature. Temperature is held constant for the two factors to relate the statement states. Therefore, my statement goes: pressure is inversely proportional to volume as temperature is held constant. My research on the scope and Methods finds a control variable that is constant, that is, it does not change. The control variable regulates the flow of the control as well (Weeks, 1929). My research involves the testing of the relationship between the independent and dependent variables. Therefore, any additional variable is part of my control variables. My control variable is held constant until the end of the research since it is affecting the independent variable and also the dependent variable is affecting it as well. If the control variable by any case is changes, therefore all my dependent and independent variables will be useless. For instance, the three values independent, dependent, and the additional variable. To determine the relationship between two variables, one must keep constant. My control variable is the variable kept at constant. The relationship between the dependent and dependent variable is controlled by the additional variable (Dobson, 1981). The three variables are denoted by ‘ID’ for independent variable, ‘DP’ for the dependent variable and ‘CV’ for the control variable. The relationship of ID and DV is in the form of, ID*DP=CV, therefore, ID =CV/DP and DP=CV/ID.

  1. Methodology

The dataset used

The dataset used

Dependent variable (VOUME) Independent variable(PRESSURE) Control Variable (TEMPERATURE)
7 3 21
8 4 32
3 7 21
7 9 63
4 2 8
8 9 72
0 3 0
3 5 15
8 8 64
5 1 5
7 9 63
5 8 40
  1. b) The operational steps to test my hypothesis

My hypothesis mainly uses arithmetic analysis to come up with the relationship between the independent variable and the dependent variable. All the time the control variable is kept at constant so that the research process can have the adequate datum or reference. The relationship goes like this:

IV*DV=CV                                     where         IV – Independent Variable,

DV – Dependent Variable, and

CV – Control Variable

My research study was to test the relationship of Boyle’s law where the variables involved are pressure, temperature and volume. The relationship between the three variables according to scientific rules and the experimental truth is; P*V=T, where ‘P’ stands for Pressure, ‘T’ stands for Temperature and ‘V’ stands for volume. The resemblance of this relationship to the previous one (IV*DV=CV) shows that, IV represents the independent variable, pressure; DV represents the dependent variable, volume and CV represents the control variable, temperature.

Strengths and weaknesses of the measures

The noted strengths of the measures are:

  • Allocation of the variables was done randomly thus no order was followed. This eased to save the time of the research study,
  • No order effects with the variables were exposed.
  • The variables used gives the validity of the study as they obey the scientific facts about the phenomenon.
  • The research took a very short time to do and deliver the results since the variable used obeyed natural laws (Griffith, 1996).

Some of the noted Weaknesses of the studies were:

  • For more valid results of the study, more variables were required to gather a more concrete and enough information about how the facts relate to nature.
  • There may still be significant individual variations in the variables that may not be filtered properly resulting in skewed results. The situation, therefore, reduces the external reliability and the validity of the results.

The research measures obey the rule of research analysis. Thus, they are valid (Cavalli & Budkowski, 1996).

  1. Findings
Strength significance Direction significance Statistical significance
4 7 4
5 5 6
6 3 8
7 2 9
3 8 4
5 7 6
7 8 8
8 9 9
1 8 2
4 7 6
6 6 4
8 4 4


Distribution of the Dependent Variable, Independent Variable and Control variable.

Table 1. Descriptive Statistics
N Minimum, Maximum Mean s.d.
Dependent Variable

(level of measurement)

12 0,8 5.6667 2.544
Independent Variable

(level of measurement)

12 1,9 5.6667 2.909
Control Variable

(level of measurement)

12 0,64 33.7500 19.778


Correlation matrix with all three variables (strength significance, direction significance, statistical Significance)

Table 3. Linear Regression on Dependent Variable
Model 1. Model 2. Multivariate
Independent Variable B




Control Variable b




Constant B






R-square 0.16 0.12 0.13
Adjusted R2 0.11 0.13 0.05
N 12 12 12
Unstandardized b coefficients reported (Standard Error)

*p<.05; **p<.01;***p<.001

The “R Square” statistic 0.16 can be interpreted to mean that;

“The twelve (12) independent variables in the regression model have a total variation of 0.16, that is, 16 percent in the given analysis.”

The higher the statistic of R-square the better the model fits the data, and this terms the model to be ‘modestly’ putting the data in order.

The ‘Adjusted R-square’ statistic in the analysis (0.11) is the modified R-square and takes into account the number of variable included in the model. The higher the number of the variables in the regression model, the better the R-square statistic. This means that even if irrelevant variables are added to the model, the R-square statistic will improve. The adjusted R-square statistic is slightly smaller than the R-square statistic it reduces (downward adjusts) the R-square when there is the introduction of additional variables of limited significance to the model. Its correct to say that, data is fitted better in a one regression model than in another model where the adjusted R-square statistic is high.


Cavalli, A., & Budkowski, S. (1996). Protocol Test Systems VIII: Proceedings of the IFIP WG6.1 TC6 Eighth International Workshop on Protocol Test Systems, September 1995. Boston, MA: Springer US.

Dobson, C. B. (1981). Scope and methods of psychology: Unnumbered. Longman.

Griffith, G. K. (1996). Statistical process control methods for long and short runs. Milwaukee, Wis: ASQC Quality Press.

Weeks, D. (1929). Scope and methods of research in land utilization. Berkeley?.

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