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Friday, November 14, 2014



QUANTITATIVE RESEARCH Presentation Transcript

  •   Quantitative research is “an inquiry into a social or human problem based on testing a theory composed of variables, measured with numbers, and analyzed with statistical procedures, in order to determine whether the predictive generalizations of the theory hold true.”
  • Quantitative Research involves “the systematic collection of numeric information, usually under conditions of considerable control and the analysis of that information using statistical procedures”
  • Quantitative research is about quantifying the relationships between variables.
  • The researcher knows in advance what he or she is looking for.
  • Goal: Prediction, control, confirmation, test hypotheses.
  • All aspects of the study are carefully designed before data are collected.
  • Quantitative research is inclined to be deductive -- it tests theory. This is in contrast to most qualitative research which tends to be inductive --- it generates theory
  • The researcher tends to remain objectively separated from the subject matter.
  • Focuses on a relatively small number of specific concepts
  • Begins with preconceived ideas about how the concepts are inter related
  • Uses structured procedures and formal instruments to collect information
  • Collects information under conditions of control
  • Emphasizes objectivity in the collection and analysis of information
  • Analyses numeric information through statistical procedures
  • t is a formal objective, systematic process in which numerical data are utilized to get information about the phenomena
  • Describe variables, examine relationships among variables and determine cause & effect relationships between variables
  • It produces “hard” science that is based on rigor, objective and control. Quantitative researchers believe all human behavior is objective, measurable and purposeful. Only right instrument needed to measure variables
  • Focus is concise & reductionist. Reductionism involves breaking the whole into parts that can be examined
  • It is done to describe, examine relationships and determine causality among variables (cause-effect). Useful in testing theory
  • It involves logistic, deductive, reasoning to make generalizations about the universe. 
  • Requires control (Extraneous variable)
  • Requires use of instruments to collect data that will generate numerical data.

1. Non-experimental research design
  • Descriptive research
  • Cor relational research
  •  Evaluative
  • Meta Analysis
  • Causal-comparative research
2. Experimental research design:
  • True Experimental,
  • Quasi Experimental

 A. Descriptive design:
  •  Descriptive research involves collecting data in order to test hypotheses or answer questions regarding the participants of the study. Data, which are typically numeric, are collected through surveys, interviews, or through observation.
  • In descriptive research, the investigator reports the numerical results for one or more variable(s) on the participants (or unit of analysis) of the study.
B. Cor relational design:
  • Cor relational research attempts to determine whether and to what degree, a relationship exists between two or more quantifiable (numerical) variables.
  •  It is important to remember that if there is a significant relationship between two variables it does not follow that one variable causes the other.  CORRELATION DOES NOT MEAN CAUSATION.
  • When two variables are correlated you can use the relationship to predict the value on one variable for a participant if you know that participant’s value on the other variable.
  • Correlation implies prediction but not causation. The investigator frequently reports the correlation coefficient, and the p-value to determine strength of the relationship.
C. Causal comparative design:
  • Causal-comparative research attempts to establish cause-effect relationships among the variables of the study.
  • The attempt is to establish that values of the independent variable have a significant effect on the dependent variable.
  • This type of research usually involves group comparisons. The groups in the study make up the values of the independent variable, for example gender (male versus female), preschool attendance versus no preschool attendance, or children with a working mother versus children without a working mother.
  • In causal-comparative research the independent variable is not under the researchers control, that is, the researcher can't randomly assign the participants to a gender classification (male or female) or socioeconomic class, but has to take the values of the independent variable as they come. The dependent variable in a study is the outcome variable.
D. Meta Analysis:
  • Meta-analysis is essentially a synthesis of available studies about a topic to arrive at a single summary.
  • From data that is after the fact that has occurred naturally (no interference from the researcher), a hypothesis of possible future correlation is drawn. Correlation studies are not cause and effect, they simply prove a correlation or not (Simon & Francis, 2001).
  • Meta-analysis combines the results of several studies that address a set of related research hypotheses
  • Begins with a systematic process of identifying similar studies.
  • After identifying the studies, define the ones you want to keep for the meta-analysis. This will help another researcher faced with the same body of literature applying the same criteria to find and work with the same studies.
  • Then structured formats are used to key in information taken from the selected studies.
  • Finally, combine the data to arrive at a summary estimate of the effect, it’s 95% confidence interval, and a test of homogeneity of the studies.
  • Experimental research like causal-comparative research attempts to establish cause-effect relationship among the groups of participants that make up the independent variable of the study, but in the case of experimental research, the cause (the independent variable) is under the control of the researcher.
  • The researcher randomly assigns participants to the groups or conditions that constitute the independent variable of the study and then measures the effect this group membership has on another variable, i.e. the dependent variable of the study.
  • There is a control and experimental group, some type of “treatment” and participants are randomly assigned to both: Control Group, manipulation, randomization).
A. True Experimental :
Characteristics of true experiments:
  • Manipulation: experimenter does something to at least some subjects.
  • Control: experimenter introduces controls over the experimental situation including use of a control group.
  • Randomization: experimenter assigns subjects to a control or experimental group on a random basis.
B. Quasi & pre experimental designs Quasi experiments
  • Like true experiments, involve the manipulation of an IV, i.e. and intervention.
  • However these designs lack randomization to treatment groups
  • Facilitate search for knowledge and examination of causality in situations in which complete control is not possible
  • Developed to control as many threats to validity as possible.
  • Pre test post test experimental design
  • Experimental
  • Quasi experimental
  • Pre-experimental
  • Non experimental
  • Basic experimental designs :
  • Pre test-post test design (before-after design)
  • Post test only design (after-only design)
  • Solomon four-group design
  • Factorial design
  • Randomized block design
  • Crossover design (repeated measure design)
  • Powerful method for testing hypotheses of cause and effect relationships
  • Highest quality evidence regarding effects of specific interventions
  • “If….then” relationship important because of implications for prediction and control.
  • Strength lies in the confidence with which causal relationship in inferred.
  • Through the controls imposed by manipulation, comparison, and randomization, alternative explanations to a causal interpretation can often be ruled out or discredited.
  • Artificiality – Reductionist and artificially constraining human experience.
  • Requirements for randomization and then equal treatment within groups
  • Does not answer ‘Why’ the intervention resulted in the observed outcome (if without a guiding theoretical framework).
  • Difficult to maintain the integrity of the intervention and control conditions if study extends over time.
  • Experimenter does not have control over the clinical environment
  • Treatment may be diluted through non participation
  • Hawthorne effect – Double blind experiments.
  • Evidence-based nursing practice comes from the idea that the care we provide be determined by sound research rather than by clinician preference or tradition. Understanding how to select the best design to answer a research question or test a hypothesis is the first step in conducting meaningful research.
  •  This process assists nurses as they read and critique original research articles. Nursing practice is seldom changed based on one study. It is the accumulation of results from several studies, often using different research designs that provide enough evidence for change.

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